Understanding and evaluating computer vision models through the lens of counterfactuals

Pushkar Shukla

REVIEW 4 major objections 6 minor 227 references

Understanding and evaluating computer vision models through the lens of counterfactuals

T0 review · 4 major / 6 minor · reviewed 2026-08-05 · deepseek-v4-flash

Challenge this review Re-run · record.json Download PDF Read on arXiv ↗

Pith's one-line read The thesis claims that counterfactual reasoning—changing one meaningful attribute while holding all else fixed—is a single lens for explaining, auditing, and mitigating bias in both image classifiers and text-to-image models, and it builds

desk verdict A competent compilation thesis whose dynamic bias-axis and intersectional tools are genuinely useful, but the VQA-backed quantitative backbone and the causal framing need more caution before the unified claims are fully secured. read the letter →

arxiv 2508.20881 v1 pith:H3XXXRHX submitted 2025-08-28 cs.CV

Pushkar Shukla This is my paper

classification cs.CV

keywords counterfactualreasoninginterpretabilitybiasevaluationmitigationtext-to-imagegenerationintersectionalityfairnesscomputervision

verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

Counterfactual reasoning—asking what a model would output if one meaningful attribute changed while everything else stayed fixed—is the thesis's organizing claim: it is not just an explanation technique but a shared mechanism for interpretability, fairness evaluation, causal diagnosis, and bias mitigation in computer vision. The thesis develops five instantiations: CAVLI quantifies how much a classifier's decision depends on a human-defined concept by masking superpixels; ASAC generates adversarial perturbations along protected attributes and fine-tunes classifiers to improve fairness without losing accuracy; TIBET dynamically surfaces prompt-relevant bias axes in text-to-image models and scores them with CAS and MAD; BiasConnect turns those scores into pairwise causal graphs of intersectional bias; InterMit uses those graphs to mitigate multiple biases under user-defined priorities. If these claims hold, bias work in vision—across classifiers and generators—can share one disciplined experimental language: intervene on an attribute, hold the rest fixed, and read the output shift. The thesis reports early evidence for that unification: on CelebA smile classification, equalized-odds difference drops from 0.088 to 0.050 while accuracy rises from 84.29 to 91.91; bias metrics track human rankings in user studies; and the mitigation algorithm removes intersectional bias in fewer steps than a fixed-prompt baseline.

What carries the argument

The central machinery is the concept-level counterfactual: an alternate input that differs from the original in a small set of semantically meaningful, human-defined concepts while all other attributes are held fixed. Every contribution is an instantiation of this operation. CAVLI implements it by turning superpixels on and off and correlating the resulting concept sensitivities with decision sensitivities, producing the Concept Dependency Score. ASAC implements it adversarially: perturbations computed against a protected-attribute classifier create inputs whose protected attribute flips while visual content is preserved, and a curriculum orders them by how strongly they deceive the target c

What would settle it

Take a fixed set of occupation prompts, generate the image sets once, then compute CAS/MAD with two different vision-language readers and with a human-annotated concept set for a subset. If the ordering of bias axes by MAD, or the sign of a BiasConnect cross-axis effect, changes across readers, the counterfactual pipeline is not the source of the measurement—the reader is.

Watch

Extended reading notes

Core claim

The paper's core discovery is that the simple counterfactual operation—take an image or prompt, change one semantically meaningful attribute or identity term, keep everything else fixed—can become a single experimental protocol covering the full life cycle of vision model bias. In discriminative models the protocol works at two scales: CAVLI masks random subsets of superpixels and measures overlap between concept-relevant and decision-relevant regions, producing a Concept Dependency Score that exposes spurious reliance (for example, cows classified by grasslands); ASAC instead perturbs pixels against a protected-attribute classifier and uses a difficulty-ranked curriculum of those adversaria

Load-bearing premise

The load-bearing premise is that the text answers a vision-language model gives about generated images accurately describe what is actually visible; the thesis reports an 18% error rate in those answers, so if the vision-language reader misreads images, every downstream bias score and causal link built on those answers is distorted.

Editorial extensions

If this is right

A single evaluation protocol could replace disjoint fairness checklists: define bias axes dynamically per prompt, generate counterfactuals, measure concept shift, and aggregate scores across prompts.
Vision classifiers can be debiased without external generative counterfactual images; ASAC's adversarial perturbations along protected attributes improve fairness gaps and often raise accuracy, avoiding stereotype artifacts that GAN-based counterfactuals introduce.
Intersectional bias can be diagnosed as pairwise causal relationships rather than just subgroup accuracy gaps, so practitioners can anticipate side effects—such as ethnic diversification reducing gender diversity—before intervening.
Bias mitigation can become user-controllable and training-free: InterMit accepts user-defined priorities and target distributions and reports fewer mitigation steps than the ITI-GEN baseline with higher mitigation amount and visual quality.
The same methodology transfers to any black-box text-to-image model, since TIBET and its successors only require prompts and generated images, not model internals.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

I read the thesis as implying that the quantitative conclusions would need to survive a change of concept reader: if a second, independently calibrated VQA model gives different CAS/MAD rankings on the same image sets, then the measurement is partly a property of the reader rather than the generator.
The pairwise causal graphs suggest a natural extension the author does not build: formulate mitigation as constrained optimization over the intersectional sensitivity matrix with user priorities, rather than the multi-step greedy heuristic InterMit uses. This could be tested by comparing InterMit's step selection against a differentiable solver on the same bias axes.
The dynamic-axis idea could transfer to video or 3D generation, where 'holding everything fixed' is harder; a risk is that the LLM-proposed axes quietly become a new fixed checklist unless re-validated per prompt.

Share X Bluesky LinkedIn Reddit HN

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Desk Editor's Note

read the letter

Quick take: this PhD thesis compiles five prior works under a counterfactual umbrella. The strongest new ideas are TIBET's LLM-driven dynamic bias axes and BiasConnect's pairwise causal graphs for intersectional bias; InterMit is also a practical training-free mitigation tool. The chapters stand on their own and the writing is refreshingly honest about limitations. But the quantitative engine—VQA concept frequencies—has a documented 18% error rate that is treated as independent noise, and the causal language outruns the associational measurements. The unified "counterfactual lens" claim is plausible but not fully secured.

What's genuinely good: TIBET's dynamic axes are a real departure from fixed-axis audits. BiasConnect's intersectional graphs are a new application, and InterMit's user-prioritized mitigation is something practitioners could actually use. CAVLI is a straightforward hybrid of LIME and TCAV, but it does give a usable local concept score with heatmaps. The thesis also does something rare: it lists limitations per chapter and in the conclusion, including challenges in metric evaluation and the risk of spurious correlations.

The soft spots are proportionate to the claims. First, the VQA issue: Section 5.5.3 reports a 13.11% MAD error. If MiniGPT-v2's mistakes are systematic—e.g., it shares the same stereotypes as Stable Diffusion—then CAS, MAD, BiasConnect edges, and InterMit estimates all inherit that bias. The sensitivity analysis does not condition on error type, and the user studies validate perceived bias, not the correctness of the concept histograms. That is a real gap, though not fatal. Second, CAVLI's CDS includes TCAV's conceptual sensitivity as a component, so validating CDS against TCAV is partly circular. Third, the causal claims in Chapter 6 need explicit identification assumptions; without them, "causal" should read "associational." Finally, several hyperparameters (noise magnitudes, curriculum order, number of images) look like they were selected after inspecting results, which weakens the ablations.

Who this is for: anyone working on fairness evaluation of TTI models or intersectional bias will find Chapters 5–7 useful. The thesis itself is not a single paper—it reads as a compilation, and I would cite the original TIBET and BiasConnect papers rather than the thesis. But the unified narrative is a good starting point for a reading group on counterfactual methods.

Recommendation: it deserves a serious referee if submitted as a monograph or distilled into a single methodological paper. The individual contributions have already been peer-reviewed, so the referee's job is to probe the integration. The VQA conditioning and causal identification are the points to press. Overall: competent, useful, and worth engaging with—just read the numbers with care.

Referee Report

4 major / 6 minor

Summary. The thesis argues that counterfactual reasoning provides a unifying methodology for interpretability, bias auditing, and bias mitigation in both vision classifiers and text-to-image (TTI) models. It develops and evaluates four main contributions: CAVLI (a hybrid TCAV/LIME concept-dependency score), ASAC (adversarial attribute-specific counterfactuals with curriculum fine-tuning), TIBET (dynamic bias-axis identification and CAS/MAD bias metrics), and BiasConnect/InterMit (pairwise dependency graphs and training-free mitigation with user-defined priorities). The evaluation spans ImageNet, CelebA, UTK, multiple backbones and TTI models, and includes user studies. The central claim is that counterfactuals are not just diagnostic tools but a principled methodology for fairness, causality, and explainability in vision systems.

Significance. If the technical concerns below are resolved, this would be a substantial contribution. The idea of prompt-dependent dynamic bias axes is a clear improvement over fixed-axis audits, and the thesis explicitly addresses intersectionality rather than treating bias dimensions independently. Strengths include the use of user studies to calibrate LLM/VQA-based measurements, multi-dataset and multi-backbone experiments, comparisons against prior methods (ITI-GEN, Ramaswamy et al.), and unusually honest limitation sections. The thesis also makes falsifiable predictions (e.g., CAS/MAD differences across prompts and models) and can be directly applied to existing black-box TTI systems. The main scientific risk is that the quantitative conclusions—especially those in Chapters 5–7—inherit an unquantified systematic error from the VQA concept extraction, and that the word 'causal' is used without identification assumptions.

major comments (4)

[Section 5.5.3 / Section 5.3.3.1] The VQA concept extraction is the single point of failure for CAS, MAD, BiasConnect, and InterMit. The reported 18% VQA error rate and 13.11% MAD error are treated as if errors were independent and random. If MiniGPT-v2 has systematic biases (for example, the same gender/ethnicity associations as Stable Diffusion), then a low CAS can reflect the VQA model's stereotypes rather than the TTI model's behavior. The user studies validate perceived bias, not the correctness of the extracted concept histograms. The thesis needs a conditional error analysis: on what type of images, and for which bias axes, does the VQA model err? Without ground-truth concept annotations or a negative-control test, the main quantitative claims of Chapters 5–7 are not secure.
[Section 6.5 / Section 6.6] The thesis calls BiasGraph 'causal graphs' and describes the approach as 'pairwise causal discovery', but no SCM is specified and no identification assumptions are stated (consistency, positivity, no unobserved confounding, no interference). The text in Section 2.4.2 itself lists hidden confounding as a central challenge, yet Chapter 6 does not apply any sensitivity analysis. The prompt-level intervention (e.g., changing 'gender' words) may affect multiple latent image-generation factors, and the resulting CAS associations need not identify a causal effect. Either state the assumptions under which the do-operator is justified for these text-to-image interventions, or rename the output as 'conditional association graphs' and add synthetic negative-control experiments where the true causal direction is known.
[Section 3.3 / Algorithm 1] The validation of CAVLI is partly circular. CDS is defined in Algorithm 1 (step 9) as CDSi = gamma_i * CS^k_{C,l}(F, x_i), where CS is the TCAV conceptual sensitivity from Eq. (3.1). The chapter then claims that mean CDS 'resembles' TCAV scores and treats this as evidence that CDS captures concept dependence. Since TCAV sensitivity is a multiplicative factor of CDS, a positive correlation is expected by construction. An independent validation is needed: synthetic images with known concept dependencies, human agreement on the concept heatmaps, or an intervention-based test that removes the concept and measures the decision change.
[Section 4.6.3 / Table 4.7 / Table 4.6] Hyperparameters appear to be selected after inspecting the same test-set metrics. Table 4.7 reports choosing noise magnitudes epsilon = {0.001, 0.01} from the ablation on the smile classifier, and Table 4.6 chooses curriculum direction (ascending vs descending difficulty) based on fairness/accuracy outcomes on the same evaluation. If these choices are not made on a separate validation set, the reported gains (e.g., DEO 0.050 vs baseline 0.088) may be partially due to selection bias. The thesis should either perform nested cross-validation or explicitly state which hyperparameters were chosen a priori and which were post-hoc.

minor comments (6)

[Throughout] There are frequent typos and formatting artifacts: 'Adavantages' (Section 2.4), 'consitency' (Section 2.5.2.1), 'TCA V' / 'CA VLI' spacing, 'TIBET' vs 'TIBET' in figure captions. A careful copyedit is needed.
[Eq. (5.1)–(5.3)] The 'Histogram Intersection-over-Union' is not the standard IoU. Please define it explicitly as sum(min(w_i, w_cf)) / sum(max(w_i, w_cf)), and clarify how zero-frequency concepts are treated in the vocabulary.
[Section 5.3.1] The text says GPT-3, but the experiments use gpt-3.5-turbo. The model version and API date matter for reproducibility; please be precise.
[Figure 5.4 / Appendix A.2] Figure 5.4 mentions 'MAD = 0.49' with no explanation of the normalization. Since the normalization is used in all later comparisons, it should be described in the main text, not only in Appendix A.2.
[Chapter 6 / Chapter 7] The terms 'VLLMs' and 'VLMs', 'TTI' and 'T2I', and 'BiasConnect' vs 'BiasGraph' are used inconsistently. Please harmonize notation.
[Reproducibility] No code release, data release, or detailed hyperparameter table is provided. Given the pipeline includes many LLM/VQA components, a reproducibility statement would strengthen the manuscript considerably.

