Pith. sign in

REVIEW 2 major objections 5 minor 227 references

Optimisation Strategies for Ensuring Fairness in Machine Learning: With and Without Demographics

T0 review · 2 major / 5 minor · reviewed 2026-08-12 · deepseek-v4-flash

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

Pith's one-line read This paper claims that a single group-blind transport map, computed from aggregate group feature distributions alone, can repair demographic bias in a dataset, and proves convergence of an algorithm that finds such a map.

desk verdict The genuinely new idea is Chapter 5's group-blind OT constraint, but the central without-demographics claim is untested because every experiment computes V from source data using the sensitive attribute. read the letter →

arxiv 2411.09056 v1 pith:MZB4DPVW submitted 2024-11-13 cs.LG

Quan Zhou This is my paper · ORCID
classification cs.LG MSC 49Q2290C22
keywords fairnessinmachinelearningdemographicparitybiasrepairoptimaltransportgroup-blindprojectionmissingsensitiveattributesfairforecastingnon-commutativepolynomialoptimization
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

The paper's central claim is that demographic bias in a dataset can be repaired without ever observing each individual's sensitive attribute, by projecting all feature vectors through one shared map. The map is derived from an optimal-transport coupling between the overall source feature distribution and a chosen target distribution, constrained so that after projection the feature distributions of the two groups coincide (total repair) or approach each other within a tunable bound (partial repair). The key identity is a vector $V$ that records how the two groups' conditional feature probabilities differ relative to the overall feature probability; the repair condition is $\gamma' V = 0$, and a relaxed condition bounds the total-variation distance between the projected group distributions. The thesis also presents a separate framework for fairness in time-series forecasting under imbalanced data, based on non-commutative polynomial optimisation.

What carries the argument

The central object is the coupling $\gamma$: an optimal-transport plan that moves probability mass from source feature values to target feature values. It is constrained by the vector $V = (P^{Xs_0} - P^{Xs_1})/P^X$, which summarises, for each feature value, how far the two groups' conditional probabilities are apart relative to the overall feature probability. Theorem 5.1 shows total repair is equivalent to $\gamma' V = 0$; partial repair replaces this by $-\Lambda \le \gamma' V \le \Lambda$, which bounds the total-variation distance between the projected group distributions by $\|\Lambda\|_1/2$. The feasible set is the intersection of three convex sets, and Algorithm 1 uses Dykstra's algorithm with KL projections, with closed-form projections for the marginal constraints and a root-finding step for the $V$ constraint, converging to the unique coupling. Projecting source samples through the induced group-blind map then produces the repaired data.

What would settle it

Run Algorithm 1 on a dataset where $V$ comes from independent population-level group statistics and the sensitive attribute is never used during repair; if the projected group-wise feature distributions have TV distance above the promised $\|\Lambda\|_1/2$ bound, or if downstream disparate impact does not move toward 1, the central claim is refuted. A direct version on the Adult Census Income data would compare the achieved TV distance and downstream disparity between using $V$ computed from the source data and $V$ computed from a held-out population sample.

Watch

Extended reading notes

Core claim

The paper introduces a bias-repair framework for transfer learning and domain adaptation in which one group-blind projection map $T$ modifies the feature values of all source samples, so that after projection the feature distributions of the two groups, defined by a binary sensitive attribute, become equal (total repair) or approximately equal with a tunable bound (partial repair). The mathematical engine is Theorem 5.1: total repair is equivalent to the coupling $\gamma$ satisfying $\gamma' V = 0$, where $V = (P^{Xs_0} - P^{Xs_1})/P^X$. Lemma 5.7 and Algorithm 1 give a convergent procedure, based on Dykstra's algorithm with KL projections, that solves the entropy-regularised optimal transport problem subject to this constraint, using only $V$ and the marginals $P^X$, $P^{\tilde X}$; no individual sensitive attribute values are used to compute the coupling or to apply the projection. On the Adult Census Income dataset, the paper reports that the repaired test sets move disparate impact toward 1 and reduce the S-wise total-variation distance with little loss in f1 accuracy, whereas a barycentre baseline that does use individual demographics loses more prediction performance.

Load-bearing premise

The load-bearing premise is that the aggregate vector $V$, built from the two groups' feature distributions in a broader population, is available without using individual sensitive attributes and that the source data are an unbiased sample of that population; the paper's experiments compute $V$ from the source data using the sensitive attribute, so the no-demographics scenario is not tested end to end.

Editorial extensions

If this is right

  • If the vector $V$ is available from population-level group distributions, bias repair for demographic parity can be applied without storing or processing individual sensitive attributes, which matters where collecting such attributes is restricted or illegal.
  • The $\Lambda$-relaxation gives a proven bound: after partial repair with parameter $\Lambda$, the total-variation distance between the projected feature distributions of the two groups is at most $\|\Lambda\|_1/2$, so the remaining disparity is controlled by a single tuning vector.
  • On the Adult Census Income dataset, the paper reports that its partial-repair schemes move disparate impact closer to 1 and reduce S-wise total-variation distance while preserving most f1 accuracy, whereas a barycentre baseline that uses individual demographics shows a larger accuracy drop.
  • The fair-forecasting framework in the first part of the thesis defines subgroup fairness and instantaneous fairness for time series, and the paper reports that solving the resulting min-max problems globally improves independence and separation indices relative to the original COMPAS scores.

Reading between the lines

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

  • A natural next experiment the paper does not run is to supply $V$ from an independent source, such as census marginals, and measure whether demographic parity transfers to a separately collected source dataset; the current experiments compute $V$ from the source data itself.
  • Because the method reduces the no-demographics problem to estimating one aggregate vector $V$, its practical value hinges on how accurately $V$ can be estimated under sampling noise; perturbing $V$ and recording the resulting TV distance would quantify that sensitivity.
  • The framework could be extended to multiple sensitive attribute classes or to soft penalties replacing the hard constraint $\gamma' V = 0$; the paper names the multi-class extension as future work, and a soft-penalty variant would make the fairness-distortion trade-off continuous.
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.

Referee Report

2 major / 5 minor

Summary. The thesis-style manuscript develops two optimisation frameworks for ML fairness. The first (Chapters 3–4) casts learning of linear dynamical systems as a non-commutative polynomial optimisation problem, provides convergence guarantees, and demonstrates fairness-aware forecasting on synthetic data and COMPAS. The second (Chapter 5) proposes a group-blind optimal-transport bias-repair framework. The central claim is that a coupling satisfying γ′V = 0, with V = (P^{Xs0} − P^{Xs1})/P^X, can be computed without individual sensitive-attribute values, and Algorithm 1 converges to such a coupling. The authors prove the total-repair condition in Theorem 5.1, provide KL-projection lemmas, and report experiments on synthetic data and Adult Census Income. The main weakness is that all experiments compute V directly from source data using the sensitive attribute, so the without-demographics claim is not validated in the exact scenario the framework is designed for.

Significance. If the Chapter 5 claim holds, it is a meaningful step: a provable demographic-parity repair method whose coupling computation and projection step do not require individual sensitive attributes, with convergence supported by Dykstra’s algorithm and Bregman projections. The manuscript provides proofs in Appendix E, reproducible code links, and clearly states limitations, including the binary-attribute restriction and the need for population-level group distributions. The total-repair guarantee is a derivation rather than a fitted prediction, and I agree with the reader’s assessment that it is not circular. The main open risk is empirical: the paper never tests the scenario where V is obtained externally, nor does it bound the effect of noisy or misspecified V. Since that scenario is the distinguishing contribution of Chapter 5, the experimental gap is load-bearing rather than cosmetic.

major comments (2)
  1. [Section 5.4.1, Section 6.2] The without-demographics claim is not validated as stated. Section 5.4.1 explicitly says that, because no population-level information is given, V is computed directly from the source data. Computing V = (P^{Xs0} − P^{Xs1})/P^X requires the sensitive attribute S for every sample, since P^{Xs0} and P^{Xs1} are conditional distributions. Therefore all reported experiments use S to construct the key input, and S is only omitted in the subsequent coupling and projection steps. Section 6.2 confirms this gap by listing as future work 'evaluating the effects when only distributions with noise are provided.' The paper should either test the framework with V obtained from an independent population source, or clearly restrict the claim and provide an experiment with noisy or partial V. This is essential because the entire contribution is defined by not requiring demographics.
  2. [Theorem 5.1, Eq. (5.16), Section 5.4.1] There is no perturbation analysis for the key quantity V. The parity guarantee γ′V = 0 is exact and relies on V coinciding with the true population-level (P^{Xs0} − P^{Xs1})/P^X. In the intended deployment scenario, V must come from a census, sandbox, or similar external source, and that source may have measurement error or may reflect a different subpopulation than the source data. The manuscript does not bound how violations of the exact equality propagate to the TV distance between projected group distributions, nor does it test V computed from an independent or noisy source. A robustness bound or a simple sensitivity experiment would materially support the central claim.
minor comments (5)
  1. [Lemma 5.2] The notation is self-referential: 'supp(X) := {i ∈ supp(X) | V_i ≠ 0}' redefines supp(X). Use a different symbol, e.g., supp_V(X) or supp(X) ∩ {V_i ≠ 0}, to avoid confusion.
  2. [Section 5.3.1, Section 5.3.6] Two cross-references are wrong: 'we assume X includes one neutral attribute till Section 5.3.4' should refer to Section 5.3.5 (higher dimensions), and 'The choice of target distribution will be mentioned in Section 3.6' should refer to Section 5.3.6.
  3. [Section 5.4.4, Figure 5.6] The labels '1e−2-repair' and '1e−3-repair' are confusing: the text says Λ = 1e−21 and Λ = 1e−31, respectively, so the figure labels do not match the numeric values. Please use consistent notation, e.g., 10^{-21}-repair and 10^{-31}-repair.
  4. [Section 5.3.4, Algorithm 1] The update rule for q_{k−3} is not motivated or explained. It is presumably the standard Dykstra auxiliary-variable update, but the indexing and the distinction between k = 4,…,7 and later k are non-obvious; a short derivation or reference to the exact Dykstra variant would improve readability.
  5. [General] The manuscript is a PhD thesis and uses thesis-style front matter (declaration, acknowledgements, publication list), while the abstract and framing say 'this paper.' If published as a journal article, the scope and framing should be adjusted accordingly.

