Pith. sign in

REVIEW 3 major objections 4 minor 39 references

AI-Spectra: A Visual Dashboard for Model Multiplicity to Enhance Informed and Transparent Decision-Making

T0 review · 3 major / 4 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 A dashboard of robot faces lets users compare many AI models at once and see where they agree or disagree.

desk verdict A clear design prototype for visualizing model multiplicity, but the central usability claim is untested and needs a user study before it can be taken as evidence. read the letter →

arxiv 2411.10490 v1 pith:S4PMH44G submitted 2024-11-14 cs.HC cs.AI

Gilles Eerlings , Sebe Vanbrabant , Jori Liesenborgs , Gustavo Rovelo Ruiz , Davy Vanacken , Kris Luyten This is my paper · ORCID
classification cs.HCcs.AI
keywords modelmultiplicityChernofffacesvisualanalyticshuman-AIinteractionRashomonsetMNISTtransparencydecisionsupport
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

AI-Spectra tries to make 'model multiplicity' usable: instead of embedding one AI model in an interface, run many equally valid models together and show users their results side by side. The paper argues that a custom Chernoff-face visualization, called Chernoff Bots, lets people quickly read each model's training background from its facial features and see at a glance which predictions the models agree on and which are outliers. This matters because divergent outputs from multiple models can correct over- and under-trust in AI, but a raw list of predictions would overwhelm users. The authors demonstrate the approach with classifiers trained on MNIST, showing that the dashboard surfaces consensus, disagreement, and model expertise in situations such as an inverted digit image.

What carries the argument

The machinery is the Chernoff Bot, a custom variant of Chernoff faces (graphical representations of points in k-dimensional space as faces) tailored to machine-learning metadata. Independent factors like number of hidden layers, dropout, activation function, batch size, and outlier percentage are mapped to eyes, body holes, antenna color, teeth lines, and antenna curvature, while dataset-dependent factors such as translation, rotation, contrast, and inversion are mapped to pupil positions, mouth rotation, ear-piece contrast, and eye or pupil color. The bot is embedded as one stacked bar element per model, so the dashboard's central operation is reading a face to recover a model's configuration and reading a bar height to recover consensus among models.

What would settle it

A controlled user study comparing AI-Spectra against a plain stacked bar chart with simple color or shape legends on tasks such as locating the model trained on the most inverted images or identifying which label has the strongest consensus; if users are not faster or more accurate with Chernoff Bots, the central claim of quick, low-effort interpretation collapses.

Watch

Extended reading notes

Core claim

The central claim is that model multiplicity can be turned into a visual dashboard that preserves the variety of model opinions instead of averaging them away. Each AI model is represented by a Chernoff Bot whose antenna curvature, number of eyes, body holes, teeth lines, mouth rotation, and colors encode hyperparameters, training-data variations, and training-process choices, so the user can see what kind of model produced a given prediction. Bots are stacked into a bar chart, one bar per predicted class, so the height of each bar shows how many models support that label while the faces show who those models are. The authors argue this supports the Human-AI Interaction guidelines most relevant to multiplicity: identifying biased models, investigating outcomes produced by various models, comparing similar models that behave differently, and reflecting on individual model outputs. A validation on MNIST digit recognition illustrates the technique rather than proving its effect on users.

Load-bearing premise

The load-bearing premise is that users will actually read Chernoff Bots accurately and effortlessly: the specific mapping from hyperparameters to facial features is assumed to be intuitive, but no user study measures comprehension speed, accuracy, or decision quality.

Editorial extensions

If this is right

  • Users can identify at a glance which predictions are supported by many models and which by only a few, making consensus an explicit cue for reliability.
  • By reading facial features, users can recognize whether the models behind a prediction were trained on relevant data, such as inverted samples, and adjust their trust accordingly.
  • The dashboard gives model multiplicity a concrete UI form, moving from single-model outputs to a council of experts without hiding disagreement behind an average.
  • Because the representation conveys metadata rather than internal logic, it extends to black-box classifiers that cannot be explained by conventional means.