Circularity Check

1 steps flagged · score 3.0 of 10

One partial self-referential validation in CAVLI; TIBET/BiasConnect/InterMit are otherwise externally validated.

self definitional [Section 3.3.1 (ImageNet evaluation) and Algorithm 1 (Section 3.2.4)]
"Calculate Concept Dependency Score: CDSi = γi · CSk C,l(F, xi). ... We propose a hypothesis that if there exists a correlation between the mean CDS scores and global concept methods like TCA V , it indicates that our metric is capable of accurately capturing the dependence between the model decisions and underlying concepts."

CDS is defined as the product of the spatial-overlap correlation γ and the TCAV conceptual sensitivity CS. The validation treats TCAV as an independent reference and takes correlation of mean CDS with TCAV as evidence that CDS captures concept dependence. But because CS is a factor in CDS, the two quantities are coupled by construction; even a CDS that added no concept information beyond TCAV's own sensitivity would correlate with TCAV. The correlation therefore cannot establish the validity of the new overlap term γ. The method still has independent content in γ, so this is a partial, not total, circularity.

full rationale

Aside from the CAVLI validation issue, the thesis is largely self-contained. CAVLI's CDS combines LIME-based perturbation with TCAV; the spatial overlap component is novel. TIBET's CAS/MAD metrics are measured from VQA concept frequencies, with external user studies and ITI-GEN ground-truth comparisons; MAD sensitivity to VQA errors is reported as a limitation, not hidden. BiasConnect is validated against post-mitigation ITI-GEN measurements, an external intervention. InterMit optimizes user-defined target distributions; evaluating against the same objectives is optimization, not circularity. Self-citations to prior papers by the author are normal for a thesis and are not load-bearing: the later chapters use the earlier methods as tools and validate them externally. Overall, one partial self-referential validation prevents a 0-2 score; there is no evidence that the central fairness/causality claims reduce by construction to their inputs.

Assumptions & free parameters 6 free parameters · 5 assumptions · 0 invented entities

No new natural-kind entities are introduced. The thesis proposes measurement constructs (CDS, CAS, MAD, Intersectional Sensitivity) and algorithms, but these are not physical entities.

free parameters (6)

noise magnitudes epsilon (FGSM/PGD) = {0.001, 0.01} for ASACs; {0.03, 0.05} in ablation
Chosen based on observed fairness/accuracy in Section 4.6.3.2; not justified by a principled criterion.
curriculum learning weight alpha = 0.5
Set as default in Section 4.5.2; no sensitivity analysis.
number of superpixels and random subsets in CAVLI = r and n unspecified
Algorithm 1 leaves r and n unspecified; results may depend on these.
number of images per prompt in TIBET = 48
Section 5.3.2; no analysis of effect of sample size.
VQA question templates = hand-designed for common axes
Table 5.2; the choice of questions affects extracted concepts.
number of counterfactuals per axis = K (variable)
MAD is normalized by K; variation in K could affect comparability.

assumptions (5)

domain assumption Uniform CAS distribution across counterfactuals implies low bias; skew implies bias
Section 5.3.4 defines MAD this way; this is a normative assumption about what bias looks like.
domain assumption MiniGPT-v2 VQA provides sufficiently accurate concept extraction
Section 5.3.3 relies on this; the author's own sensitivity analysis (Section 5.5.3) shows 18% error.
domain assumption GPT-3.5 can generate relevant bias axes and counterfactuals
Section 5.3.1; precision/recall in User Study 1 is not perfect.
domain assumption Adversarial perturbations of the protected-attribute classifier preserve semantics and only change the protected attribute
Section 4.4.1; this is the foundation of ASACs but is not empirically verified beyond qualitative examples.
domain assumption Pairwise causal effects measured via prompt interventions are unconfounded and can be combined into a causal graph
Sections 6.4-6.6; no causal identification assumptions (e.g., no hidden confounders) are stated.

how reviews work

0 comments

Cite this review

Pith. "Pith review of Understanding and evaluating computer vision models through the lens of counterfactuals." pith.science (2026). https://pith.science/paper/H3XXXRHX

@misc{pith2026250820881,
  author       = {Pith},
  title        = {Pith review of: Understanding and evaluating computer vision models through the lens of counterfactuals},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/H3XXXRHX}},
  note         = {Machine review of arXiv:2508.20881}
}

read the original abstract

Counterfactual reasoning -- the practice of asking ``what if'' by varying inputs and observing changes in model behavior -- has become central to interpretable and fair AI. This thesis develops frameworks that use counterfactuals to explain, audit, and mitigate bias in vision classifiers and generative models. By systematically altering semantically meaningful attributes while holding others fixed, these methods uncover spurious correlations, probe causal dependencies, and help build more robust systems. The first part addresses vision classifiers. CAVLI integrates attribution (LIME) with concept-level analysis (TCAV) to quantify how strongly decisions rely on human-interpretable concepts. With localized heatmaps and a Concept Dependency Score, CAVLI shows when models depend on irrelevant cues like backgrounds. Extending this, ASAC introduces adversarial counterfactuals that perturb protected attributes while preserving semantics. Through curriculum learning, ASAC fine-tunes biased models for improved fairness and accuracy while avoiding stereotype-laden artifacts. The second part targets generative Text-to-Image (TTI) models. TIBET provides a scalable pipeline for evaluating prompt-sensitive biases by varying identity-related terms, enabling causal auditing of how race, gender, and age affect image generation. To capture interactions, BiasConnect builds causal graphs diagnosing intersectional biases. Finally, InterMit offers a modular, training-free algorithm that mitigates intersectional bias via causal sensitivity scores and user-defined fairness goals. Together, these contributions show counterfactuals as a unifying lens for interpretability, fairness, and causality in both discriminative and generative models, establishing principled, scalable methods for socially responsible bias evaluation and mitigation.

Figures

Figures reproduced from arXiv: 2508.20881 by the authors.

**Figure 1.1.** Counterfactual reasoning reveals hiring bias. In a famous study conducted by Moss [PITH_FULL_IMAGE:figures/full_fig_p021_1_1.png] view at source ↗

**Figure 1.2.** This figure illustrates how counterfactual reasoning is employed throughout the thesis to probe and enhance the capabilities of computer vision models. In Chapter 3, we introduce CAVLI, a method for quantifying the influence of visual concepts on model decisions via counterfactuals. Chapter 4 leverages counterfactuals for bias mitigation using ASACs, showing their efficacy in reducing unfair model behavior. In Chapt… view at source ↗

**Figure 2.1.** A brief timeline tracing the development of counterfactual reasoning. While the term [PITH_FULL_IMAGE:figures/full_fig_p031_2_1.png] view at source ↗

Figures from the paper (32 more)

**Figure 3.1.** Figure 3.1: This chapter begins with a central question: When a black-box model predicts a label [PITH_FULL_IMAGE:figures/full_fig_p054_3_1.png]

**Figure 3.2.** Figure 3.2: Overview of our proposed approach, CAVLI, to estimate the dependence of a concept [PITH_FULL_IMAGE:figures/full_fig_p057_3_2.png]

**Figure 3.3.** Figure 3.3: We use concept and decision heatmaps to analyze a classifier’s decisions and their [PITH_FULL_IMAGE:figures/full_fig_p065_3_3.png]

**Figure 4.1.** Figure 4.1: What happens when we ask a counterfactual generation engine to modify an image [PITH_FULL_IMAGE:figures/full_fig_p068_4_1.png]

**Figure 4.2.** Figure 4.2: An example of gender-based counterfactuals generated by StyleGAN2 and our method [PITH_FULL_IMAGE:figures/full_fig_p070_4_2.png]

**Figure 4.3.** Figure 4.3: Bias Mitigation Strategy: Our proposed solution for mitigating biases in a model (e.g., smile classifier) M(θ,ρ) involves training sensitive attribute classifier C(θ,ϕ) (shown in the network architecture). We then follow a three-stage pipeline. (1) We generate ASACs …

**Figure 4.4.** Figure 4.4: Qualitative results showing that our trained model becomes robust to ASACs after [PITH_FULL_IMAGE:figures/full_fig_p083_4_4.png]

**Figure 5.1.** Figure 5.1: In generative modeling, the dimensions along which bias manifests are highly dependent [PITH_FULL_IMAGE:figures/full_fig_p090_5_1.png]

**Figure 5.2.** Figure 5.2: TIBET. Given an input prompt, we query an LLM (GPT-3) to identify axes of biases (Step 1), and generate counterfactual prompts for each axis of bias (Step 2). Here, we show a sample of three counterfactual prompts for the physical appearance bias, and two for the abl…

**Figure 5.3.** Figure 5.3: VQA-based Image Comparison. The VQA process in the Image Comparison Module. For each image in the initial set and the counterfactual set, we ask a list of questions to the VQA model, MiniGPT-v2, to extract concepts. The underlined words indicate the axis of bias that…

**Figure 5.4.** Figure 5.4: Metrics: (a) MAD is low when the CAS scores are uniform across all counterfactuals, and high when the CAS scores are skewed. (b) MAD is only dependent on variability in CAS, not on amount of CAS occurring concepts in a given image for a specific bias axis. To calcula…

**Figure 5.5.** Figure 5.5: Usefulness of TIBET. In this example setting, we show how TIBET can be useful to a user concerned about biases in the images generated by a TTI model. We show how TIBET can analyse biases along human-observable axes of bias, with post-hoc explainablity. 84 [PITH_FUL…

**Figure 5.6.** Figure 5.6: Analysis enabled by TIBET. Our approach calculates CAS and MAD scores to measure association with counterfactual prompts and bias degree in generated images. Qualitative metrics like Top-K Concepts and Axis-Aligned Top-K Concepts offer post-hoc model explanations. Ad…

**Figure 5.7.** Figure 5.7: Sensitivity Analysis on CAS and MAD for errors in VQA. Per User Study 3 , we estimate an 18% error rate in VQA, leading to 4.73% and 13.11% error in CAS and MAD respectively. Diffusion 2.1 seems to reduce bias among a few of these professions, notably “accountant” an…