Circularity Check

2 steps flagged · score 4.0 of 10

Total-repair constraint is exactly the parity condition; experiments compute V from source data with the sensitive attribute, so the 'without demographics' fairness gains are partly constructed from the input rather than independently predicted.

  1. self definitional [Section 5.3.2, Theorem 5.1, Eqs. (5.16)-(5.17); Section 5.4.1]
    "Theorem 5.1 ... if one wishes to achieve total repair, the coupling should satisfy P˜X0 − P˜X1 = γ′ ( P Xs0 − P Xs1 / P X ) = γ′V = 0, (5.16) ... V := P Xs0 − P Xs1 / P X , (5.17) ... In our experiments, since there is no such population-level information given, we directly compute V from the source data."

    Total repair is defined as equality of the projected group distributions P˜Xs0 = P˜Xs1 (Definition 5.5). Theorem 5.1 rewrites that condition equivalently as γ′V = 0, where V is the normalized difference of the group-conditional source distributions. When V is computed from the source data using the sensitive attribute, as done in every experiment, the constraint γ′V = 0 directly forces the projected group distributions to coincide. The reported equalization is therefore the same statement as the input V, not an independent consequence of group-blindness. The algorithm is group-blind only after V is supplied; the 'without demographics' validation does not test the case where V comes from an independent population source.

  2. fitted input called prediction [Section 5.4.4 (Adult experiments) and Lemma 5.2]
    "From the test set, we compute the empirical marginal distributions of the feature X (i.e., P X, P X0, P X1) and V."

    The paper's headline fairness index, S-wise TV distance after repair, is bounded by ∥γ′V∥1/2 (Lemma 5.2). Since V is computed from the same test set that is then repaired and evaluated, the decrease in S-wise TV distance is a check that Algorithm 1 enforced its own constraint, not a prediction validated on independent demographic-free data. The paper's Section 6.2 lists as future work 'evaluating the effects when only distributions with noise are provided', confirming that the externally-supplied-V scenario remains untested. The experimental demonstration therefore reduces partly to the construction of the input.

full rationale

The formal derivation in Chapter 5 is not circular at the level of the theorems: Lemma 5.1 and Theorem 5.1 correctly derive that total repair is equivalent to γ′V = 0, and Lemma 5.7 establishes convergence of Dykstra's algorithm for the constrained optimal-transport problem. These are internal mathematical results, not fitted predictions, and the coupling computation itself is blind to individual sensitive-attribute values once V is supplied. The circularity burden is in the experimental validation of the 'without demographics' claim. Every experiment obtains V from the same source/test data using the sensitive attribute (Sections 5.4.1 and 5.4.4), so the reported parity gains are enforced by the constraint γ′V = 0, whose input V already encodes the group difference. The S-wise TV distance after repair is, by Lemma 5.2, a direct measure of how well γ′V ≈ 0 was satisfied; measuring it on the same data is a consistency check rather than independent external evidence. The paper itself concedes in Section 6.2 that the case 'when only distributions with noise are provided' is future work, confirming that the externally-supplied-V scenario is not tested. There is no load-bearing self-citation chain and no imported uniqueness theorem, and Chapters 3-4 are benchmarked against external toolboxes rather than against their own inputs. Hence a moderate score of 4: partial circularity of the evaluation and of the 'without demographics' demonstration, while the central theorem retains independent mathematical content.

Assumptions & free parameters 8 free parameters · 7 assumptions · 0 invented entities

The central Chapter 5 claim rests on the availability of the group-difference vector V, the unbiased-sampling assumption, and the binary-sensitive-attribute restriction. The Chapter 3 claim rests on the Archimedean and flatness conditions that are stated but not always guaranteed. No new physical entities are introduced.

free parameters (8)
  • lambda1, lambda2 (noise regularizers) = lambda1 = 1,3,5; lambda2 = 0.01
    Chosen by hand or small iteration in Chapters 3-4; the objective and solutions depend on them, with no principled selection rule.
  • SDP relaxation moment order d = 1
    Set to 1 for tractability in all reported experiments, so the claimed global optimality is not actually realized in the numerical results.
  • Entropic regularization epsilon = 0.01
    Fixed for all Chapter 5 experiments; it controls coupling smoothness and data distortion but is not tuned or justified.
  • Cost weights rho for multi-feature transport = reciprocals of feature ranges
    Chosen ad hoc to normalize per-feature movement cost in the Adult experiments.
  • Prediction threshold in Adult repair experiment = 0.1
    Selected by grid search, so part of the reported accuracy-fairness trade-off is fitted to the test procedure.
  • lambda3 in COMPAS post-processing = 0.05
    Fixed in Equation (4.10); no sensitivity analysis is reported.
  • V = (P^{Xs0}-P^{Xs1})/P^X = estimated from source data frequencies in all experiments
    The total-repair guarantee is enforced through V as a constraint, and the reported experiments estimate V from the same data using the sensitive attribute, rather than from an external population source.
  • Iteration count K and underflow guard epsilon = K = 400 to 600; epsilon = 1e-4 or 1e-5
    Numerical hyperparameters governing the approximation quality of Algorithm 1.
assumptions (7)
  • domain assumption The linear dynamical system is observable and the noises are zero-mean Gaussian with covariances W and V.
    Invoked in the LDS model and Section 3.2; the identification method is built on this structure.
  • domain assumption The Archimedean assumption holds for the NCPOP relaxation hierarchy.
    Required for Theorem D.6 and Theorem 3.1 to obtain convergence of the SDP bounds; stated explicitly in Theorem 3.1.
  • ad hoc to paper The flatness condition holds for extraction of system matrices from the SDP relaxation.
    The extraction step in Theorem 3.1 is only guaranteed when flatness is satisfied; the paper does not prove this for all observable systems and notes the extracted estimate can have error larger than epsilon.
  • domain assumption Source data are unbiased samples from the broader population, so source group distributions equal population group distributions.
    Core to Chapter 5; V is computed from population-level P^{Xs0}, P^{Xs1}, P^X, and the paper assumes source frequencies match the population.
  • domain assumption Only one binary sensitive attribute is considered.
    Explicitly stated in Sections 5.3 and 6.2; all constraints are derived for supp(S) = {s0, s1}.
  • domain assumption P^X_i is positive on supp(X) and the norm of 1/P^X is finite.
    Needed for V to be well-defined and for the KL projection onto C3 to be finite; stated as Assumption 5.1.
  • standard math Dykstra's algorithm with Bregman projections converges for the convex sets C1, C2, C3.
    Relied on in Lemma 5.7 via Bauschke and Lewis (2000); requires non-empty intersection and standard convex-set conditions.

how reviews work

0 comments
Cite this review

Pith. "Pith review of Optimisation Strategies for Ensuring Fairness in Machine Learning: With and Without Demographics." pith.science (2026). https://pith.science/paper/MZB4DPVW

@misc{pith2026241109056,
  author       = {Pith},
  title        = {Pith review of: Optimisation Strategies for Ensuring Fairness in Machine Learning: With and Without Demographics},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/MZB4DPVW}},
  note         = {Machine review of arXiv:2411.09056}
}
read the original abstract

Ensuring fairness has emerged as one of the primary concerns in AI and its related algorithms. Over time, the field of machine learning fairness has evolved to address these issues. This paper provides an extensive overview of this field and introduces two formal frameworks to tackle open questions in machine learning fairness. In one framework, operator-valued optimisation and min-max objectives are employed to address unfairness in time-series problems. This approach showcases state-of-the-art performance on the notorious COMPAS benchmark dataset, demonstrating its effectiveness in real-world scenarios. In the second framework, the challenge of lacking sensitive attributes, such as gender and race, in commonly used datasets is addressed. This issue is particularly pressing because existing algorithms in this field predominantly rely on the availability or estimations of such attributes to assess and mitigate unfairness. Here, a framework for a group-blind bias-repair is introduced, aiming to mitigate bias without relying on sensitive attributes. The efficacy of this approach is showcased through analyses conducted on the Adult Census Income dataset. Additionally, detailed algorithmic analyses for both frameworks are provided, accompanied by convergence guarantees, ensuring the robustness and reliability of the proposed methodologies.

Figures

Figures reproduced from arXiv: 2411.09056 by the authors.