Reading between the lines

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

  • This is an editorial inference: whether Chernoff Bots actually lower cognitive effort is untested, and a controlled comparison against simple bar charts with color or symbol encodings would settle it.
  • Another inference: consistent patterns of disagreement across models could become a tool for auditing data quality, since they may flag problematic training subsets.
  • A further inference: the mapping from hyperparameters to facial features is arbitrary; a user study that tracks misreadings could drive a more principled encoding, perhaps even a learned or standardized one.
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

3 major / 4 minor

Summary. The paper introduces AI-Spectra, a process and visual dashboard for model multiplicity. The process trains multiple classifier variants on the same task and groups them into Rashomon sets; the dashboard encodes each model's hyperparameters and data-augmentation settings as a customized Chernoff face called a Chernoff Bot, and arranges the faces as a bar chart grouped by predicted label. The central claim is that this visualization allows users to quickly interpret complex, multivariate model configurations and compare predictions across multiple models while minimizing cognitive effort. Validation consists of training a large number of MNIST neural-network classifiers and presenting example dashboard screenshots.

Significance. If the usability claim held, AI-Spectra would address a real gap in the visualization of model multiplicity: most prior tools support model selection or interpretability, not comparison of a council of equally accurate black-box models. The paper gives a concrete design, grounded in Amershi et al.'s Human-AI Interaction guidelines, and a concrete model-training pipeline. However, the central claim is empirical and untested: no user study measures comprehension, speed, or decision quality, and no baseline comparison is provided. The significance is therefore conditional on a future evaluation; as presented, the contribution is a design proposal with a feasibility demonstration.

major comments (3)
  1. [Section 5, Figures 3–4] The validation consists solely of training MNIST classifiers and showing example dashboard screenshots. There is no user study measuring comprehension speed, accuracy, or decision quality, so the abstract's claim that Chernoff Bots 'let users quickly interpret' model configurations and predictions is unsupported. The cognitive-science premise in Section 4.3 (innate facial processing) does not directly establish that this particular mapping is readable, especially because many mappings in Table 1 are arbitrary (e.g., batch size to number of teeth lines, activation function to antenna color).
  2. [Section 4.4] The paper asserts that the dashboard reduces cognitive effort, but it provides no comparison to any baseline, such as a tabular listing of the same metadata or a conventional bar chart with color encoding. Without a baseline, the specific added value of Chernoff Bots over simpler encodings cannot be assessed, and the contribution as formulated in contributions (2) and (3) remains unverified.
  3. [Section 4.3] The sentence 'Chernoff faces guarantee the presence of positive hedonic aspects for human users' is an empirical overclaim presented without citation or experiment. At most, the design can be said to aim at leveraging familiar facial processing; framing this as a guarantee weakens the paper's rigor and should be revised to a hypothesis or design rationale.
minor comments (4)
  1. [Section 3] The process description for forming Rashomon sets is underspecified: the text does not state how many models were trained, what accuracy threshold was used to eliminate 'bad learners', or how large the resulting Rashomon sets were, which hinders reproducibility.
  2. [Section 6] The limitations section candidly discusses computational scalability and the black-box scope, but it does not acknowledge the absence of a user evaluation, which is the most immediate threat to the central usability claim.
  3. [Abstract and headings] There are several typographical issues: 'well know practices' should be 'well-known practices', and the table headers 'T able 1' and 'T able 2' contain a spurious space.
  4. [Section 4.4] The dashboard's visual scalability is not addressed: when many models support a given label, the corresponding bar becomes tall and individual Chernoff Bot faces become small and difficult to distinguish, so the paper should discuss how users are expected to compare faces within a large bar.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the paper is a design and demonstration contribution with no derivation that reduces to its own inputs.