**Figure 5.8.** Figure 5.8: Comparing our VQA and CLIP methods for Image Comparison. In this example, we see that humans rank racial bias to be more significant compared to gender bias, which is also observable in the images. We compare our VQA-based method to our CLIP-based method, and observe…

**Figure 5.9.** Figure 5.9: Bias identification and mitigation. We compute difference in CAS scores for male and female counterfactuals for 11 occupation prompts. (a) and (b) show male and female leaning professions using Stable Diffusion 1.5 and 2.1 respectively. (c) shows how the difference i…

**Figure 5.10.** Figure 5.10: Bias Identification and Mitigation using TIBET and ITI-GEN - Ground Truth. Here, we show ground truth gender differences in the initial set of images before bias mitigation, and after bias mitigation. The reduction in gender bias is in line with what we observe usin…

**Figure 6.1.** Figure 6.1: This figure demonstrates how a minimal prompt change—from “A [PITH_FULL_IMAGE:figures/full_fig_p117_6_1.png]

**Figure 6.2.** Figure 6.2: Exploring Intersectionality of Biases: Analysing the Top-K concepts shows that pharmacists in Europe and Asia are depicted with different gender distributions. (a) Intersectionality in "a photo of a chef": When we study counterfactuals for "geographical bias", how do…

**Figure 6.3.** Figure 6.3: Intersectional Observations using TIBET. We observe that images generated for a chef in Africa may be depicted outdoors (tree) unlike chefs in other regions of the world. Using TIBET, we can study intersectionality by observing counterfactuals along one bias axis, an…

**Figure 6.4.** Figure 6.4: An example for which BiasConnect estimates a negative impact of bias mitigation along [PITH_FULL_IMAGE:figures/full_fig_p125_6_4.png]

**Figure 6.5.** Figure 6.5: An overview of BiasConnect. We use a counterfactual-based approach to measure pairwise causality between bias axes. For dependent axes, we measure the causal effect, estimating how bias mitigation on one axis impacts another. 6.4.1.1 Counterfactual Prompts & Image Ge…

**Figure 6.6.** Figure 6.6: Analyzing bias intersectionality matrices from BiasConnect. (a) Shows how mitigating [PITH_FULL_IMAGE:figures/full_fig_p130_6_6.png]

**Figure 6.7.** Figure 6.7: Sensitivity analysis on BiasConnect. We evaluate the robustness of our approach by analyzing the impact of VQA errors and the effect of the number of images on Intersectional Sensitivity . These high correlations demonstrate that our framework effectively predicts th…

**Figure 6.8.** Figure 6.8: We compare aggregated causal graphs for four models: Stable Diffusion 1.4, Flux-dev, [PITH_FULL_IMAGE:figures/full_fig_p136_6_8.png]

**Figure 6.9.** Figure 6.9: Additional examples on TIBET dataset (a-b) and Occupation prompt (c) on prompt-level [PITH_FULL_IMAGE:figures/full_fig_p138_6_9.png]

**Figure 6.10.** Figure 6.10: Comparison of real-world and Stable Diffusion 3.5 pairwise causal relationships for [PITH_FULL_IMAGE:figures/full_fig_p139_6_10.png]

**Figure 6.11.** Figure 6.11: The figure illustrates bias interpretations from Bias Connects, combining all pairwise [PITH_FULL_IMAGE:figures/full_fig_p140_6_11.png]

**Figure 7.1.** Figure 7.1: Key challenges in mitigating bias in TTI models. Effective methods must handle how [PITH_FULL_IMAGE:figures/full_fig_p143_7_1.png]

**Figure 7.2.** Figure 7.2: Overview of the InterMit pipeline. Prompts are first analyzed using BiasConnect and [PITH_FULL_IMAGE:figures/full_fig_p147_7_2.png]

**Figure 7.3.** Figure 7.3: Examples of InterMit mitigation. Blue columns are mitigated. (a) and (c) show efficient [PITH_FULL_IMAGE:figures/full_fig_p156_7_3.png]

**Figure 7.4.** Figure 7.4: Modifying D∗ (ideal distribution) in InterMit can have a significant effect on the Intersectional Sensitivity values. Intermit accoounts for this change during the mitigation process and allows user to specify the ideal distribution they want to have. 139 [PITH_FULL…

Discussion (0). Sign in to comment.

Reference graph

Works this paper leans on

227 extracted references · 51 canonical work pages

[1]

Moss-Racusin, John F

Corinne A. Moss-Racusin, John F. Dovidio, Victoria L. Brescoll, Mark J. Graham, and Jo Handelsman. Science faculty’s subtle gender biases favor male students. Proceedings of the National Academy of Sciences, 109(41):16474–16479, 2012

2012
[2]

Counterfactuals

David Lewis. Counterfactuals. Harvard University Press, 1973

1973
[3]

High-resolution image synthesis with latent diffusion models

Robin Rombach, Andreas Blattmann, Dominik Lorenz, Patrick Esser, and Bj ¨orn Ommer. High-resolution image synthesis with latent diffusion models. InProceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 10684–10695, 2022

2022
[4]

Towards accuracy-fairness paradox: Adversarial example-based data augmentation for visual debiasing

Yi Zhang and Jitao Sang. Towards accuracy-fairness paradox: Adversarial example-based data augmentation for visual debiasing. In Proceedings of the 28th ACM International Conference on Multimedia, pages 4346–4354, 2020

2020
[5]

Ramaswamy, Sunnie SY

Vikram V . Ramaswamy, Sunnie SY . Kim, and Olga Russakovsky. Fair attribute classification through latent space de-biasing. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 9301–9310, 2021

2021
[6]

Image Counterfactual Sensitivity Analysis for Detecting Unintended Bias

Emily Denton, Ben Hutchinson, Margaret Mitchell, Timnit Gebru, and Andrew Zaldivar. Image counterfactual sensitivity analysis for detecting unintended bias. arXiv preprint arXiv:1906.06439, 2019

work page Pith review arXiv 1906
[7]

Towards fairness in visual recognition: Effective strategies for bias mitigation

Zeyu Wang, Klint Qinami, Ioannis Christos Karakozis, Kyle Genova, Prem Nair, Kenji Hata, and Olga Russakovsky. Towards fairness in visual recognition: Effective strategies for bias mitigation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 8919–8928, 2020

2020
[8]

Balasubramanian, and Amit Sharma

Saloni Dash, Vineeth N. Balasubramanian, and Amit Sharma. Evaluating and mitigating bias in image classifiers: A causal perspective using counterfactuals. In Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pages 915–924, 2022

2022

Show all 227 references

[9]

Towards causal bench- marking of biasin face analysis algorithms

Guha Balakrishnan, Yuanjun Xiong, Wei Xia, and Pietro Perona. Towards causal bench- marking of biasin face analysis algorithms. In Deep Learning-Based Face Analytics, pages 327–359. Springer, 2021

2021
[10]

Meaningfully debugging model mistakes using conceptual counterfactual explanations

Abubakar Abid, Mert Yuksekgonul, and James Zou. Meaningfully debugging model mistakes using conceptual counterfactual explanations. In Proceedings of the International Conference on Machine Learning, pages 66–88. PMLR, 2022. 150

2022
[11]

Causalm: Causal model explanation through counterfactual language models

Amir Feder, Nadav Oved, Uri Shalit, and Roi Reichart. Causalm: Causal model explanation through counterfactual language models. Computational Linguistics, 47(2):333–386, 2021

2021
[12]

Cavli-using image associations to produce local concept-based explanations

Pushkar Shukla, Sushil Bharati, and Matthew Turk. Cavli-using image associations to produce local concept-based explanations. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 3749–3754, 2023

2023
[13]

Selvaraju, Michael Cogswell, Abhishek Das, Ramakrishna Vedantam, Devi Parikh, and Dhruv Batra

Ramprasaath R. Selvaraju, Michael Cogswell, Abhishek Das, Ramakrishna Vedantam, Devi Parikh, and Dhruv Batra. Grad-cam: Visual explanations from deep networks via gradient- based localization. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 61...

2017
[14]

Tibet: Identifying and evaluating biases in text-to-image generative models

Aditya Chinchure, Pushkar Shukla, Gaurav Bhatt, Kiri Salij, Kartik Hosanagar, Leonid Sigal, and Matthew Turk. Tibet: Identifying and evaluating biases in text-to-image generative models. Proceedings of the European Conference on Computer Vision, 2024

2024
[15]

Demarginalizing the intersection of race and sex: A black feminist critique of antidiscrimination doctrine, feminist theory, and antiracist politics

Kimberle Crenshaw. Demarginalizing the intersection of race and sex: A black feminist critique of antidiscrimination doctrine, feminist theory, and antiracist politics. In University of Chicago Legal Forum, pages 139–167. 1989

1989
[16]

Utilizing adversarial examples for bias mitigation and accuracy enhancement

Pushkar Shukla, Dhruv Srikanth, Lee Cohen, and Matthew Turk. Utilizing adversarial examples for bias mitigation and accuracy enhancement. arXiv preprint arXiv:2404.11819, 2024

2024 arXiv
[17]

Balasubramanian, Leonid Sigal, and Matthew Turk

Pushkar Shukla, Aditya Chinchure, Emily Diana, Alexander Tolbert, Kartik Hosanagar, Vineeth N. Balasubramanian, Leonid Sigal, and Matthew Turk. Mitigate one, skew another? tackling intersectional biases in text-to-image models. arXiv preprint arXiv:2505.17280, 2025

2025 arXiv
[18]

Counterfactual visual explanations

Yash Goyal, Ziyan Wu, Jonas Ernst, Dhruv Batra, Devi Parikh, and Stefan Lee. Counterfactual visual explanations. In Proceedings of the International Conference on Machine Learning, 2019

2019
[19]

Counterfactual explanations without opening the black box: Automated decisions and the gdpr

Sandra Wachter, Brent Mittelstadt, and Chris Russell. Counterfactual explanations without opening the black box: Automated decisions and the gdpr. Harvard Journal of Law & Technology, 2017

2017
[20]

Estimating causal effects of treatments in randomized and nonrandomized studies

Donald B Rubin. Estimating causal effects of treatments in randomized and nonrandomized studies. Journal of Educational Psychology, 66(5):688–701, 1974

1974
[21]

Causality: Models, Reasoning and Inference

Judea Pearl. Causality: Models, Reasoning and Inference. Cambridge University Press, 2000

2000
[22]

Mothilal, Amit Sharma, and Chenhao Tan

Ramaravind K. Mothilal, Amit Sharma, and Chenhao Tan. Explaining machine learning classifiers through diverse counterfactual explanations. In Proceedings of the 2020 conference on Fairness, Accountability, and Transparency, pages 607–617, 2020

2020
[23]

Tongshuang Wu, Marco Tulio Ribeiro, Jeffrey Heer, and Daniel S. Weld. Polyjuice: Gen- erating counterfactuals for explaining, evaluating, and improving models. In Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th 151 Internati...