Figure 1.1
Figure 1.1. Structure of the chapters in the thesis • Chapter 1: Introduction – Initiate a discussion on the societal impact of AI’s popularity, and present the efforts of governments and academia to address the associated concerns. – Outline the contributions of the thesis within this context. – Include a publication list. • Chapter 2: Preliminaries in Machine Learning Fairness – Introduce the state-of-the-art of machine learn… view at source ↗
Figure 3.1
Figure 3.1. Left: The fit values (3.4) of 81 experiments of our method at different combinations of noise standard deviations of process noise W and observation noise V and Right: at different combinations of parameters λ1 and λ2. Both use the data generated from systems in (LDS). Lighter colours indicate higher fit values and thus better simulation performance [PITH_FULL_IMAGE:figures/full_fig_p037_3_1.png] view at source ↗
Figure 3
Figure 3. shows in the left subplot the results obtained by our method (a yellow curve), and [PITH_FULL_IMAGE:figures/full_fig_p038_3.png] view at source ↗
Figures from the paper (20 more)
Figure 3.2
Figure 3.2. Figure 3.2: The fit values (3.4) of our method compared to the leading system identification [PITH_FULL_IMAGE:figures/full_fig_p039_3_2.png]
Figure 3
Figure 3. Figure 3: illustrates the runtime of the SDP relaxations and the baseline “least squares auto” [PITH_FULL_IMAGE:figures/full_fig_p039_3.png]
Figure 3.3
Figure 3.3. Figure 3.3: Left: The time series of stock price (dark) for the 21st-121st period used in Liu et al. (2016), and the predicted outputs of our method (yellow) compared against “least squares auto” (blue) implemented in Matlab™ System Identification Toolbox™. The estimated dimensi…
Figure 3.4
Figure 3.4. Figure 3.4: Left: The (solid or dashed) curves show the mean runtime of the SDP relaxation of the baseline “least squares auto” (blue), the TSSOS hierarchy (green) and the NPA hierarchy (yellow), at different moment orders d or estimated dimensions ˆk. The mean ± one standard de…
Figure 4.1
Figure 4.1. Figure 4.1: Forecast obtained using Equations (4.4–4.6): the solid lines in primary colours [PITH_FULL_IMAGE:figures/full_fig_p048_4_1.png]
Figure 4
Figure 4. Figure 4: illustrates 10 experiments with general forecasting procedures. For each experiment, () [PITH_FULL_IMAGE:figures/full_fig_p048_4.png]
Figure 4.2
Figure 4.2. Figure 4.2: Accuracy as a function of the degree of under-representation bias: the boxplot of [PITH_FULL_IMAGE:figures/full_fig_p049_4_2.png]
Figure 4.3
Figure 4.3. Figure 4.3: The dimensions of relaxations and the runtime of SDP thereupon as a function of [PITH_FULL_IMAGE:figures/full_fig_p051_4_3.png]
Figure 4.4
Figure 4.4. Figure 4.4: COMPAS recidivism scores of African-American and Caucasian defendants against [PITH_FULL_IMAGE:figures/full_fig_p052_4_4.png]
Figure 4
Figure 4. Figure 4: depicts an investigation of the performance of “Subgroup-Fair”, “Instant-Fair”, [PITH_FULL_IMAGE:figures/full_fig_p055_4.png]
Figure 4.5
Figure 4.5. Figure 4.5: The state of the art in post-processing for improving fairness. Five post-processing [PITH_FULL_IMAGE:figures/full_fig_p056_4_5.png]
Figure 4.6
Figure 4.6. Figure 4.6: The effects of our methods, when applied as post-processing. Three post-processing [PITH_FULL_IMAGE:figures/full_fig_p057_4_6.png]
Figure 4.7
Figure 4.7. Figure 4.7: Three post-processing methods “subgroup-fair” (blue), “instant-fair” (green), [PITH_FULL_IMAGE:figures/full_fig_p057_4_7.png]
Figure 5.1
Figure 5.1. Figure 5.1: Left: A cut-off point (vertical dark line) for an exam score is used to make school￾admission decisions. With the hypothetical exam-score distributions of the unprivileged group (orange) and the privileged group (purple), this group-blind cut-off point would result i…
Figure 5.2
Figure 5.2. Figure 5.2: Overview of the empirical distributions of [PITH_FULL_IMAGE:figures/full_fig_p079_5_2.png]
Figure 5.3
Figure 5.3. Figure 5.3: From left to right: the baseline coupling of Equation (5.41), the optimal coupling [PITH_FULL_IMAGE:figures/full_fig_p080_5_3.png]
Figure 5.4
Figure 5.4. Figure 5.4: Group-blind distributions. Solid green curves are the S-blind target distribu￾tions P X˜ used to compute couplings, which are the green curves in [PITH_FULL_IMAGE:figures/full_fig_p080_5_4.png]
Figure 5.5
Figure 5.5. Figure 5.5: Group-wise distributions. From left to right: the S-wise empirical distributions of P Xs0 , P Xs1 in source data, P X˜s0 , P X˜s1 in projected data from baseline, from 10−21-repair, from 10−31-repair, and from total repair. P X˜s0 is plotted orange and P X˜s1 is plot…
Figure 5
Figure 5. Figure 5: shows that our methods (”1 [PITH_FULL_IMAGE:figures/full_fig_p083_5.png]
Figure 5.6
Figure 5.6. Figure 5.6: Prediction performance for the Adult Census Income dataset (Becker and Kohavi, [PITH_FULL_IMAGE:figures/full_fig_p084_5_6.png]

Discussion (0). Continue with ORCID to comment.

Reference graph

Works this paper leans on

227 extracted references · 62 canonical work pages

  1. [1]

    and Mare c ek, J

    Zhou, Q. and Mare c ek, J. (2023). Learning of linear dynamical systems as a non-commutative polynomial optimization problem. IEEE Transactions on Automatic Control

    2023

  2. [2]

    Zhou, Q., Mare c ek, J., and Shorten, R. (2023a). Fairness in forecasting of observations of linear dynamical systems. Journal of Artificial Intelligence Research , 76:1247--1280

    2023

  3. [3]

    Zhou, Q., Mare c ek, J., and Shorten, R. (2023b). Subgroup fairness in two-sided markets. Plos one , 18(2):e0281443

    2023

  4. [4]

    Zhou, Q., Mare c ek, J., and Shorten, R. N. (2021). Fairness in forecasting and learning linear dynamical systems. In Proceedings of the AAAI Conference on Artificial Intelligence , volume 35, pages 11134--11142

    2021

  5. [5]

    Agarwal, S. (2021). Trade-offs between fairness and interpretability in machine learning. In IJCAI 2021 Workshop on AI for Social Good

    2021

  6. [6]

    J., and Vayanos, P

    Aghaei, S., Azizi, M. J., and Vayanos, P. (2019). Learning optimal and fair decision trees for non-discriminative decision-making. In Proceedings of the AAAI Conference on Artificial Intelligence , volume 33, pages 1418--1426

    2019

  7. [7]

    and Carlier, G

    Agueh, M. and Carlier, G. (2011). Barycenters in the W asserstein space. SIAM Journal on Mathematical Analysis , 43(2):904--924

    2011

  8. [8]

    Albert, A. A. and Muckenhoupt, B. (1957). On matrices of trace zeros. Michigan Mathematical Journal , 4(1):1--3

    1957

Show all 227 references
  1. [9]

    Allen, H. J. (2019). Regulatory sandboxes. Geo. Wash. L. Rev. , 87:579

    2019

  2. [10]

    P., Schwarting, W., Bhatia, S

    Amini, A., Soleimany, A. P., Schwarting, W., Bhatia, S. N., and Rus, D. (2019). Uncovering and mitigating algorithmic bias through learned latent structure. In Proceedings of the 2019 AAAI/ACM Conference on AI, Ethics, and Society , pages 289--295

    2019

  3. [11]

    Anava, O., Hazan, E., Mannor, S., and Shamir, O. (2013). Online learning for time series prediction. In COLT 2013 - The 26th Annual Conference on Learning Theory, June 12-14, 2013, Princeton University, NJ, USA

    2013

  4. [12]

    Anderson, R. M. and May, R. M. (1992). Infectious diseases of humans: dynamics and control . Oxford university press

    1992

  5. [13]

    and Villeneuve, S

    Andrus, M. and Villeneuve, S. (2022). Demographic-reliant algorithmic fairness: Characterizing the risks of demographic data collection in the pursuit of fairness. In Proceedings of the 2022 ACM Conference on Fairness, Accountability, and Transparency , pages 1709--1721

    2022

  6. [14]

    Angwin, J., Larson, J., Mattu, S., and Kirchner, L. (2016). Machine bias. ProPublica, May , 23:2016

    2016

  7. [15]

    and Weller, A

    Ashurst, C. and Weller, A. (2023). Fairness without demographic data: A survey of approaches. In Proceedings of the 3rd ACM Conference on Equity and Access in Algorithms, Mechanisms, and Optimization , pages 1--12

    2023

  8. [16]

    B., Brevoort, K

    Avery, R. B., Brevoort, K. P., and Canner, G. (2012). Does credit scoring produce a disparate impact? Real Estate Economics , 40:S65--S114

    2012

  9. [17]

    Awasthi, P., Cortes, C., Mansour, Y., and Mohri, M. (2020). Beyond individual and group fairness. CoRR , abs/2008.09490

    2020 arXiv

  10. [18]

    Awwad, Y., Fletcher, R., Frey, D., Gandhi, A., Najafian, M., and Teodorescu, M. (2020). Exploring fairness in machine learning for international development. Technical report, CITE MIT D-Lab

    2020

  11. [19]

    Bakshi, A., Liu, A., Moitra, A., and Yau, M. (2023). A new approach to learning linear dynamical systems. arXiv preprint arXiv:2301.09519

    2023 arXiv

  12. [20]

    Barocas, S., Hardt, M., and Narayanan, A. (2023). Fairness and machine learning: Limitations and opportunities . MIT Press

    2023

  13. [21]

    and Selbst, A

    Barocas, S. and Selbst, A. D. (2016). Big data's disparate impact. California law review , pages 671--732

    2016

  14. [22]

    H., Burachik, R

    Bauschke, H. H., Burachik, R. S., Herman, D. B., and Kaya, C. Y. (2020). On D ykstra's algorithm: finite convergence, stalling, and the method of alternating projections. Optimization Letters , 14:1975--1987

    2020

  15. [23]

    Bauschke, H. H. and Lewis, A. S. (2000). Dykstras algorithm with B regman projections: A convergence proof. Optimization , 48(4):409--427

    2000

  16. [24]

    and Kohavi, R

    Becker, B. and Kohavi, R. (1996). Adult . UCI Machine Learning Repository. DOI : https://doi.org/10.24432/C5XW20

    1996 doi

  17. [25]

    Bellamy, R. K. E., Dey, K., Hind, M., Hoffman, S. C., Houde, S., Kannan, K., Lohia, P., Martino, J., Mehta, S., Mojsilovic, A., Nagar, S., Ramamurthy, K. N., Richards, J., Saha, D., Sattigeri, P., Singh, M., Varshney, K. R., and Zhang, Y. (2018). AI Fairness 360: An extensible...