2021
[24]

Counterfactual fairness

Matt J Kusner, Joshua Loftus, Chris Russell, and Ricardo Silva. Counterfactual fairness. Advances in Neural Information Processing Systems, 30, 2017

2017
[25]

Counterfactual sensitivity and robustness

Timothy Christensen and Benjamin Connault. Counterfactual sensitivity and robustness. Econometrica, 91(1):263–298, 2023

2023
[26]

Divyansh Kaushik, Eduard Hovy, and Zachary C. Lipton. Learning the difference that makes a difference with counterfactually-augmented data. arXiv preprint arXiv:1909.12434, 2019

1909 arXiv
[27]

Counterfactuals

Jonathan Bennett and Kai von Fintel. Counterfactuals. https://plato.stanford. edu/entries/counterfactuals/, 2021. Stanford Encyclopedia of Philosophy, Accessed: 2025-06-12

2021
[28]

Argumentation and counterfactual reasoning in parmenides and melissus

Andr´e Laks. Argumentation and counterfactual reasoning in parmenides and melissus. Archai: Revista de Estudos sobre as Origens do Pensamento Ocidental , 9:45–56, 2012. URL https://www.scielo.br/j/archai/a/Cv7GBGFGwMZDTfQfmWK99Pr/ ?lang=en. Accessed: 2025-06-12

2012
[29]

Philosophy in Classical India: The Proper Work of Reason

Jonardon Ganeri. Philosophy in Classical India: The Proper Work of Reason . Oxford University Press, 2001

2001
[30]

The Character of Logic in India

Bimal Krishna Matilal and Kisor Kumar Chakrabarti. The Character of Logic in India. State University of New York Press, 1998

1998
[31]

Georges B.J. Dreyfus. Recognizing Reality: Dharmak ¯ırti’s Philosophy and Its Tibetan Interpretations. SUNY Press, 1997

1997
[32]

An Enquiry Concerning Human Understanding

David Hume. An Enquiry Concerning Human Understanding. A. Millar, 1748. Originally published 1748; many modern editions available
[33]

Essays on the Active Powers of the Human Mind

Thomas Reid. Essays on the Active Powers of the Human Mind. Bell & Bradfute, 1788
[34]

Critique of Pure Reason

Immanuel Kant. Critique of Pure Reason. Hartknoch, 1781. English translations by Norman Kemp Smith and others available
[35]

A System of Logic, Ratiocinative and Inductive

John Stuart Mill. A System of Logic, Ratiocinative and Inductive. Parker, 1843. See Book III for the Method of Difference
[36]

The Emotions and the Will

Alexander Bain. The Emotions and the Will. Longman, Green, Longman, and Roberts, 1859
[37]

Causality: Models, Reasoning, and Inference

Judea Pearl. Causality: Models, Reasoning, and Inference. Cambridge University Press, 2 edition, 2009

2009
[38]

Efficient search for diverse coherent explanations

Chris Russell. Efficient search for diverse coherent explanations. Proceedings of the 2020 FAT* Conference, 2019. 152

2020
[39]

Multi-objective counter- factual explanations

Susanne Dandl, Christoph Molnar, Martin Binder, and Bernd Bischl. Multi-objective counter- factual explanations. Machine Learning, 110(4):961–989, 2021

2021
[40]

Face: Feasible and actionable counterfactual explanations

Rafael Poyiadzi, Kacper Sokol, Raul Santos-Rodriguez, Tijl De Bie, and Peter Flach. Face: Feasible and actionable counterfactual explanations. Proceedings of the AAAI Conference on Artificial Intelligence, 34(03):1399–1407, 2020

2020
[41]

Model-agnostic counter- factual explanations for consequential decisions

Amir-Hossein Karimi, Gilles Barthe, Borja Balle, and Isabel Valera. Model-agnostic counter- factual explanations for consequential decisions. In International Conference on Artificial Intelligence and Statistics, pages 895–905. PMLR, 2020

2020
[42]

Focus: Flexible optimizable counterfactual explanations for tree ensembles

Ana-Marija Lucic, Hinda Haned, and Maarten de Rijke. Focus: Flexible optimizable counterfactual explanations for tree ensembles. In Proceedings of the 2022 Conference on Fairness, Accountability, and Transparency, 2022

2022
[43]

Hidden assumptions in counterfactual explanations and fairness

Solon Barocas, Moritz Hardt, and Arvind Narayanan. Hidden assumptions in counterfactual explanations and fairness. Communications of the ACM, 63(9):62–71, 2020

2020
[44]

Certifai: A common framework to provide explanations and recourse through counterfactuals

Amit Sharma, Peter Henderson, Shubham Ghosh, Prateek Agarwal, and Matei Zaharia. Certifai: A common framework to provide explanations and recourse through counterfactuals. arXiv preprint arXiv:1905.07831, 2020

1905 arXiv
[45]

Good counterfactuals and where to find them: A case-based technique for generating counterfactuals for explainable ai (xai)

Mark T Keane and Barry Smyth. Good counterfactuals and where to find them: A case-based technique for generating counterfactuals for explainable ai (xai). Proceedings of the AAAI Conference on Artificial Intelligence, 34(03):1364–1371, 2020

2020
[46]

Path-specific counterfactual fairness

Silvia Chiappa. Path-specific counterfactual fairness. In Proceedings of the AAAI conference on Artificial Intelligence, volume 33, pages 7801–7808, 2019

2019
[47]

Fairness through causal awareness: Learning causal latent representations for fair prediction

David Madras, Elliot Creager, Toniann Pitassi, and Richard Zemel. Fairness through causal awareness: Learning causal latent representations for fair prediction. Proceedings of the 35th Conference on Uncertainty in Artificial Intelligence, 2019

2019
[48]

Chi, and Alex Beutel

Sahaj Garg, Vincent Perot, Nicole Limtiaco, Ankur Taly, Ed H. Chi, and Alex Beutel. Counterfactual fairness in text classification through robustness. In Proceedings of the 2019 AAAI/ACM Conference on AI, Ethics, and Society, pages 219–226, 2019

2019
[49]

Conditional counterfactuals for fairness audits in nlp

Maarten Hall and Hanna Wallach. Conditional counterfactuals for fairness audits in nlp. Proceedings of the Conference on Empirical Methods in Natural Language Processing, 2020

2020
[50]

Fliptest: Fairness testing via statistical and semantic counterfactual generation

Emily Black, Alireza Farhadi, and Osbert Bastani. Fliptest: Fairness testing via statistical and semantic counterfactual generation. Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency, 2021

2021
[51]

Constructing and measuring visual counterfactuals for grounded bias analysis

Aparna Balagopalan, Vajira Thambawita, Sigurdur Olafsson, Jesse Hicks, Egil Arnesen, Thomas Goodwin, Eric Breck, and James Wexler. Constructing and measuring visual counterfactuals for grounded bias analysis. In CVPR Workshop on Responsible Vision, 2022. 153

2022
[52]

Evaluating bias and fairness in pretrained multimodal models

Jieyu Zhao, Tianlu Wang, Daniel Khashabi, Sameer Singh, and Dan Roth. Evaluating bias and fairness in pretrained multimodal models. arXiv preprint arXiv:2204.11756, 2022

2022 arXiv
[53]

A survey on bias and fairness in machine learning

Ninareh Mehrabi, Fred Morstatter, Nripsuta Saxena, Kristina Lerman, and Aram Galstyan. A survey on bias and fairness in machine learning. ACM Computing Surveys (CSUR), 54(6): 1–35, 2021

2021
[54]

Counterfactual fairness through semantic data augmentation

Tien Wu, Allison Woodruff, Jilin Chen, and Munmun De Choudhury. Counterfactual fairness through semantic data augmentation. In Proceedings of the AAAI Conference on Artificial Intelligence, 2021

2021
[55]

Actionable recourse in linear classification

Berk Ustun, Alexander Spangher, and Yang Liu. Actionable recourse in linear classification. In Proceedings of the Conference on Fairness, Accountability, and Transparency, pages 10–19, 2019

2019
[56]

Explaining and harnessing adver- sarial examples

Ian J Goodfellow, Jonathon Shlens, and Christian Szegedy. Explaining and harnessing adver- sarial examples. Proceedings of the International Conference on Learning Representations, 2015

2015
[57]

Benchmarking neural network robustness to common corruptions and perturbations

Dan Hendrycks and Thomas Dietterich. Benchmarking neural network robustness to common corruptions and perturbations. arXiv preprint arXiv:1903.12261, 2019

1903 arXiv
[58]

Reassessing the robustness of visual counterfactual explanations

Robin Morris, Brandon Carter, and William Herlands. Reassessing the robustness of visual counterfactual explanations. arXiv preprint arXiv:2105.08130, 2021

2021 arXiv
[59]

Counterfactual data augmen- tation for mitigating gender stereotypes in languages with rich morphology

Ran Zmigrod, Ryan Cotterell, Jacob Andreas, and Frank Keller. Counterfactual data augmen- tation for mitigating gender stereotypes in languages with rich morphology. In Proceedings of Association of Computational Linguists, 2019

2019
[60]

Aguirre, and Thomas Sch ¨on

Antˆonio H Ribeiro, Koen Tiels, Luis A. Aguirre, and Thomas Sch ¨on. Beyond exploding and vanishing gradients: analysing rnn training using attractors and smoothness. In International Conference on Artificial Intelligence and Statistics, pages 2370–2380. PMLR, 2020

2020
[61]

Estimating individual treatment effect: generalization bounds and algorithms

Uri Shalit, Fredrik D Johansson, and David Sontag. Estimating individual treatment effect: generalization bounds and algorithms. In Proceedings of the International conference on machine Learning, pages 3076–3085. PMLR, 2017

2017
[62]

Learning representations for counterfactual inference

Fredrik Johansson, Uri Shalit, and David Sontag. Learning representations for counterfactual inference. In Proceedings of the International conference on machine Learning , pages 3020–3029. PMLR, 2016

2016
[63]

Causal effect inference with deep latent-variable models

Christos Louizos, Uri Shalit, Joris M Mooij, David Sontag, Richard Zemel, and Max Welling. Causal effect inference with deep latent-variable models. Advances in Neural Information Processing Systems, 30, 2017

2017
[64]

Ganite: Estimation of individual- ized treatment effects using generative adversarial nets

Jinsung Yoon, James Jordon, and Mihaela Van Der Schaar. Ganite: Estimation of individual- ized treatment effects using generative adversarial nets. In Proceedings of the International Conference on Learning Representations, 2018. 154

2018
[65]

Learn: Controllable counterfactual genera- tion via latent disentanglement

Xinyi Lu, Piji Qi, and William Yang Wang. Learn: Controllable counterfactual genera- tion via latent disentanglement. Findings North American Chapter of the Association for Computational Linguistics, 2022

2022
[66]

Interpretations are useful: Penalizing explanations to align neural networks with prior knowledge

Laura Rieger, Chandresh Singh, W James Murdoch, and Bin Yu. Interpretations are useful: Penalizing explanations to align neural networks with prior knowledge. In Proceedings of the International Conference on Machine Learning, 2020

2020
[67]

A survey on counterfactual data generation for text

Zixuan Zhang, Lei Huang, Hongyu Zhang, and Yulan He. A survey on counterfactual data generation for text. arXiv preprint arXiv:2302.04702, 2023

2023 arXiv
[68]

Explanation by progressive exaggeration

Sahil Singla and Soheil Feizi. Explanation by progressive exaggeration. In Proceedings of the International Conference on Machine Learning, 2019

2019
[69]

Explaining image classifiers using statistical fault localization

Chun-Hao Chang, Cedric Anders, Andreas Muller, and Been Kim. Explaining image classifiers using statistical fault localization. In Advances in Neural Information Processing Systems, 2019

2019
[70]

Gan dissection: Visualizing and understanding generative adversarial networks

David Bau, Jun-Yan Zhu, Hendrik Strobelt, Bolei Zhou, Josh Tenenbaum, Bill Freeman, and Antonio Torralba. Gan dissection: Visualizing and understanding generative adversarial networks. Proceedings of the International Conference on Learning Representations, 2019

2019
[71]

Explaining black-box classifiers using post-hoc explanations: A unified review

Kedar Dhamdhere, Mukund Sundararajan, and Qiqi Yan. Explaining black-box classifiers using post-hoc explanations: A unified review. arXiv preprint arXiv:1909.13584, 2019

1909 arXiv
[72]

On robustness of causal claims in text classification

Shyam Upadhyay, Kai-Wei Chang, and Dan Roth. On robustness of causal claims in text classification. In Proceedings of the Annual Meeting of the Association for Computational Linguistics, 2021

2021
[73]

Preserving semantic relations for zero-shot learning

Dibya Mahajan, S Karthik, and Abhishek Mishra. Preserving semantic relations for zero-shot learning. arXiv preprint arXiv:1911.05071, 2019

1911 arXiv
[74]

Jon Gauthier, Tianyi Zhang, Bernd Bohnet, and Alexander M. Rush. Counterfactual data augmentation for robust nlp. In Proceedings of the Annual Meeting of the Association for Computational Linguistics, 2021

2021
[75]

Donghyun Kim, Siddhartha Dalmia, and Alan W. Black. Counterfactual reasoning in multimodal language models. In Proceedings of the Conference on Empirical Methods in Natural Language Processing, 2022

2022
[76]

Anchors: High-precision model- agnostic explanations

Marco Tulio Ribeiro, Sameer Singh, and Carlos Guestrin. Anchors: High-precision model- agnostic explanations. In AAAI Conference on Artificial Intelligence, 2018

2018
[77]

Grounding visual explanations

Lisa Anne Hendricks, Ronghang Hu, Trevor Darrell, and Zeynep Akata. Grounding visual explanations. In Proceedings of the European Conference on Computer Vision, 2018

2018
[78]

Algorithmic re- course: From counterfactual explanations to interventions

Amir-Hossein Karimi, Gilles Barthe, Bernhard Sch¨olkopf, and Isabel Valera. Algorithmic re- course: From counterfactual explanations to interventions. In Advances in Neural Information Processing Systems, 2020. 155

2020
[79]

Karthik, and Vivek Srikumar

Dibya Mahajan, Aditya Singh, S. Karthik, and Vivek Srikumar. Generating interpretable coun- terfactual explanations by structural interventions. Proceedings of International Conference on Machine Learning, 2023

2023
[80]

Friedler, John Moeller, Carlos Scheidegger, and Suresh Venkata- subramanian

Michael Feldman, Sorelle A. Friedler, John Moeller, Carlos Scheidegger, and Suresh Venkata- subramanian. Certifying and removing disparate impact. In Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2015

2015
[81]

Equality of opportunity in supervised learning

Moritz Hardt, Eric Price, and Nati Srebro. Equality of opportunity in supervised learning. Advances in Neural Information Processing Systems, 29, 2016

2016
[82]

Fairness through awareness

Cynthia Dwork, Moritz Hardt, Toniann Pitassi, Omer Reingold, and Richard Zemel. Fairness through awareness. In Proceedings of the 3rd Innovations in Theoretical Computer Science Conference, 2012

2012
[83]

Inherent trade-offs in the fair determination of risk scores

Jon Kleinberg, Sendhil Mullainathan, and Manish Raghavan. Inherent trade-offs in the fair determination of risk scores. arXiv preprint arXiv:1609.05807, 2016

2016 arXiv
[84]

Fairness metrics: A comparative study

Samira Garg, Vinodkumar Prabhakaran, and Nanyun Peng. Fairness metrics: A comparative study. In Proceedings of the Annual Meeting of the Association for Computational Linguistics, 2020

2020
[85]

Trading off fairness and accuracy in algorithmic recourse

Daniel Roessler, Harvineet Singh, and Himabindu Lakkaraju. Trading off fairness and accuracy in algorithmic recourse. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society, 2022

2022
[86]

Gender bias in coreference resolution: Evaluation and debiasing methods

Jieyu Zhao, Tianlu Wang, Mark Yatskar, Vicente Ordonez, and Kai-Wei Chang. Gender bias in coreference resolution: Evaluation and debiasing methods. In Proceedings of the Annual Meeting of the Association for Computational Linguistics : Human Language Technologies, 2018

2018
[87]

Chang, James Y

Tolga Bolukbasi, Kai W. Chang, James Y . Zou, Venkatesh Saligrama, and Adam T. Kalai. Man is to computer programmer as woman is to homemaker? debiasing word embeddings. Advances in Neural Information Processing Systems, 29, 2016

2016
[88]

The woman worked as a babysitter: On biases in language generation

Emily Sheng, Kai-Wei Chang, Premkumar Natarajan, and Nanyun Peng. The woman worked as a babysitter: On biases in language generation. In Proceedings of the Conference on Empirical Methods in Natural Language Processing, 2019

2019
[89]

Maarten Sap, Dallas Card, Saadia Gabriel, Yejin Choi, and Noah A. Smith. The risk of racial bias in hate speech detection. In Proceedings of the Annual Meeting of the Association for Computational Linguistics, 2019

2019
[90]

Counterfactual probing for understanding gender bias in masked language models

Vinay Uday Prabhu et al. Counterfactual probing for understanding gender bias in masked language models. In Proceedings of the Conference of the North American Chapter of the Association for Computational Linguistics, 2023. 156

2023
[91]

Adversarial removal of demographic attributes from text data

Yanai Elazar and Yoav Goldberg. Adversarial removal of demographic attributes from text data. In Proceedings of the Conference on Empirical Methods in Natural Language Processing, 2018

2018
[92]

Investigating gender bias in language models using causal mediation analysis

Jesse Vig and Yonatan Belinkov. Investigating gender bias in language models using causal mediation analysis. In Advances in Neural Information Processing Systems, 2020

2020
[93]

The red team blues: A survey of failures in rlhf models

Deep Ganguli, Amanda Askell, Long Ouyang, et al. The red team blues: A survey of failures in rlhf models. arXiv preprint arXiv:2209.07858, 2022

2022 arXiv
[94]

Holistic evaluation of language models

Percy Liang, Rishi Bommasani, Eli Zelikman, et al. Holistic evaluation of language models. In Proceedings of the 2022 ACM Conference on Fairness, Accountability, and Transparency, 2022

2022
[95]

Multimodal datasets: Misogyny, pornography, and malignant stereotypes

Abeba Birhane and Vinay Uday Prabhu. Multimodal datasets: Misogyny, pornography, and malignant stereotypes. arXiv preprint arXiv:2110.01963, 2021

2021 arXiv
[96]

Gender shades: Intersectional accuracy disparities in com- mercial gender classification

Joy Buolamwini and Timnit Gebru. Gender shades: Intersectional accuracy disparities in com- mercial gender classification. In Conference on Fairness, Accountability and Transparency, pages 77–91. PMLR, 2018

2018
[97]

Actionable auditing: Investigating the impact of publicly naming biased performance results of commercial ai products

Inioluwa Deborah Raji, Joy Buolamwini, Margaret Mitchell, Timnit Gebru, and Alex Hanna. Actionable auditing: Investigating the impact of publicly naming biased performance results of commercial ai products. In Proceedings of the 2019 AAAI/ACM Conference on AI, Ethics, and Soci...

2019
[98]

Lawrence Zitnick

Tsung-Yi Lin, Michael Maire, Serge Belongie, James Hays, Pietro Perona, Deva Ramanan, Piotr Doll´ar, and C. Lawrence Zitnick. Microsoft coco: Common objects in context. In Proceedings of the European Conference on Computer Vision, pages 740–755. Springer, 2014

2014
[99]

The open images dataset v4: Unified image classification, object detection, and visual relationship detection at scale

Alina Kuznetsova, Hassan Rom, Neil Alldrin, Jasper Uijlings, Ivan Krasin, Jordi Pont-Tuset, Shahab Kamali, Stefan Popov, Matteo Malloci, Alexander Kolesnikov, et al. The open images dataset v4: Unified image classification, object detection, and visual relationship detection a...

1956
[100]

Ethical dilemmas in generative vision systems

Jieyu Zhao, Yuyan Chen, Kai-Wei Chang, et al. Ethical dilemmas in generative vision systems. In Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency, 2021

2021
[101]

T2iat: Measuring valence and stereotypical biases in text-to-image generation

Jialu Wang, Xinyue Gabby Liu, Zonglin Di, Yang Liu, and Xin Wang. T2iat: Measuring valence and stereotypical biases in text-to-image generation. In Findings of the Association for Computational Linguistics: ACL 2023, 2023

2023
[102]

Dall-eval: Probing the reasoning skills and social biases of text-to-image generation models

Jaemin Cho, Abhay Zala, and Mohit Bansal. Dall-eval: Probing the reasoning skills and social biases of text-to-image generation models. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 3043–3054, 2023

2023
[103]

’person’== light-skinned, western man, and sexualization of women of color: Stereotypes in stable diffusion

Sourojit Ghosh and Aylin Caliskan. ’person’== light-skinned, western man, and sexualization of women of color: Stereotypes in stable diffusion. arXiv preprint arXiv:2310.19981, 2023. 157

2023 arXiv
[104]

Easily accessible text-to-image generation amplifies demographic stereotypes at large scale

Federico Bianchi, Pratyusha Kalluri, Esin Durmus, Faisal Ladhak, Myra Cheng, Debora Nozza, Tatsunori Hashimoto, Dan Jurafsky, James Zou, and Aylin Caliskan. Easily accessible text-to-image generation amplifies demographic stereotypes at large scale. In Proceedings of the 2023 ...

2023
[105]

Fair diffusion: Instructing text-to-image generation models on fairness

Felix Friedrich, Patrick Schramowski, Manuel Brack, Lukas Struppek, Dominik Hintersdorf, Sasha Luccioni, and Kristian Kersting. Fair diffusion: Instructing text-to-image generation models on fairness. arXiv preprint arXiv:2302.10893, 2023

2023 arXiv
[106]

Mitigating stereotypical biases in text to image generative systems

Piero Esposito, Parmida Atighehchian, Anastasis Germanidis, and Deepti Ghadiyaram. Mitigating stereotypical biases in text to image generative systems. arXiv preprint arXiv:2310.06904, 2023

2023 arXiv
[107]

Datasheets for datasets

Timnit Gebru, Jamie Morgenstern, Briana Vecchione, Jennifer Wortman Vaughan, Hanna Wallach, Hal Daum ´e III, and Kate Crawford. Datasheets for datasets. arXiv preprint arXiv:1803.09010, 2018

2018 arXiv
[108]

Hendricks, Kaylee Burns, Kate Saenko, Trevor Darrell, and Anna Rohrbach

Lisa A. Hendricks, Kaylee Burns, Kate Saenko, Trevor Darrell, and Anna Rohrbach. Women also snowboard: Overcoming bias in captioning models. In Proceedings of the European Conference on Computer Vision (ECCV), pages 771–787, 2018

2018
[109]

Counterfactual fairness in text-to-image generation

Mohammad Bashir, Qi Zhang, Jinjun Wu, and Ruoming Jin. Counterfactual fairness in text-to-image generation. In Proceedings of the 2021 IEEE/CVF International Conference on Computer Vision Workshops (ICCVW), 2021

2021
[110]

Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav)

Been Kim, Martin Wattenberg, Justin Gilmer, Carrie Cai, James Wexler, and Fernanda Viegas. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In Proceedings of the International Conference on Machine Learning, pages 2668–
[111]

Post-hoc concept bottleneck models

Mert Yuksekgonul, Maggie Wang, and James Zou. Post-hoc concept bottleneck models. arXiv preprint arXiv:2205.15480, 2022

2022 arXiv
[112]

Datasheets for datasets help stakeholders understand social impacts

Morgan Klaus Scheuerman, Alex Hanna, and Emily L Denton. Datasheets for datasets help stakeholders understand social impacts. Communications of the ACM, 64(12):62–71, 2021

2021
[113]

Tommy J. Curry. Killing boogeymen: Phallicism and the misandric mischaracterizations of black males in theory. Res Philosophica, 2018

2018
[114]

Correcting underrepresentation and intersec- tional bias for classification

Emily Diana and Alexander Williams Tolbert. Correcting underrepresentation and intersec- tional bias for classification. arXiv preprint arXiv:2306.11112, 2023

2023 arXiv
[115]

Fairness aware counterfactuals for subgroups

Loukas Kavouras, Konstantinos Tsopelas, Giorgos Giannopoulos, Dimitris Sacharidis, Eleni Psaroudaki, Nikolaos Theologitis, Dimitrios Rontogiannis, Dimitris Fotakis, and Ioannis Emiris. Fairness aware counterfactuals for subgroups. Advances in Neural Information Processing Syst...