    2018

  18. [26]

    Benamou, J.-D., Carlier, G., Cuturi, M., Nenna, L., and Peyr \'e , G. (2015). Iterative B regman projections for regularized transportation problems. SIAM Journal on Scientific Computing , 37(2):A1111--A1138

    2015

  19. [27]

    Bertail, P., Cl \'e men c on, S., Guyonvarch, Y., and Noiry, N. (2021). Learning from biased data: A semi-parametric approach. In Meila, M. and Zhang, T., editors, Proceedings of the 38th International Conference on Machine Learning , volume 139 of Proceedings of Machine Learn...

    2021

  20. [28]

    Bertsekas, D. (2009). Convex optimization theory , volume 1. Athena Scientific

    2009

  21. [29]

    J., Hammel, E

    Bickel, P. J., Hammel, E. A., and O'Connell, J. W. (1977). Sex bias in graduate admissions: Data from berkeley. Statistics and public policy , pages 113--130

    1977

  22. [30]

    Binns, R. (2020). On the apparent conflict between individual and group fairness. In Proceedings of the 2020 conference on fairness, accountability, and transparency , pages 514--524

    2020

  23. [31]

    and Stangl, K

    Blum, A. and Stangl, K. (2019). Recovering from biased data: Can fairness constraints improve accuracy? arXiv preprint arXiv:1912.01094 , abs/1912.01094

    2019 arXiv

  24. [32]

    Bondar, D., Jacobs, K., Korpas, G., Marecek, J., Popovych, Z., and Vala, J. (2023). A globally convergent approach for quantum control and system identification. Bulletin of the American Physical Society

    2023

  25. [33]

    I., Popovych, Z., Jacobs, K., Korpas, G., and Marecek, J

    Bondar, D. I., Popovych, Z., Jacobs, K., Korpas, G., and Marecek, J. (2022). Recovering models of open quantum systems from data via polynomial optimization: Towards globally convergent quantum system identification. arXiv preprint arXiv:2203.17164

    2022 arXiv

  26. [34]

    Borwein, J. M. and Wolkowicz, H. (1981). Facial reduction for a cone-convex programming problem. Journal of the Australian Mathematical Society , 30(3):369--380

    1981

  27. [35]

    (October 12th, 2023)

    Bose, P., Biswas, S., and Sengupta, S. (October 12th, 2023). The case for caste census in india | explained. The Hindu

    2023

  28. [36]

    Bregman, L. M. (1967). The relaxation method of finding the common point of convex sets and its application to the solution of problems in convex programming. USSR computational mathematics and mathematical physics , 7(3):200--217

    1967

  29. [37]

    Burgdorf, S., Klep, I., Povh, J., et al. (2016). Optimization of polynomials in non-commuting variables , volume 2. Springer

    2016

  30. [38]

    and De Bie, T

    Buyl, M. and De Bie, T. (2022). Optimal transport of classifiers to fairness. Advances in Neural Information Processing Systems , 35:33728--33740

    2022

  31. [39]

    Caffarelli, L. A. and McCann, R. J. (2010). Free boundaries in optimal transport and M onge- A mpere obstacle problems. Annals of mathematics , pages 673--730

    2010

  32. [40]

    and Z liobait \.e , I

    Calders, T. and Z liobait \.e , I. (2013). Why unbiased computational processes can lead to discriminative decision procedures. In Discrimination and Privacy in the Information Society: Data mining and profiling in large databases , pages 43--57. Springer

    2013

  33. [41]

    P., Wei, D., Vinzamuri, B., Ramamurthy, K

    Calmon, F. P., Wei, D., Vinzamuri, B., Ramamurthy, K. N., and Varshney, K. R. (2017). Optimized pre-processing for discrimination prevention. In Proceedings of the 31st International Conference on Neural Information Processing Systems , pages 3995--4004

    2017

  34. [42]

    Campi, M. C. and Weyer, E. (2002). Finite sample properties of system identification methods. IEEE Transactions on Automatic Control , 47(8):1329--1334

    2002

  35. [43]

    and Haas, C

    Caton, S. and Haas, C. (2023). Fairness in machine learning: A survey. ACM Comput. Surv. Just Accepted

    2023

  36. [44]

    Caton, S., Malisetty, S., and Haas, C. (2022). Impact of imputation strategies on fairness in machine learning. Journal of Artificial Intelligence Research , 74:1011--1035

    2022

  37. [45]

    Chai, J., Jang, T., and Wang, X. (2022). Fairness without demographics through knowledge distillation. Advances in Neural Information Processing Systems , 35:19152--19164

    2022

  38. [46]

    and Wang, X

    Chai, J. and Wang, X. (2022). Self-supervised fair representation learning without demographics. Advances in Neural Information Processing Systems , 35:27100--27113

    2022

  39. [47]

    and Shokri, R

    Chang, H. and Shokri, R. (2021). On the privacy risks of algorithmic fairness. In 2021 IEEE European Symposium on Security and Privacy (EuroS&P) , pages 292--303. IEEE

    2021

  40. [48]

    Chapel, L., Flamary, R., Wu, H., F \'e votte, C., and Gasso, G. (2021). Unbalanced optimal transport through non-negative penalized linear regression. Advances in Neural Information Processing Systems , 34:23270--23282

    2021

  41. [49]

    D., and Sontag, D

    Chen, I., Johansson, F. D., and Sontag, D. (2018). Why is my classifier discriminatory? Advances in neural information processing systems , 31

    2018

  42. [50]

    Chiappa, S. (2019). Path-specific counterfactual fairness. In Proceedings of the AAAI Conference on Artificial Intelligence , volume 33, pages 7801--7808

    2019

  43. [51]

    Chizat, L., Peyr \'e , G., Schmitzer, B., and Vialard, F.-X. (2018a). Scaling algorithms for unbalanced optimal transport problems. Mathematics of Computation , 87(314):2563--2609

    2018

  44. [52]

    Chizat, L., Peyr \'e , G., Schmitzer, B., and Vialard, F.-X. (2018b). Unbalanced optimal transport: Dynamic and kantorovich formulations. Journal of Functional Analysis , 274(11):3090--3123

    2018

  45. [53]

    and Voit, E

    Chou, I.-C. and Voit, E. O. (2009). Recent developments in parameter estimation and structure identification of biochemical and genomic systems. Mathematical biosciences , 219(2):57--83

    2009

  46. [54]

    Chouldechova, A. (2017). Fair prediction with disparate impact: A study of bias in recidivism prediction instruments. Big data , 5(2):153--163

    2017

  47. [55]

    and Roth, A

    Chouldechova, A. and Roth, A. (2020). A snapshot of the frontiers of fairness in machine learning. Communications of the ACM , 63(5):82--89

    2020

  48. [56]

    Chzhen, E., Denis, C., Hebiri, M., Oneto, L., and Pontil, M. (2020). Fair regression with W asserstein barycenters. Advances in Neural Information Processing Systems , 33:7321--7331

    2020

  49. [57]

    Chzhen, E., Giraud, C., and Stoltz, G. (2021). A unified approach to fair online learning via blackwell approachability. Advances in Neural Information Processing Systems , 34:18280--18292

    2021

  50. [58]

    W., Biancofiore, G

    Cornacchia, G., Anelli, V. W., Biancofiore, G. M., Narducci, F., Pomo, C., Ragone, A., and Di Sciascio, E. (2023). Auditing fairness under unawareness through counterfactual reasoning. Information Processing & Management , 60(2):103224

    2023

  51. [59]

    Creager, E., Madras, D., Jacobsen, J.-H., Weis, M., Swersky, K., Pitassi, T., and Zemel, R. (2019). Flexibly fair representation learning by disentanglement. In International conference on machine learning , pages 1436--1445. PMLR

    2019

  52. [60]

    Creager, E., Madras, D., Pitassi, T., and Zemel, R. (2020). Causal modeling for fairness in dynamical systems. In International conference on machine learning , pages 2185--2195. PMLR

    2020

  53. [61]

    Cummings, R., Gupta, V., Kimpara, D., and Morgenstern, J. (2019). On the compatibility of privacy and fairness. In Adjunct Publication of the 27th Conference on User Modeling, Adaptation and Personalization , pages 309--315

    2019

  54. [62]

    Curto, R. E. and Fialkow, L. A. (2005). Truncated k-moment problems in several variables. Journal of Operator Theory , pages 189--226

    2005

  55. [63]

    D'Amour, A., Srinivasan, H., Atwood, J., Baljekar, P., Sculley, D., and Halpern, Y. (2020). Fairness is not static: deeper understanding of long term fairness via simulation studies. In Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency , pages 525--534

    2020

  56. [64]

    Dard, C., Fricker-Hidalgo, H., Brenier-Pinchart, M.-P., and Pelloux, H. (2016). Relevance of and new developments in serology for toxoplasmosis. Trends in parasitology , 32(6):492--506

    2016

  57. [65]

    Delis, M. D. and Papadopoulos, P. (2019). Mortgage lending discrimination across the us: New methodology and new evidence. Journal of Financial Services Research , 56(3):341--368

    2019

  58. [66]

    and Farid, H

    Dressel, J. and Farid, H. (2021). The dangers of risk prediction in the criminal justice system

    2021

  59. [67]

    Dutta, S., Wei, D., Yueksel, H., Chen, P.-Y., Liu, S., and Varshney, K. R. (2019). An information-theoretic perspective on the relationship between fairness and accuracy. ArXiv , abs/1910.07870

    2019 arXiv

  60. [68]

    Dwork, C. (2006). Differential privacy. In Bugliesi, M., Preneel, B., Sassone, V., and Wegener, I., editors, Automata, Languages and Programming , pages 1--12, Berlin, Heidelberg. Springer Berlin Heidelberg

    2006

  61. [69]

    Dwork, C., Hardt, M., Pitassi, T., Reingold, O., and Zemel, R. (2012). Fairness through awareness. In Proceedings of the 3rd innovations in theoretical computer science conference , pages 214--226

    2012

  62. [70]

    and Temam, R

    Ekeland, I. and Temam, R. (1999). Convex analysis and variational problems . SIAM

    1999

  63. [71]

    Elzayn, H., Black, E., Vossler, P., Jo, N., Goldin, J., and Ho, D. E. (2023). Optimal transport in some fairness scenarios

    2023

  64. [72]

    A., Neville, S., Scheidegger, C., and Venkatasubramanian, S

    Ensign, D., Friedler, S. A., Neville, S., Scheidegger, C., and Venkatasubramanian, S. (2018). Runaway feedback loops in predictive policing. In Conference on fairness, accountability and transparency , pages 160--171. PMLR

    2018

  65. [73]

    Fair Isaac Corporation (2021). About. https://www.ficoscore.com/about

    2021

  66. [74]

    Faradonbeh, M. K. S., Tewari, A., and Michailidis, G. (2018). Finite time identification in unstable linear systems. Automatica , 96:342--353

    2018

  67. [75]

    A., Moeller, J., Scheidegger, C., and Venkatasubramanian, S

    Feldman, M., Friedler, S. A., Moeller, J., Scheidegger, C., and Venkatasubramanian, S. (2015). Certifying and removing disparate impact. In Proceedings of the 21th ACM SIGKDD international conference on knowledge discovery and data mining , pages 259--268

    2015

  68. [76]

    Fernando, M.-P., C \`e sar, F., David, N., and Jos \'e , H.-O. (2021). Missing the missing values: The ugly duckling of fairness in machine learning. International Journal of Intelligent Systems , 36(7):3217--3258

    2021

  69. [77]

    M., Cot \'e , M., and Criado, N

    Ferrer, X., van Nuenen, T., Such, J. M., Cot \'e , M., and Criado, N. (2021). Bias and discrimination in ai: a cross-disciplinary perspective. IEEE Technology and Society Magazine , 40(2):72--80

    2021

  70. [78]

    Figalli, A. (2010). The optimal partial transport problem. Archive for rational mechanics and analysis , 195(2):533--560

    2010

  71. [79]

    Friede, G., Busch, T., and Bassen, A. (2015). ESG and financial performance: aggregated evidence from more than 2000 empirical studies. Journal of Sustainable Finance & Investment , 5(4):210--233

    2015

  72. [80]

    Fukuda, M., Kojima, M., Murota, K., and Nakata, K. (2001). Exploiting sparsity in semidefinite programming via matrix completion i: General framework. SIAM Journal on Optimization , 11(3):647--674

    2001

  73. [81]

    and Pechenizkiy, M

    Gajane, P. and Pechenizkiy, M. (2018). On formalizing fairness in prediction with machine learning. In Friedler, S. A. and Wilson, C., editors, Conference on Fairness, Accountability and Transparency, FAT 2018 , volume 81 of Proceedings of Machine Learning Research , New York,...

    2018

  74. [82]

    and \'S wi e ch, A

    Gangbo, W. and \'S wi e ch, A. (1998). Optimal maps for the multidimensional monge-kantorovich problem. Communications on Pure and Applied Mathematics: A Journal Issued by the Courant Institute of Mathematical Sciences , 51(1):23--45

    1998

  75. [83]

    Geiger, P., Zhang, K., Schoelkopf, B., Gong, M., and Janzing, D. (2015). Causal inference by identification of vector autoregressive processes with hidden components. In International Conference on Machine Learning , pages 1917--1925

    2015

  76. [84]

    and Neumark, M

    Gelfand, I. and Neumark, M. (1943). On the imbedding of normed rings into the ring of operators in H ilbert space. Rec. Math. [Mat. Sbornik] N.S. , 12(2):197--217

    1943

  77. [85]

    Gordaliza, P., Del Barrio, E., Fabrice, G., and Loubes, J.-M. (2019). Obtaining fairness using optimal transport theory. In International Conference on Machine Learning , pages 2357--2365. PMLR

    2019

  78. [86]

    L., Loubes, J.-M., and Rigollet, P

    Gouic, T. L., Loubes, J.-M., and Rigollet, P. (2020). Projection to fairness in statistical learning. arXiv preprint arXiv:2005.11720

    2020 arXiv

  79. [87]

    M., Rasch, M

    Gretton, A., Borgwardt, K. M., Rasch, M. J., Sch \"o lkopf, B., and Smola, A. (2012). A kernel two-sample test. The Journal of Machine Learning Research , 13(1):723--773

    2012

  80. [88]

    B., Gummadi, K

    Grgi \'c -Hla c a, N., Zafar, M. B., Gummadi, K. P., and Weller, A. (2018). Beyond distributive fairness in algorithmic decision making: Feature selection for procedurally fair learning. In Proceedings of the AAAI Conference on Artificial Intelligence , volume 32

    2018

  81. [89]

    Hancock, B., Hioe, E., and Schaninger, B. (2018). The fairness factor in performance management. McKinsey Quarterly , 2:45--54

    2018

  82. [90]

    Hardt, M., Ma, T., and Recht, B. (2018). Gradient descent learns linear dynamical systems. Journal of Machine Learning Research , 19:1--44

    2018

  83. [91]

    Hardt, M., Price, E., and Srebro, N. (2016). Equality of opportunity in supervised learning. In Advances in neural information processing systems , pages 3315--3323

    2016

  84. [92]

    Hashimoto, T., Srivastava, M., Namkoong, H., and Liang, P. (2018). Fairness without demographics in repeated loss minimization. In International Conference on Machine Learning , pages 1929--1938. PMLR

    2018

  85. [93]

    Hazan, E., Lee, H., Singh, K., Zhang, C., and Zhang, Y. (2018). Spectral filtering for general linear dynamical systems. In Advances in Neural Information Processing Systems , pages 4634--4643

    2018

  86. [94]

    Hazan, E., Singh, K., and Zhang, C. (2017). Learning linear dynamical systems via spectral filtering. In Advances in Neural Information Processing Systems , pages 6702--6712

    2017

  87. [95]

    and Ma, Y

    He, H. and Ma, Y. (2013). Imbalanced Learning: Foundations, Algorithms, and Applications . Wiley, Germany

    2013

  88. [96]

    Hegarty, P. (2019). Inequality brokered. Proceedings of the National Academy of Sciences , 116(19):9152--9154

    2019

  89. [97]

    and McCullough, S

    Helton, J. and McCullough, S. (2004). A positivstellensatz for non-commutative polynomials. Transactions of the American Mathematical Society , 356(9):3721--3737

    2004

  90. [98]

    Hoens, T. R. and Chawla, N. V. (2013). Imbalanced datasets: from sampling to classifiers. Imbalanced learning: Foundations, algorithms, and applications , pages 43--59

    2013

  91. [99]

    and Zhang, L

    Hu, Y. and Zhang, L. (2022). Achieving long-term fairness in sequential decision making. In Proceedings of the AAAI Conference on Artificial Intelligence , volume 36, pages 9549--9557

    2022

  92. [100]

    Jabbari, S., Joseph, M., Kearns, M., Morgenstern, J., and Roth, A. (2017). Fairness in reinforcement learning. In International conference on machine learning , pages 1617--1626. PMLR

    2017

  93. [101]

    Jaeger, M. (2009). On fairness and randomness. Information and Computation , 207(9):909--922

    2009

  94. [102]

    Jansson, M. (2003). Subspace identification and ARX modeling. In Proceedings of the 13th IFAC SYSID Symposium , pages 1625--1630

    2003

  95. [103]

    and Proutiere, A

    Jedra, Y. and Proutiere, A. (2020). Finite-time identification of stable linear systems optimality of the least-squares estimator. In 2020 59th IEEE Conference on Decision and Control (CDC) , pages 996--1001. IEEE

    2020

  96. [104]

    Jeong, H., Wang, H., and Calmon, F. P. (2022). Fairness without imputation: A decision tree approach for fair prediction with missing values. In Proceedings of the AAAI Conference on Artificial Intelligence , volume 36, pages 9558--9566