2023
[116]

Preventing fairness gerry- mandering: Auditing and learning for subgroup fairness

Michael Kearns, Seth Neel, Aaron Roth, and Zhiwei Steven Wu. Preventing fairness gerry- mandering: Auditing and learning for subgroup fairness. In Proceedings of the International Conference on Machine Learning, pages 2564–2572. PMLR, 2018

2018
[117]

Multicalibration: Calibration for the (Computationally-identifiable) masses

Ursula Hebert-Johnson, Michael Kim, Omer Reingold, and Guy Rothblum. Multicalibration: Calibration for the (Computationally-identifiable) masses. In Jennifer Dy and Andreas Krause, editors, Proceedings of the International Conference on Machine Learning , volume 80 of Proceedi...

1939
[118]

Mini- max group fairness: Algorithms and experiments

Emily Diana, Wesley Gill, Michael Kearns, Krishnaram Kenthapadi, and Aaron Roth. Mini- max group fairness: Algorithms and experiments. In Proceedings of the 2021 AAAI/ACM Conference on AI, Ethics, and Society, pages 66–76, 2021

2021
[119]

Foulds, Rashidul Islam, Kamrun Naher Keya, and Shimei Pan

James R. Foulds, Rashidul Islam, Kamrun Naher Keya, and Shimei Pan. An intersectional definition of fairness. In 2020 IEEE 36th International Conference on Data Engineering (ICDE), pages 1918–1921. IEEE, 2020

2020
[120]

Characterizing intersectional group fairness with worst-case comparisons

Avijit Ghosh, Lea Genuit, and Mary Reagan. Characterizing intersectional group fairness with worst-case comparisons. In Artificial Intelligence Diversity, Belonging, Equity, and Inclusion, pages 22–34. PMLR, 2021

2021
[121]

Lalor, Yi Yang, Kendall Smith, Nicole Forsgren, and Ahmed Abbasi

John P. Lalor, Yi Yang, Kendall Smith, Nicole Forsgren, and Ahmed Abbasi. Benchmarking intersectional biases in nlp. In Proceedings of the conference of the North American chapter of the Association for Computational Linguistics: Human language technologies , pages 3598–3609, 2022

2022
[122]

Lassen, Mina Almasi, Kenneth Enevoldsen, and Ross Deans Kristensen- McLachlan

Ida Marie S. Lassen, Mina Almasi, Kenneth Enevoldsen, and Ross Deans Kristensen- McLachlan. Detecting intersectionality in ner models: A data-driven approach. In Proceed- ings of the 7th joint SIGHUM Workshop on Computational Linguistics for Cultural Heritage, Social Sciences,...

2023
[123]

Detecting emergent intersectional biases: Contextualized word embeddings contain a distribution of human-like biases

Wei Guo and Aylin Caliskan. Detecting emergent intersectional biases: Contextualized word embeddings contain a distribution of human-like biases. In Proceedings of the 2021 AAAI/ACM Conference on AI, Ethics, and Society, pages 122–133, 2021

2021
[124]

Assessing social and intersectional biases in contextualized word representations

Yi Chern Tan and L Elisa Celis. Assessing social and intersectional biases in contextualized word representations. Advances in Neural Information Processing Systems, 32, 2019

2019
[125]

Dreyer, Aleksandar Shtedritski, and Yuki M

Hannah Kirk, Yennie Jun, Haider Iqbal, Elias Benussi, Filippo V olpin, Frederic A. Dreyer, Aleksandar Shtedritski, and Yuki M. Asano. Bias Out-of-the-Box: An Empirical Analysis of Intersectional Occupational Biases in Popular Generative Language Models, October 2021. URL http:...

2021 arXiv
[126]

Intersectional stereotypes in large language models: Dataset and analysis

Weicheng Ma, Brian Chiang, Tong Wu, Lili Wang, and Soroush V osoughi. Intersectional stereotypes in large language models: Dataset and analysis. In Findings of the Association for Computational Linguistics: EMNLP 2023, pages 8589–8597, 2023. 159

2023
[127]

We don’t talk about that: case studies on intersectional analysis of social bias in large language models

Hannah Devinney, Jenny Bj¨orklund, and Henrik Bj¨orklund. We don’t talk about that: case studies on intersectional analysis of social bias in large language models. In Workshop on Gender Bias in Natural Language Processing (GeBNLP), Bangkok, Thailand, 16th August, 2024., pages...

2024
[128]

Griffiths

Xuechunzi Bai, Angelina Wang, Ilia Sucholutsky, and Thomas L. Griffiths. Explicitly unbiased large language models still form biased associations. Proceedings of the National Academy of Sciences, 122(8):e2416228122, 2025

2025
[129]

Socialcounterfactuals: Probing and mitigating intersectional social biases in vision-language models with counterfactual examples

Phillip Howard, Avinash Madasu, Tiep Le, Gustavo Lujan Moreno, Anahita Bhiwandiwalla, and Vasudev Lal. Socialcounterfactuals: Probing and mitigating intersectional social biases in vision-language models with counterfactual examples. In Proceedings of the IEEE/CVF Conference o...

2024
[130]

Racial and intersectional debiasing of contrastive language image pretraining

Elizabeth Hoepfinger. Racial and intersectional debiasing of contrastive language image pretraining. Master’s thesis, University of Georgia, 2023

2023
[131]

Image representations learned with unsupervised pretraining contain human-like biases

Ryan Steed and Aylin Caliskan. Image representations learned with unsupervised pretraining contain human-like biases. In Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency, pages 701–713, 2021

2021
[132]

”why should i trust you?”: Explaining the predictions of any classifier

Marco Tulio Ribeiro, Sameer Singh, and Carlos Guestrin. ”why should i trust you?”: Explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2016

2016
[133]

A unified approach to interpreting model predictions

Scott M Lundberg and Su-In Lee. A unified approach to interpreting model predictions. In Advances in Neural Information Processing Systems, 2017

2017
[134]

Axiomatic attribution for deep networks

Mukund Sundararajan, Ankur Taly, and Qiqi Yan. Axiomatic attribution for deep networks. In Proceedings of the International conference on machine Learning, pages 3319–3328. PMLR, 2017

2017
[135]

Learning important features through propagating activation differences

Avanti Shrikumar, Peyton Greenside, and Anshul Kundaje. Learning important features through propagating activation differences. In Proceedings of the International Conference on Machine Learning, 2017

2017
[136]

On pixel-wise explanations for non-linear classifier decisions by layer-wise relevance propagation

Sebastian Bach, Alexander Binder, Gr´egoire Montavon, Frederick Klauschen, Klaus-Robert M¨uller, and Wojciech Samek. On pixel-wise explanations for non-linear classifier decisions by layer-wise relevance propagation. PloS one, 2015

2015
[137]

Smooth- grad: removing noise by adding noise

Daniel Smilkov, Nikhil Thorat, Been Kim, Fernanda Vi´egas, and Martin Wattenberg. Smooth- grad: removing noise by adding noise. arXiv preprint arXiv:1706.03825, 2017

2017 arXiv
[138]

Towards automatic concept- based explanations

Amirata Ghorbani, James Wexler, James Y Zou, and Been Kim. Towards automatic concept- based explanations. Advances in Neural Information Processing Systems, 32, 2019

2019
[139]

Concept whitening for interpretable image recognition

Chun-Hui Chang, Elliot Creager, Anna Goldenberg, and David Duvenaud. Concept whitening for interpretable image recognition. In Proceedings of the International Conference on Machine Learning, 2020. 160

2020
[140]

Towards robust interpretability with self- explaining neural networks

David Alvarez-Melis and Tommi Jaakkola. Towards robust interpretability with self- explaining neural networks. In Advances in Neural Information Processing Systems, 2018

2018
[141]

This looks like that: deep learning for interpretable image recognition

Chaofan Chen, Oscar Li, Daniel Tao, Alina Barnett, Cynthia Rudin, and Jonathan K Su. This looks like that: deep learning for interpretable image recognition. Advances in Neural Information Processing Systems, 32, 2019

2019
[142]

Concept bottleneck models

Pang Wei Koh, Shiori Sagawa, Hamed Hassani Marklund, Sang Michael Xie, Fanny Zhang, Samy Bengio, and Percy Liang. Concept bottleneck models. In Proceedings of the Interna- tional Conference on Machine Learning, 2020

2020
[143]

Disentangled interventional video explanation (dive)

Ziyan Wu, Yash Goyal, Dhruv Batra, and Stefan Lee. Disentangled interventional video explanation (dive). In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2021

2021
[144]

Learning model-agnostic counter- factual explanations for tabular data

Martin Pawelczyk, Kai Broelemann, and Gjergji Kasneci. Learning model-agnostic counter- factual explanations for tabular data. In AAAI Conference on Artificial Intelligence, 2020

2020
[145]

Countergan: Generating realistic counterfactuals with residual gans

Daniel Nemirovsky, Shalmali Joshi, Jesse Vig, and Yonatan Belinkov. Countergan: Generating realistic counterfactuals with residual gans. In NeurIPS Workshop on Robustness in Sequence Modeling, 2020

2020
[146]

Fido: Feature importance for deep networks via orthogonal projections

Chun-Hui Chang, Xinran Zhang, and David Duvenaud. Fido: Feature importance for deep networks via orthogonal projections. In Proceedings of the International Conference on Learning Representations, 2022

2022
[147]

Explain yourself! leveraging language models for commonsense reasoning

Nazneen Fatema Rajani, Bryan McCann, Caiming Xiong, and Richard Socher. Explain yourself! leveraging language models for commonsense reasoning. In Proceedings of the Annual Meeting of the Association for Computational Linguistics, 2019

2019
[148]

Vqa-x: Visual explanations for visual question answering

Alexander Huk, Dong Huk Park, Lisa Anne Hendricks, Anna Rohrbach, Holger Schwenk, and Trevor Darrell. Vqa-x: Visual explanations for visual question answering. In Proceedings of the European Conference on Computer Vision, 2018

2018
[149]

Multimodal explanations: Justifying decisions and pointing to the evidence

Dong Huk Park, Lisa Anne Hendricks, Zeynep Akata, Holger Schwenk, Trevor Darrell, and Anna Rohrbach. Multimodal explanations: Justifying decisions and pointing to the evidence. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2018

2018
[150]

Can language models learn from explanations in context? arXiv preprint arXiv:2204.02329, 2022

Andrew Lampinen, Ishita Dasgupta, et al. Can language models learn from explanations in context? arXiv preprint arXiv:2204.02329, 2022

2022 arXiv
[151]

Measuring faithfulness in natural language explanations with diagnostic classification

Sarah Wiegreffe and Yuval Pinter. Measuring faithfulness in natural language explanations with diagnostic classification. In Proceedings of the Annual Meeting of the Association for Computational Linguistics, 2021

2021
[152]

Best of both worlds: local and global explanations with human-understandable concepts

Jessica Schrouff, Sebastien Baur, Shaobo Hou, Diana Mincu, Eric Loreaux, Ralph Blanes, James Wexler, Alan Karthikesalingam, and Been Kim. Best of both worlds: local and global explanations with human-understandable concepts. arXiv preprint arXiv:2106.08641, 2021. 161

2021 arXiv
[153]

Model-agnostic interpretability of machine learning

Marco Tulio Ribeiro, Sameer Singh, and Carlos Guestrin. Model-agnostic interpretability of machine learning. arXiv preprint arXiv:1606.05386, 2016