    2022

  97. [105]

    and Nachum, O

    Jiang, H. and Nachum, O. (2020). Identifying and correcting label bias in machine learning. In International Conference on Artificial Intelligence and Statistics , pages 702--712. PMLR

    2020

  98. [106]

    Jiang, R., Pacchiano, A., Stepleton, T., Jiang, H., and Chiappa, S. (2020). Wasserstein fair classification. In Uncertainty in artificial intelligence , pages 862--872. PMLR

    2020

  99. [107]

    H., and Roth, A

    Joseph, M., Kearns, M., Morgenstern, J. H., and Roth, A. (2016). Fairness in learning: Classic and contextual bandits. Advances in neural information processing systems , 29

    2016

  100. [108]

    T., Asher, N., Loubes, J.-M., and Risser, L

    Jourdan, F., Kaninku, T. T., Asher, N., Loubes, J.-M., and Risser, L. (2023). How optimal transport can tackle gender biases in multi-class neural network classifiers for job recommendations. Algorithms , 16(3):174

    2023

  101. [109]

    Kamiran, F., Karim, A., and Zhang, X. (2012). Decision theory for discrimination-aware classification. In 2012 IEEE 12th International Conference on Data Mining , pages 924--929. IEEE

    2012

  102. [110]

    Katayama, T. (2006). Subspace methods for system identification . Springer Science & Business Media

    2006

  103. [111]

    S., and Malhi, A

    Kaur, H., Pannu, H. S., and Malhi, A. K. (2019). A systematic review on imbalanced data challenges in machine learning: Applications and solutions. ACM Computing Surveys (CSUR) , 52(4):1--36

    2019

  104. [112]

    R., Parascandolo, G., Hardt, M., Janzing, D., and Sch \"o lkopf, B

    Kilbertus, N., Carulla, M. R., Parascandolo, G., Hardt, M., Janzing, D., and Sch \"o lkopf, B. (2017). Avoiding discrimination through causal reasoning. In Advances in Neural Information Processing Systems , pages 656--666

    2017

  105. [113]

    S., Chen, J., and Talwalkar, A

    Kim, J. S., Chen, J., and Talwalkar, A. (2020). Fact: A diagnostic for group fairness trade-offs. In International Conference on Machine Learning , pages 5264--5274. PMLR

    2020

  106. [114]

    Kleinberg, J., Ludwig, J., Mullainathan, S., and Sunstein, C. R. (2018). Discrimination in the age of algorithms. Journal of Legal Analysis , 10:113--174

    2018

  107. [115]

    Kleinberg, J., Mullainathan, S., and Raghavan, M. (2017). Inherent trade-offs in the fair determination of risk scores. In 8th Innovations in Theoretical Computer Science Conference (ITCS 2017) . Schloss Dagstuhl-Leibniz-Zentrum fuer Informatik

    2017

  108. [116]

    Klep, I., Magron, V., and Povh, J. (2022). Sparse noncommutative polynomial optimization. Mathematical Programming , pages 1--41

    2022

  109. [117]

    Klep, I., Povh, J., and Volcic, J. (2018). Minimizer extraction in polynomial optimization is robust. SIAM Journal on Optimization , 28(4):3177--3207

    2018

  110. [118]

    and McCann, R

    Korman, J. and McCann, R. (2015). Optimal transportation with capacity constraints. Transactions of the American Mathematical Society , 367(3):1501--1521

    2015

  111. [119]

    and McCann, R

    Korman, J. and McCann, R. J. (2013). Insights into capacity-constrained optimal transport. Proceedings of the National Academy of Sciences , 110(25):10064--10067

    2013

  112. [120]

    Kozdoba, M., Marecek, J., Tchrakian, T., and Mannor, S. (2019). On-line learning of linear dynamical systems: Exponential forgetting in K alman filters. In The Thirty-Third AAAI Conference on Artificial Intelligence (AAAI-19) , pages 4098--4105. arXiv preprint arXiv:1809.05870

    2019 arXiv

  113. [121]

    and Marecek, J

    Kungurtsev, V. and Marecek, J. (2020). A two-step pre-processing for semidefinite programming. In 2020 59th IEEE Conference on Decision and Control (CDC) , pages 384--389. IEEE

    2020

  114. [122]

    J., Loftus, J., Russell, C., and Silva, R

    Kusner, M. J., Loftus, J., Russell, C., and Silva, R. (2017). Counterfactual fairness. In Advances in Neural Information Processing Systems , pages 4066--4076

    2017

  115. [123]

    Lahoti, P., Beutel, A., Chen, J., Lee, K., Prost, F., Thain, N., Wang, X., and Chi, E. (2020). Fairness without demographics through adversarially reweighted learning. Advances in neural information processing systems , 33:728--740

    2020

  116. [124]

    Lasserre, J. B. (2001). Global optimization with polynomials and the problem of moments. SIAM Journal on optimization , 11(3):796--817

    2001

  117. [125]

    Lasserre, J. B. (2009). Moments, positive polynomials and their applications , volume 1. World Scientific

    2009

  118. [126]

    Le, K., Nguyen, H., Nguyen, K., Pham, T., and Ho, N. (2022). On multimarginal partial optimal transport: Equivalent forms and computational complexity. In International Conference on Artificial Intelligence and Statistics , pages 4397--4413. PMLR

    2022

  119. [127]

    Lee, Y., Boche, H., and Kutyniok, G. (2023). Computability of optimizers. arXiv preprint arXiv:2301.06148

    2023 arXiv

  120. [128]

    C., Zhao, P., and Sun, J

    Liu, C., Hoi, S. C., Zhao, P., and Sun, J. (2016). Online arima algorithms for time series prediction. In Thirtieth AAAI conference on artificial intelligence

    2016

  121. [129]

    Liu, D., Do, V., Usunier, N., and Nickel, M. (2023). Group fairness without demographics using social networks. In Proceedings of the 2023 ACM Conference on Fairness, Accountability, and Transparency , pages 1432--1449

    2023

  122. [130]

    Z., Haghgoo, B., Chen, A

    Liu, E. Z., Haghgoo, B., Chen, A. S., Raghunathan, A., Koh, P. W., Sagawa, S., Liang, P., and Finn, C. (2021). Just train twice: Improving group robustness without training group information. In International Conference on Machine Learning , pages 6781--6792. PMLR

    2021

  123. [131]

    T., Dean, S., Rolf, E., Simchowitz, M., and Hardt, M

    Liu, L. T., Dean, S., Rolf, E., Simchowitz, M., and Hardt, M. (2018). Delayed impact of fair machine learning. In International Conference on Machine Learning , pages 3150--3158. PMLR

    2018

  124. [132]

    and Vicente, L

    Liu, S. and Vicente, L. N. (2022). Accuracy and fairness trade-offs in machine learning: A stochastic multi-objective approach. Computational Management Science , 19(3):513--537

    2022

  125. [133]

    Ljung, L. (1976). Consistency of the least-squares identification method. IEEE Transactions on Automatic Control , 21(5):779--781

    1976

  126. [134]

    Ljung, L. (1998). System Identification: Theory for the User . Pearson Education

    1998

  127. [135]

    Locatello, F., Abbati, G., Rainforth, T., Bauer, S., Sch\" o lkopf, B., and Bachem, O. (2019). On the fairness of disentangled representations. In Advances in Neural Information Processing Systems 32 , pages 14611--14624. Curran Associates, Inc., Vancouver, Canada

    2019

  128. [136]

    K., Ramamurthy, K

    Lohia, P. K., Ramamurthy, K. N., Bhide, M., Saha, D., Varshney, K. R., and Puri, R. (2019). Bias mitigation post-processing for individual and group fairness. In Icassp 2019-2019 ieee international conference on acoustics, speech and signal processing (icassp) , pages 2847--2851. IEEE

    2019

  129. [137]

    and Wang, J

    Magron, V. and Wang, J. (2023). Sparse polynomial optimization: theory and practice . World Scientific

    2023

  130. [138]

    Maity, S., Mukherjee, D., Yurochkin, M., and Sun, Y. (2021). Does enforcing fairness mitigate biases caused by subpopulation shift? Advances in Neural Information Processing Systems , 34

    2021

  131. [139]

    Majumdar, A., Hall, G., and Ahmadi, A. A. (2019). Recent scalability improvements for semidefinite programming with applications in machine learning, control, and robotics. Annual Review of Control, Robotics, and Autonomous Systems , 3

    2019

  132. [140]

    Mandal, D., Deng, S., Jana, S., Wing, J., and Hsu, D. J. (2020). Ensuring fairness beyond the training data. Advances in neural information processing systems , 33:18445--18456

    2020

  133. [141]

    Mann, H. B. and Wald, A. (1943). On the statistical treatment of linear stochastic difference equations. Econometrica, Journal of the Econometric Society , pages 173--220

    1943

  134. [142]

    Mashiat, T., Gitiaux, X., Rangwala, H., Fowler, P., and Das, S. (2022). Trade-offs between group fairness metrics in societal resource allocation. In Proceedings of the 2022 ACM Conference on Fairness, Accountability, and Transparency , pages 1095--1105

    2022

  135. [143]

    Mehrabi, N., Morstatter, F., Saxena, N., Lerman, K., and Galstyan, A. (2021). A survey on bias and fairness in machine learning. ACM computing surveys (CSUR) , 54(6):1--35

    2021

  136. [144]

    Mhasawade, V., Zhao, Y., and Chunara, R. (2021). Machine learning and algorithmic fairness in public and population health. Nature Machine Intelligence , 3(8):659--666

    2021

  137. [145]

    Morgan, D. (2023). Anticipatory regulatory instruments for ai systems: A comparative study of regulatory sandbox schemes. In Proceedings of the 2023 AAAI/ACM Conference on AI, Ethics, and Society , pages 980--981

    2023

  138. [146]

    The MOSEK O ptimizer API for P ython 9.2

    MOSEK, ApS (2020). The MOSEK O ptimizer API for P ython 9.2

    2020

  139. [147]

    Mukherjee, D., Yurochkin, M., Banerjee, M., and Sun, Y. (2020). Two simple ways to learn individual fairness metrics from data. In International Conference on Machine Learning , pages 7097--7107. PMLR

    2020

  140. [148]

    Nabi, R., Malinsky, D., and Shpitser, I. (2019). Learning optimal fair policies. In International Conference on Machine Learning , pages 4674--4682. PMLR

    2019

  141. [149]

    and Shpitser, I

    Nabi, R. and Shpitser, I. (2018). Fair inference on outcomes. In Proceedings of the AAAI Conference on Artificial Intelligence , volume 32

    2018

  142. [150]

    Nath, J. S. and Jawanpuria, P. (2020). Statistical optimal transport posed as learning kernel mean embedding. Technical report, Technical report

    2020

  143. [151]

    Navascu \'e s, M., Pironio, S., and Ac \' n, A. (2012). Sdp relaxations for non-commutative polynomial optimization. Handbook on Semidefinite, Conic and Polynomial Optimization , pages 601--634

    2012

  144. [152]

    (June 13th, 2020)

    Ndiaye, S. (June 13th, 2020). Nous payons aujourd’hui l’effacement de l’universalisme républicain. Le Monde

    2020

  145. [153]

    and Fercoq, O

    Necoara, I. and Fercoq, O. (2022). Linear convergence of random dual coordinate descent on nonpolyhedral convex problems. Mathematics of Operations Research , 47(4):2641--2666

    2022

  146. [154]

    J., and Nagin, D

    Neil, R., Sampson, R. J., and Nagin, D. S. (2021). Social change and cohort differences in group-based arrest trajectories over the last quarter-century. Proceedings of the National Academy of Sciences , 118(31)

    2021

  147. [155]

    Nickerson, D. et al. (2016). Asset price volatility, credit rationing and rational lending discrimination. International Journal of Economics and Finance , 8(10):140--158

    2016

  148. [156]

    and Chiappa, S

    Oneto, L. and Chiappa, S. (2020). Fairness in machine learning. In Recent trends in learning from data: Tutorials from the inns big data and deep learning conference (innsbddl2019) , pages 155--196. Springer

    2020

  149. [157]

    Oneto, L., Doninini, M., Elders, A., and Pontil, M. (2019). Taking advantage of multitask learning for fair classification. In Proceedings of the 2019 AAAI/ACM Conference on AI, Ethics, and Society , pages 227--237

    2019

  150. [158]

    Osoba, O. A. and Welser IV, W. (2017). An intelligence in our image: The risks of bias and errors in artificial intelligence . Rand Corporation

    2017

  151. [159]

    Overschee, P. V. and Moor, B. D. (2012). Subspace identification for linear systems: Theory—Implementation—Applications . Springer Science & Business Media

    2012

  152. [160]

    and Ozay, N

    Oymak, S. and Ozay, N. (2019). Non-asymptotic identification of LTI systems from a single trajectory. In 2019 American Control Conference (ACC) , pages 5655--5661. IEEE

    2019

  153. [161]

    and Parrilo, P

    Permenter, F. and Parrilo, P. (2018). Partial facial reduction: simplified, equivalent sdps via approximations of the psd cone. Mathematical Programming , 171(1-2):1--54

    2018

  154. [162]

    and Shmueli, E

    Pessach, D. and Shmueli, E. (2022). A review on fairness in machine learning. ACM Computing Surveys (CSUR) , 55(3):1--44

    2022

  155. [163]

    Petersen, F., Mukherjee, D., Sun, Y., and Yurochkin, M. (2021). Post-processing for individual fairness. Advances in Neural Information Processing Systems , 34:25944--25955

    2021

  156. [164]

    Peyr \'e , G. (2015). Entropic approximation of W asserstein gradient flows. SIAM Journal on Imaging Sciences , 8(4):2323--2351

    2015

  157. [165]

    Peyr \'e , G., Cuturi, M., et al. (2019). Computational optimal transport. Foundations and Trends in Machine Learning , 11(5-6):355--607

    2019

  158. [166]

    Pham, K., Le, K., Ho, N., Pham, T., and Bui, H. (2020). On unbalanced optimal transport: An analysis of sinkhorn algorithm. In International Conference on Machine Learning , pages 7673--7682. PMLR

    2020

  159. [167]

    Pinz \'o n, C., Palamidessi, C., Piantanida, P., and Valencia, F. (2021). On the impossibility of non-trivial accuracy under fairness constraints. arXiv preprint arXiv:2107.06944 , abs/2107.06944

    2021 arXiv

  160. [168]

    Pironio, S., Navascu \'e s, M., and Acin, A. (2010a). Convergent relaxations of polynomial optimization problems with noncommuting variables. SIAM Journal on Optimization , 20(5):2157--2180

    2010

  161. [169]

    Pironio, S., Navascu \'e s, M., and Acin, A. (2010b). Convergent relaxations of polynomial optimization problems with noncommuting variables. SIAM Journal on Optimization , 20(5):2157--2180

    2010

  162. [170]

    and Bareinboim, E

    Plecko, D. and Bareinboim, E. (2024). Causal fairness for outcome control. Advances in Neural Information Processing Systems , 36

    2024

  163. [171]

    Ple c ko, D., Bareinboim, E., et al. (2024). Causal fairness analysis: A causal toolkit for fair machine learning. Foundations and Trends in Machine Learning , 17(3):304--589

    2024

  164. [172]

    Pleiss, G., Raghavan, M., Wu, F., Kleinberg, J., and Weinberger, K. Q. (2017). On fairness and calibration. Advances in neural information processing systems , 30

    2017

  165. [173]

    and Sharmanska, V

    Quadrianto, N. and Sharmanska, V. (2017). Recycling privileged learning and distribution matching for fairness. Advances in neural information processing systems , 30

    2017

  166. [174]

    Quinn, A., Shorten, R., Corless, M., Boufelja, S., and Zhou, Q. (2023). Optimal transport in some fairness scenarios. presented at the plenary meeting of the AutoFair project in Athens on Wednesday, 10th May 2023. Available on-line

    2023

  167. [175]

    G., Vincenot, Q., and Loubes, J.-M

    Risser, L., Sanz, A. G., Vincenot, Q., and Loubes, J.-M. (2022). Tackling algorithmic bias in neural-network classifiers using W asserstein-2 regularization. Journal of Mathematical Imaging and Vision , 64(6):672--689

    2022

  168. [176]

    T., Lamba, H., and Ghani, R

    Rodolfa, K. T., Lamba, H., and Ghani, R. (2021). Empirical observation of negligible fairness--accuracy trade-offs in machine learning for public policy. Nature Machine Intelligence , 3(10):896--904

    2021

  169. [177]

    T., Recht, B., and Jordan, M

    Rolf, E., Worledge, T. T., Recht, B., and Jordan, M. (2021). Representation matters: Assessing the importance of subgroup allocations in training data. In Meila, M. and Zhang, T., editors, Proceedings of the 38th International Conference on Machine Learning , volume 139 of Pro...

    2021

  170. [178]

    Rubin, D. B. (1976). Inference and missing data. Biometrika , 63(3):581--592

    1976

  171. [179]

    and Rakhlin, A

    Sarkar, T. and Rakhlin, A. (2019). Near optimal finite time identification of arbitrary linear dynamical systems. In Chaudhuri, K. and Salakhutdinov, R., editors, Proceedings of the 36th International Conference on Machine Learning , volume 97 of Proceedings of Machine Learnin...

    2019

  172. [180]

    Sarkar, T., Rakhlin, A., and Dahleh, M. A. (2022). Finite time LTI system identification. J. Mach. Learn. Res. , 22(1)

    2022

  173. [181]

    R., Kalchbrenner, N., Goyal, A., and Bengio, Y

    Schölkopf, B., Locatello, F., Bauer, S., Ke, N. R., Kalchbrenner, N., Goyal, A., and Bengio, Y. (2021). Toward causal representation learning. Proceedings of the IEEE , 109(5):612--634

    2021

  174. [182]

    Segal, I. E. (1947). Irreducible representations of operator algebras. Bulletin of the American Mathematical Society , 53(2):73--88

    1947

  175. [183]

    and Cuturi, M

    Seguy, V. and Cuturi, M. (2015). Principal geodesic analysis for probability measures under the optimal transport metric. Advances in Neural Information Processing Systems , 28

    2015

  176. [184]

    Sewell, R., MacKay, D., and McLean, I. (2009). Probabilistic electoral methods, representative probability, and maximum entropy. Voting matters , 26:16--38

    2009

  177. [185]

    Sharifi-Malvajerdi, S., Kearns, M., and Roth, A. (2019). Average individual fairness: Algorithms, generalization and experiments. In Advances in Neural Information Processing Systems , pages 8240--8249

    2019

  178. [186]

    Shui, C., Chen, Q., Li, J., Wang, B., and Gagn \'e , C. (2022). Fair representation learning through implicit path alignment. In International Conference on Machine Learning , pages 20156--20175. PMLR

    2022

  179. [187]