2016 arXiv
[154]

Slic superpixels compared to state-of-the-art superpixel methods

Radhakrishna Achanta, Appu Shaji, Kevin Smith, Aurelien Lucchi, Pascal Fua, and Sabine S¨usstrunk. Slic superpixels compared to state-of-the-art superpixel methods. IEEE transac- tions on pattern analysis and machine intelligence, 34(11):2274–2282, 2012

2012
[155]

Learning not to learn: Training deep neural networks with biased data

Byungju Kim, Hyunwoo Kim, Kyungsu Kim, Sungjin Kim, and Junmo Kim. Learning not to learn: Training deep neural networks with biased data. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 9012–9020, 2019

2019
[156]

Deep learning face attributes in the wild

Ziwei Liu, Ping Luo, Xiaogang Wang, and Xiaoou Tang. Deep learning face attributes in the wild. In Proceedings of the IEEE/CVF International Conference on Computer Vision, December 2015

2015
[157]

Underdiagnosis bias of artificial intelligence algorithms applied to chest radiographs in under-served patient populations

Laleh Seyyed K., Haoran Zhang, Matthew BA McDermott, Irene Y Chen, and Marzyeh Ghas- semi. Underdiagnosis bias of artificial intelligence algorithms applied to chest radiographs in under-served patient populations. Nature medicine, 27(12):2176–2182, 2021

2021
[158]

Ramaswamy, Ruth Fong, and Olga Russakovsky

Nicole Meister, Dora Zhao, Angelina Wang, Vikram V . Ramaswamy, Ruth Fong, and Olga Russakovsky. Gender artifacts in visual datasets. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 4837–4848, 2023

2023
[159]

Revise: A tool for measuring and mitigating bias in visual datasets

Angelina Wang, Alexander Liu, Ryan Zhang, Anat Kleiman, Leslie Kim, Dora Zhao, Iroha Shirai, Arvind Narayanan, and Olga Russakovsky. Revise: A tool for measuring and mitigating bias in visual datasets. International Journal of Computer Vision, 130(7):1790–1810, 2022

2022
[160]

Fair loss: Margin-aware reinforcement learning for deep face recognition

Bingyu Liu, Weihong Deng, Yaoyao Zhong, Mei Wang, Jiani Hu, Xunqiang Tao, and Yaohai Huang. Fair loss: Margin-aware reinforcement learning for deep face recognition. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 10052– 10061, 2019

2019
[161]

Joshi, Xavier Suau Cuadros, Nivedha Sivakumar, Luca Zappella, and Nicholas Apostoloff

Aparna R. Joshi, Xavier Suau Cuadros, Nivedha Sivakumar, Luca Zappella, and Nicholas Apostoloff. Fair sa: Sensitivity analysis for fairness in face recognition. In Algorithmic Fairness Through the Lens of Causality and Robustness Workshop , pages 40–58. PMLR, 2022

2022
[162]

Overwriting pretrained bias with finetuning data

Angelina Wang and Olga Russakovsky. Overwriting pretrained bias with finetuning data. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 3957– 3968, 2023

2023
[163]

Analyzing and improving the image quality of stylegan

Tero Karras, Samuli Laine, Miika Aittala, Janne Hellsten, Jaakko Lehtinen, and Timo Aila. Analyzing and improving the image quality of stylegan. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 8110–8119, 2020

2020
[164]

Maudslay, Hila Gonen, Ryan Cotterell, and Simone Teufel

Rowan H. Maudslay, Hila Gonen, Ryan Cotterell, and Simone Teufel. It’s all in the name: Mitigating gender bias with name-based counterfactual data substitution. In Kentaro Inui, Jing Jiang, Vincent Ng, and Xiaojun Wan, editors,Proceedings of the Conference on Empirical 162 Met...

2019
[165]

Adversarial examples in physical world

Jiakai Wang. Adversarial examples in physical world. In IJCAI, pages 4925–4926, 2021

2021
[166]

Intriguing properties of neural networks

Christian Szegedy, Wojciech Zaremba, Ilya Sutskever, Joan Bruna, Dumitru Erhan, Ian Goodfellow, and Rob Fergus. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199, 2013

2013 arXiv
[167]

Geoda: a geometric framework for black-box adversarial attacks

Ali Rahmati, Seyed-Mohsen Moosavi-Dezfooli, Pascal Frossard, and Huaiyu Dai. Geoda: a geometric framework for black-box adversarial attacks. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern recognition, pages 8446–8455, 2020

2020
[168]

Black-box adversarial attacks on video recognition models

Linxi Jiang, Xingjun Ma, Shaoxiang Chen, James Bailey, and Yu-Gang Jiang. Black-box adversarial attacks on video recognition models. InProceedings of the 27th ACM International Conference on Multimedia, pages 864–872, 2019

2019
[169]

Simple black-box adversarial attacks on deep neural networks

Nina Narodytska and Shiva Kasiviswanathan. Simple black-box adversarial attacks on deep neural networks. In Proceedings of the IEEE/CVF Conference Computer Vision and Pattern Recognition Workshops (CVPRW), pages 1310–1318, 2017. doi: 10.1109/CVPRW.2017.172

2017 doi
[170]

Explaining and harnessing adver- sarial examples

Ian Goodfellow, Jonathon Shlens, and Christian Szegedy. Explaining and harnessing adver- sarial examples. arXiv preprint arXiv:1412.6572, 2014

2014 arXiv
[171]

Towards deep learning models resistant to adversarial attacks

Aleksander Madry, Aleksandar Makelov, Ludwig Schmidt, Dimitris Tsipras, and Adrian Vladu. Towards deep learning models resistant to adversarial attacks. arXiv preprint arXiv:1706.06083, 2017

2017 arXiv
[172]

Exploring counterfactual explanations through the lens of adversarial examples: A theoretical and empirical analysis

Martin Pawelczyk, Chirag Agarwal, Shalmali Joshi, Sohini Upadhyay, and Himabindu Lakkaraju. Exploring counterfactual explanations through the lens of adversarial examples: A theoretical and empirical analysis. In International Conference on Artificial Intelligence and Statisti...

2022
[173]

Interpretable counterfactual explanations guided by prototypes

Arnaud Van Looveren and Janis Klaise. Interpretable counterfactual explanations guided by prototypes. In Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pages 650–665. Springer, 2021

2021
[174]

Alex Beutel, Jilin Chen, Zhe Zhao, and Ed H. Chi. Data decisions and theoretical implications when adversarially learning fair representations. arXiv preprint arXiv:1707.00075, 2017

2017 arXiv
[175]

Semanticadv: Generating adversarial examples via attribute-conditioned image editing

Haonan Qiu, Chaowei Xiao, Lei Yang, Xinchen Yan, Honglak Lee, and Bo Li. Semanticadv: Generating adversarial examples via attribute-conditioned image editing. In Proceedings of the European Conference on Computer Vision, pages 19–37. Springer, 2020

2020
[176]

Biasadv: Bias-adversarial augmentation for model debiasing

Jongin Lim, Youngdong Kim, Byungjai Kim, Chanho Ahn, Jinwoo Shin, Eunho Yang, and Seungju Han. Biasadv: Bias-adversarial augmentation for model debiasing. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 3832–3841, 2023. 163

2023
[177]

Score-cam: Score-weighted visual explanations for convolutional neural networks

Haofan Wang, Zifan Wang, Mengnan Du, Fan Yang, Zijian Zhang, Sirui Ding, Piotr Mardziel, and Xia Hu. Score-cam: Score-weighted visual explanations for convolutional neural networks. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops,...

2020
[178]

Curriculum learning

Yoshua Bengio, J´erˆome Louradour, Ronan Collobert, and Jason Weston. Curriculum learning. In Proceedings of the International Conference on Machine Learning, pages 41–48, 2009

2009
[179]

Self-paced curriculum learning

Lu Jiang, Deyu Meng, Qian Zhao, Shiguang Shan, and Alexander Hauptmann. Self-paced curriculum learning. In Proceedings of the AAAI Conference on Artificial Intelligence , volume 29, 2015

2015
[180]

Teacher–student curriculum learning

Tambet Matiisen, Avital Oliver, Taco Cohen, and John Schulman. Teacher–student curriculum learning. IEEE Transactions on Neural networks and learning systems, 31(9):3732–3740, 2019

2019
[181]

Adaptive curriculum learning

Yajing Kong, Liu Liu, Jun Wang, and Dacheng Tao. Adaptive curriculum learning. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 5067– 5076, 2021

2021
[182]

Bellemare, Jacob Menick, Remi Munos, and Koray Kavukcuoglu

Alex Graves, Marc G. Bellemare, Jacob Menick, Remi Munos, and Koray Kavukcuoglu. Automated curriculum learning for neural networks. In Proceedings of the International Conference on Machine Learning, pages 1311–1320. Pmlr, 2017

2017
[183]

Assessing multilingual fairness in pre-trained multi- modal representations

Jialu Wang, Yang Liu, and Xin Wang. Assessing multilingual fairness in pre-trained multi- modal representations. In Findings of the Association for Computational Linguistics: ACL 2022, pages 2681–2695, 2022

2022
[184]

Age progression/regression by conditional adversarial autoencoder

Zhifei Zhang, Yang Song, and Hairong Qi. Age progression/regression by conditional adversarial autoencoder. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. IEEE, 2017

2017
[185]

Deep residual learning for image recognition

Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. Deep residual learning for image recognition. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 770–778, 2016

2016
[186]

Kingma and Jimmy Ba

Diederik P. Kingma and Jimmy Ba. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980, 2014

2014 arXiv
[187]

Densely connected convolutional networks

Gao Huang, Zhuang Liu, Laurens Van Der Maaten, and Kilian Q Weinberger. Densely connected convolutional networks. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 4700–4708, 2017

2017
[188]

Stable bias: Evaluating societal representations in diffusion models

Sasha Luccioni, Christopher Akiki, Margaret Mitchell, and Yacine Jernite. Stable bias: Evaluating societal representations in diffusion models. In Advances in Neural Information Processing Systems Datasets and Benchmarks Track, 2023. 164

2023
[189]

Balasubramanian

Gaurav Bhatt, Deepayan Das, Leonid Sigal, and Vineeth N. Balasubramanian. Mitigating the effect of incidental correlations on part-based learning. Advances in Neural Information Processing Systems, 36, 2024

2024
[190]

Iti-gen: Inclusive text-to-image generation

Cheng Zhang, Xuanbai Chen, Siqi Chai, Chen Henry Wu, Dmitry Lagun, Thabo Beeler, and Fernando De la Torre. Iti-gen: Inclusive text-to-image generation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 3969–3980, 2023

2023
[191]

Social biases in nlp models as barriers for persons with disabilities

Ben Hutchinson, Vinodkumar Prabhakaran, Emily Denton, Kellie Webster, Yu Zhong, and Stephen Denuyl. Social biases in nlp models as barriers for persons with disabilities. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pages 5491–5501, 2020

2020
[192]

Andrew Schwartz, and Dirk Hovy

Deven Santosh Shah, H. Andrew Schwartz, and Dirk Hovy. Predictive biases in natural language processing models: A conceptual framework and overview. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics , pages 5248–5264, 2020

2020
[193]

A survey on bias in deep nlp

Ismael Garrido M., Arturo Montejo R., Fernando Mart´ınez-S., and L Alfonso Ure˜na-L´opez. A survey on bias in deep nlp. Applied Sciences, 11(7):3184, 2021

2021
[194]

Mitigating language-dependent ethnic bias in bert

Jaimeen Ahn and Alice Oh. Mitigating language-dependent ethnic bias in bert. InProceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, pages 533– 549, 2021

2021
[195]