    Simchowitz, M., Boczar, R., and Recht, B. (2019). Learning linear dynamical systems with semi-parametric least squares. In Conference on Learning Theory , pages 2714--2802. PMLR

    2019

  180. [188]

    I., and Recht, B

    Simchowitz, M., Mania, H., Tu, S., Jordan, M. I., and Recht, B. (2018). Learning without mixing: Towards a sharp analysis of linear system identification. In Conference On Learning Theory , pages 439--473

    2018

  181. [189]

    and Knopp, P

    Sinkhorn, R. and Knopp, P. (1967). Concerning nonnegative matrices and doubly stochastic matrices. Pacific Journal of Mathematics , 21(2):343--348

    1967

  182. [190]

    Sohoni, N., Dunnmon, J., Angus, G., Gu, A., and R \'e , C. (2020). No subclass left behind: Fine-grained robustness in coarse-grained classification problems. Advances in Neural Information Processing Systems , 33:19339--19352

    2020

  183. [191]

    Sommerfeld, M., Schrieber, J., Zemel, Y., and Munk, A. (2019). Optimal transport: Fast probabilistic approximation with exact solvers. J. Mach. Learn. Res. , 20(105):1--23

    2019

  184. [192]

    Sun, J., Jin, H., Yang, Z., and Su, L. (2024). Optimizing long-term efficiency and fairness in ride-hailing under budget constraint via joint order dispatching and driver repositioning. IEEE Transactions on Knowledge and Data Engineering

    2024

  185. [193]

    Sun, Y., Oymak, S., and Fazel, M. (2020). Finite sample system identification: Optimal rates and the role of regularization. In Learning for Dynamics and Control , pages 16--25. PMLR

    2020

  186. [194]

    Tangirala, A. K. (2014). Principles of system identification: theory and practice . Crc Press

    2014

  187. [195]

    Tran, C., Dinh, M., and Fioretto, F. (2021). Differentially private empirical risk minimization under the fairness lens. Advances in Neural Information Processing Systems , 34

    2021

  188. [196]

    Tsiamis, A., Matni, N., and Pappas, G. (2020). Sample complexity of K alman filtering for unknown systems. In Learning for Dynamics and Control , pages 435--444. PMLR

    2020

  189. [197]

    and Pappas, G

    Tsiamis, A. and Pappas, G. J. (2019). Finite sample analysis of stochastic system identification. In 2019 IEEE 58th Conference on Decision and Control (CDC) , pages 3648--3654. IEEE

    2019

  190. [198]

    and Pappas, G

    Tsiamis, A. and Pappas, G. J. (2022). Online learning of the K alman filter with logarithmic regret. IEEE Transactions on Automatic Control , pages 1--16

    2022

  191. [199]

    Tu, R., Zhang, C., Ackermann, P., Mohan, K., Kjellstr \"o m, H., and Zhang, K. (2019). Causal discovery in the presence of missing data. In The 22nd International Conference on Artificial Intelligence and Statistics , pages 1762--1770. PMLR

    2019

  192. [200]

    Tun c el, L. (2000). Potential reduction and primal-dual methods. In Handbook of semidefinite programming , pages 235--265. Springer

    2000

  193. [201]

    Ustun, B., Liu, Y., and Parkes, D. (2019). Fairness without harm: Decoupled classifiers with preference guarantees. In International Conference on Machine Learning , pages 6373--6382. PMLR

    2019

  194. [202]

    and Boyd, S

    Vandenberghe, L. and Boyd, S. (1996). Semidefinite programming. SIAM review , 38(1):49--95

    1996

  195. [203]

    and Binns, R

    Veale, M. and Binns, R. (2017). Fairer machine learning in the real world: Mitigating discrimination without collecting sensitive data. Big Data & Society , 4(2):2053951717743530

    2017

  196. [204]

    K., Brugere, I., Dutta, S., Mishler, A., and Garg, S

    Veldanda, A. K., Brugere, I., Dutta, S., Mishler, A., and Garg, S. (2023). Hyper-parameter tuning for fair classification without sensitive attribute access. arXiv preprint arXiv:2302.01385

    2023 arXiv

  197. [205]

    Villani, C. (2021). Topics in optimal transportation , volume 58. American Mathematical Soc

    2021

  198. [206]

    Vogel, R., Bellet, A., and Cl \'e men c on, S. (2021). Learning fair scoring functions: Bipartite ranking under roc-based fairness constraints. In International Conference on Artificial Intelligence and Statistics , pages 784--792. PMLR

    2021

  199. [207]

    and Magron, V

    Wang, J. and Magron, V. (2021a). Exploiting term sparsity in noncommutative polynomial optimization. Computational Optimization and Applications , 80:483--521

    2021

  200. [208]

    and Magron, V

    Wang, J. and Magron, V. (2021b). Exploiting term sparsity in noncommutative polynomial optimization. Computational Optimization and Applications , 80(2):483--521

    2021

  201. [209]

    Wang, J., Magron, V., and Lasserre, J.-B. (2021a). Chordal-tssos: a moment-sos hierarchy that exploits term sparsity with chordal extension. SIAM Journal on Optimization , 31(1):114--141

    2021

  202. [210]

    Wang, J., Magron, V., and Lasserre, J.-B. (2021b). Tssos: A moment-sos hierarchy that exploits term sparsity. SIAM Journal on optimization , 31(1):30--58

    2021

  203. [211]

    Wang, S., Guo, W., Narasimhan, H., Cotter, A., Gupta, M., and Jordan, M. (2020). Robust optimization for fairness with noisy protected groups. Advances in Neural Information Processing Systems , 33:5190--5203

    2020

  204. [212]

    Wen, M., Bastani, O., and Topcu, U. (2021). Algorithms for fairness in sequential decision making. In International Conference on Artificial Intelligence and Statistics , pages 1144--1152. PMLR

    2021

  205. [213]

    and Harrison, J

    West, M. and Harrison, J. (1997). Bayesian Forecasting and Dynamic Models (2nd ed.) . Springer-Verlag, Berlin, Heidelberg

    1997

  206. [214]

    Wittek, P. (2015). Algorithm 950: Ncpol2sdpa—sparse semidefinite programming relaxations for polynomial optimization problems of noncommuting variables. ACM Transactions on Mathematical Software (TOMS) , 41(3):1--12

    2015

  207. [215]

    Yan, S., Kao, H.-t., and Ferrara, E. (2020). Fair class balancing: Enhancing model fairness without observing sensitive attributes. In Proceedings of the 29th ACM International Conference on Information & Knowledge Management , pages 1715--1724

    2020

  208. [216]

    and Stoyanovich, J

    Yang, K. and Stoyanovich, J. (2016). Measuring fairness in ranked outputs

    2016

  209. [217]

    Yang, M., Sheng, J., Liu, W., Jin, B., Wang, X., and Wang, X. (2022). Obtaining dyadic fairness by optimal transport. In 2022 IEEE International Conference on Big Data (Big Data) , pages 4726--4732. IEEE

    2022

  210. [218]

    B., Valera, I., Gomez Rodriguez, M., and Gummadi, K

    Zafar, M. B., Valera, I., Gomez Rodriguez, M., and Gummadi, K. P. (2017a). Fairness beyond disparate treatment & disparate impact: Learning classification without disparate mistreatment. In Proceedings of the 26th international conference on world wide web , pages 1171--1180

    2017

  211. [219]

    B., Valera, I., Gomez-Rodriguez, M., and Gummadi, K

    Zafar, M. B., Valera, I., Gomez-Rodriguez, M., and Gummadi, K. P. (2019). Fairness constraints: A flexible approach for fair classification. The Journal of Machine Learning Research , 20(1):2737--2778

    2019

  212. [220]

    B., Valera, I., Rodriguez, M., Gummadi, K., and Weller, A

    Zafar, M. B., Valera, I., Rodriguez, M., Gummadi, K., and Weller, A. (2017b). From parity to preference-based notions of fairness in classification. Advances in neural information processing systems , 30

    2017

  213. [221]

    Zehlike, M., Yang, K., and Stoyanovich, J. (2022a). Fairness in ranking, part i: Score-based ranking. ACM Comput. Surv. , 55(6)

    2022

  214. [222]

    Zehlike, M., Yang, K., and Stoyanovich, J. (2022b). Fairness in ranking, part ii: Learning-to-rank and recommender systems. ACM Comput. Surv. , 55(6)

    2022

  215. [223]

    Zemel, R., Wu, Y., Swersky, K., Pitassi, T., and Dwork, C. (2013). Learning fair representations. In International conference on machine learning , pages 325--333. PMLR

    2013

  216. [224]

    and Gordon, G

    Zhao, H. and Gordon, G. J. (2022). Inherent tradeoffs in learning fair representations. J. Mach. Learn. Res. , 23(1)

    2022

  217. [225]

    Zhao, T., Dai, E., Shu, K., and Wang, S. (2022). Towards fair classifiers without sensitive attributes: Exploring biases in related features. In Proceedings of the Fifteenth ACM International Conference on Web Search and Data Mining , pages 1433--1442

    2022

  218. [226]

    and Mare c ek, J

    Zhou, Q. and Mare c ek, J. (2023). Group-blind optimal transport to group parity and its constrained variants. arXiv preprint arXiv:2310.11407

    2023 arXiv

  219. [227]

    Zliobaite, I. (2015). On the relation between accuracy and fairness in binary classification. In The 2nd workshop on Fairness, Accountability, and Transparency in Machine Learning (FATML) at ICML'15

    2015

Pith tools

Reviewed August 12, 2026 · model on record in the stance chip above.