Identifying implicit social biases in vision-language models

Kimia Hamidieh, Haoran Zhang, Thomas Hartvigsen, and Marzyeh Ghassemi. Identifying implicit social biases in vision-language models. 2023

2023
[196]

The bias amplification paradox in text-to- image generation

Preethi Seshadri, Sameer Singh, and Yanai Elazar. The bias amplification paradox in text-to- image generation. arXiv preprint arXiv:2308.00755, 2023

2023 arXiv
[197]

Scaling fair learning to hundreds of intersectional groups

Eric Zhao, De-An Huang, Hao Liu, Zhiding Yu, Anqi Liu, Olga Russakovsky, and Anima Anandkumar. Scaling fair learning to hundreds of intersectional groups. 2021

2021
[198]

Fair-vqa: Fairness- aware visual question answering through sensitive attribute prediction

Sungho Park, Sunhee Hwang, Jongkwang Hong, and Hyeran Byun. Fair-vqa: Fairness- aware visual question answering through sensitive attribute prediction. IEEE Access, 8: 215091–215099, 2020

2020
[199]

Fairness in ai systems: Mitigating gender bias from language-vision models

Lavisha Aggarwal and Shruti Bhargava. Fairness in ai systems: Mitigating gender bias from language-vision models. arXiv preprint arXiv:2305.01888, 2023

2023 arXiv
[200]

Gender and racial bias in visual question answering datasets

Yusuke Hirota, Yuta Nakashima, and Noa Garcia. Gender and racial bias in visual question answering datasets. In Proceedings of the 2022 ACM Conference on Fairness, Accountability, and Transparency, pages 1280–1292, 2022

2022
[201]

Chain-of-thought prompting elicits reasoning in large language models

Jason Wei, Xuezhi Wang, Dale Schuurmans, Maarten Bosma, Fei Xia, Ed Chi, Quoc V Le, Denny Zhou, et al. Chain-of-thought prompting elicits reasoning in large language models. Advances in Neural Information Processing Systems, 35:24824–24837, 2022. 165

2022
[202]

Automatic chain of thought prompting in large language models

Zhuosheng Zhang, Aston Zhang, Mu Li, and Alex Smola. Automatic chain of thought prompting in large language models. In Proceedings of the International Conference on Learning Representations, 2023

2023
[203]

Language models are few-shot learners

Tom Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared D Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, et al. Language models are few-shot learners. Advances in Neural Information Processing Systems , 33: 1877–1901, 2020

1901
[204]

Minigpt-v2: Large language model as a unified interface for vision-language multi-task learning

Jun Chen, Deyao Zhu, Xiaoqian Shen, Xiang Li, Zechun Liu, Pengchuan Zhang, Raghuraman Krishnamoorthi, Vikas Chandra, Yunyang Xiong, and Mohamed Elhoseiny. Minigpt-v2: Large language model as a unified interface for vision-language multi-task learning. arXiv preprint arXiv:2310...

2023 arXiv
[205]

Learning transfer- able visual models from natural language supervision

Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, et al. Learning transfer- able visual models from natural language supervision. In Proceedings of the International Conference on...

2021
[206]

Wang, Evan Montoya, David Munechika, Haoyang Yang, Benjamin Hoover, and Duen Horng Chau

Zijie J. Wang, Evan Montoya, David Munechika, Haoyang Yang, Benjamin Hoover, and Duen Horng Chau. DiffusionDB: A large-scale prompt gallery dataset for text-to-image generative models. arXiv:2210.14896 [cs], 2022. URL https://arxiv.org/abs/ 2210.14896

2022 arXiv
[207]

Divide, evaluate, and refine: Evaluating and improving text-to-image alignment with iterative vqa feedback

Jaskirat Singh and Liang Zheng. Divide, evaluate, and refine: Evaluating and improving text-to-image alignment with iterative vqa feedback. In Advances in Neural Information Processing Systems, 2023

2023
[208]

Tifa: Accurate and interpretable text-to-image faithfulness evaluation with question answering

Yushi Hu, Benlin Liu, Jungo Kasai, Yizhong Wang, Mari Ostendorf, Ranjay Krishna, and Noah A Smith. Tifa: Accurate and interpretable text-to-image faithfulness evaluation with question answering. arXiv preprint arXiv:2303.11897, 2023

2023 arXiv
[209]

Mapping the margins: Intersectionality, identity politics, and violence against women of color

Kimberle Crenshaw. Mapping the margins: Intersectionality, identity politics, and violence against women of color. In Stanford Law Review, volume 43, pages 1241–1299. 1991

1991
[210]

Zero-shot text-to-image generation

Aditya Ramesh, Mikhail Pavlov, Gabriel Goh, Scott Gray, Chelsea V oss, Alec Radford, Mark Chen, and Ilya Sutskever. Zero-shot text-to-image generation. arXiv preprint arXiv:2102.12092, 2021

2021 arXiv
[211]

Photorealistic text-to-image diffusion models with deep language understanding

Chitwan Saharia, William Chan, Saurabh Saxena, Lala Li, Jay Whang, Emily Denton, Seyed Kamyar Seyed Ghasemipour, Burcu Karagol Ayan, S Sara Mahdavi, Raphael Gontijo Lopes, et al. Photorealistic text-to-image diffusion models with deep language understanding. arXiv preprint arX...

2022 arXiv
[212]

Ramaswamy, and Olga Russakovsky

Angelina Wang, Vikram V . Ramaswamy, and Olga Russakovsky. Towards Intersectionality in Machine Learning: Including More Identities, Handling Underrepresentation, and Performing Evaluation. In Proceedings of the 2022 ACM Conference on Fairness, Accountability, and Transparency...

2022
[213]

BlackForestLabs. Flux. https://github.com/black-forest-labs/flux, 2024

2024
[214]

Openbias: Open-set bias detection in text-to-image generative models

Moreno D’Inc`a, Elia Peruzzo, Massimiliano Mancini, Dejia Xu, Vidit Goel, Xingqian Xu, Zhangyang Wang, Humphrey Shi, and Nicu Sebe. Openbias: Open-set bias detection in text-to-image generative models. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Re...

2024
[215]

Play- ground v2.5: Three insights towards enhancing aesthetic quality in text-to-image generation, 2024

Daiqing Li, Aleks Kamko, Ehsan Akhgari, Ali Sabet, Linmiao Xu, and Suhail Doshi. Play- ground v2.5: Three insights towards enhancing aesthetic quality in text-to-image generation, 2024

2024
[216]

kandinsky 2.2, 2023

Arseniy Shakhmatov, Anton Razzhigaev, Aleksandr Nikolich, Vladimir Arkhipkin, Igor Pavlov, Andrey Kuznetsov, and Denis Dimitrov. kandinsky 2.2, 2023

2023
[217]

Kandinsky: An improved text-to-image synthesis with image prior and latent diffusion

Anton Razzhigaev, Arseniy Shakhmatov, Anastasia Maltseva, Vladimir Arkhipkin, Igor Pavlov, Ilya Ryabov, Angelina Kuts, Alexander Panchenko, Andrey Kuznetsov, and Denis Dimitrov. Kandinsky: An improved text-to-image synthesis with image prior and latent diffusion. In Proceeding...

2023
[218]

Chan, and Chen Change Loy

Jianyi Wang, Kelvin C.K. Chan, and Chen Change Loy. Exploring clip for assessing the look and feel of images. In Proceedings of the AAAI Conference on Artificial Intelligence, volume 37, pages 2555–2563, 2023

2023
[219]

Unified concept editing in diffusion models

Rohit Gandikota, Hadas Orgad, Yonatan Belinkov, Joanna Materzy ´nska, and David Bau. Unified concept editing in diffusion models. In Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pages 5111–5120, 2024

2024
[220]

AI now report 2018

Meredith Whittaker, Kate Crawford, Roel Dobbe, Genevieve Fried, Elizabeth Kaziunas, Varoon Mathur, Sarah Mysers West, Rashida Richardson, Jason Schultz, Oscar Schwartz, et al. AI now report 2018. AI Now Institute at New York University New York, 2018

2018
[221]

Balasubramanian

Gaurav Bhatt, Deepayan Das, Leonid Sigal, and Vineeth N. Balasubramanian. Mitigating the effect of incidental correlations on part-based learning. arXiv preprint, 2023

2023
[222]

Challenges of big data analysis

Jianqing Fan, Fang Han, and Han Liu. Challenges of big data analysis. National Science Review, 1(2):293–314, 2014. 167 APPENDIX A Definitions of Biases Our aim is to quantify and establish a framework for analyzing biases in generative Text-to-Image (TTI) models. While these b...

2014
[223]

All answers are combined into a single string

First, we use VQA with MiniGPT-v2 to obtain answers for each image. All answers are combined into a single string
[224]

This string undergoes processing to remove punctuation and stop words
[225]

FreqDist.html) to obtain a list of words and their corresponding word frequencies

We use the FreqDict function in NLTK (https://www.nltk.org/api/nltk.probability. FreqDist.html) to obtain a list of words and their corresponding word frequencies. We normalize this frequency by the number of images in the set (in our setting, 48). This represents a set of con...
[226]

Axes-related

Societal Bias Related to ”Axes”:Is there evidence of "Axes-related" societal bias in the prompt?
[227]

Axes-related

Incidental Bias Related to ”Axes”:Is there evidence of "Axes-related" incidental bias in the prompt? where ”Axes” is replaced with the actual bias axis name obtained using the LLM. This approach ensures that users are equipped to identify and assess these specific biases in th...

Pith tools

Reviewed August 5, 2026 · model on record in the stance chip above.

[1] [1]

Moss-Racusin, John F

Corinne A. Moss-Racusin, John F. Dovidio, Victoria L. Brescoll, Mark J. Graham, and Jo Handelsman. Science faculty’s subtle gender biases favor male students. Proceedings of the National Academy of Sciences, 109(41):16474–16479, 2012

2012

[2] [2]

Counterfactuals

David Lewis. Counterfactuals. Harvard University Press, 1973

1973

[3] [3]

High-resolution image synthesis with latent diffusion models

Robin Rombach, Andreas Blattmann, Dominik Lorenz, Patrick Esser, and Bj ¨orn Ommer. High-resolution image synthesis with latent diffusion models. InProceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 10684–10695, 2022

2022

[4] [4]

Towards accuracy-fairness paradox: Adversarial example-based data augmentation for visual debiasing

Yi Zhang and Jitao Sang. Towards accuracy-fairness paradox: Adversarial example-based data augmentation for visual debiasing. In Proceedings of the 28th ACM International Conference on Multimedia, pages 4346–4354, 2020

2020

[5] [5]

Ramaswamy, Sunnie SY

Vikram V . Ramaswamy, Sunnie SY . Kim, and Olga Russakovsky. Fair attribute classification through latent space de-biasing. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 9301–9310, 2021

2021

[6] [6]

Image Counterfactual Sensitivity Analysis for Detecting Unintended Bias

Emily Denton, Ben Hutchinson, Margaret Mitchell, Timnit Gebru, and Andrew Zaldivar. Image counterfactual sensitivity analysis for detecting unintended bias. arXiv preprint arXiv:1906.06439, 2019

work page Pith review arXiv 1906

[7] [7]

Towards fairness in visual recognition: Effective strategies for bias mitigation

Zeyu Wang, Klint Qinami, Ioannis Christos Karakozis, Kyle Genova, Prem Nair, Kenji Hata, and Olga Russakovsky. Towards fairness in visual recognition: Effective strategies for bias mitigation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 8919–8928, 2020

2020

[8] [8]

Balasubramanian, and Amit Sharma

Saloni Dash, Vineeth N. Balasubramanian, and Amit Sharma. Evaluating and mitigating bias in image classifiers: A causal perspective using counterfactuals. In Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pages 915–924, 2022

2022