full rationale

This paper is a design and demonstration contribution, not a predictive derivation. The central artifact is a visual encoding (Chernoff Bots) and a dashboard; Table 1 defines the mapping from model metadata to facial features, and the subsequent figures demonstrate the mapping on MNIST models. No quantity is fitted and then renamed as a prediction, and no uniqueness theorem is imported from the authors' prior work. The self-citations that appear (e.g., Refs. [13] and [14]) are background references on trust and intelligibility in interactive systems, and they are not load-bearing for the paper's central contribution. The claim that users can 'quickly interpret complex, multivariate model configurations' is an untested design assertion; that is an empirical-validity and evaluation concern, not a circularity. The paper does not define interpretability in terms of the visualization itself, nor does it use the visualization to prove its own effectiveness. The validation section trains a variety of MNIST models and shows example outputs, but this is a pipeline demonstration, not a claim that reduces to the input configurations by construction. Therefore, the circularity score is 0.

Assumptions & free parameters 0 free parameters · 4 assumptions · 1 invented entities

The paper does not fit any parameters in a statistical sense; the dashboard is a design artifact. Its central claim rests on domain assumptions about model multiplicity and Chernoff faces, plus an ad hoc feature mapping, none of which are empirically tested here.

assumptions (4)
  • domain assumption Model multiplicity (the Rashomon effect) is a real and important phenomenon for trust and transparency.
    Motivation relies on prior work by Black et al. and Breiman; the paper does not test whether its trained MNIST models form Rashomon sets with materially different internal logic.
  • domain assumption Chernoff faces are effective mnemonic devices for multidimensional data.
    Section 4.3 asserts this based on the 1973 and 1981 papers, treating it as established rather than validating it for the AI-Spectra context.
  • ad hoc to paper The mapping of hyperparameters to facial features in Table 1 is intuitive and does not mislead users.
    This mapping is defined for this project without user testing or perceptual justification.
  • domain assumption The Amershi et al. Human-AI Interaction guidelines are appropriate and complete for this dashboard design.
    Section 4.2 derives design decisions from these guidelines without evaluating competing frameworks.
invented entities (1)
  • Chernoff Bot
    purpose: Robot-face glyph that encodes an AI model's hyperparameters, training data variations, and validation set usage for visual comparison.
    This visual encoding is new to this paper; no external evidence shows it improves comprehension. It is demonstrated only in example figures.

how reviews work

0 comments
Cite this review

Pith. "Pith review of AI-Spectra: A Visual Dashboard for Model Multiplicity to Enhance Informed and Transparent Decision-Making." pith.science (2026). https://pith.science/paper/S4PMH44G

@misc{pith2026241110490,
  author       = {Pith},
  title        = {Pith review of: AI-Spectra: A Visual Dashboard for Model Multiplicity to Enhance Informed and Transparent Decision-Making},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/S4PMH44G}},
  note         = {Machine review of arXiv:2411.10490}
}
read the original abstract

We present an approach, AI-Spectra, to leverage model multiplicity for interactive systems. Model multiplicity means using slightly different AI models yielding equally valid outcomes or predictions for the same task, thus relying on many simultaneous "expert advisors" that can have different opinions. Dealing with multiple AI models that generate potentially divergent results for the same task is challenging for users to deal with. It helps users understand and identify AI models are not always correct and might differ, but it can also result in an information overload when being confronted with multiple results instead of one. AI-Spectra leverages model multiplicity by using a visual dashboard designed for conveying what AI models generate which results while minimizing the cognitive effort to detect consensus among models and what type of models might have different opinions. We use a custom adaptation of Chernoff faces for AI-Spectra; Chernoff Bots. This visualization technique lets users quickly interpret complex, multivariate model configurations and compare predictions across multiple models. Our design is informed by building on established Human-AI Interaction guidelines and well know practices in information visualization. We validated our approach through a series of experiments training a wide variation of models with the MNIST dataset to perform number recognition. Our work contributes to the growing discourse on making AI systems more transparent, trustworthy, and effective through the strategic use of multiple models.

Figures

Figures reproduced from arXiv: 2411.10490 by the authors.

Figure 1
Figure 1. A process for using and integrating model multiplicity, highlighting two stages: training and preparation (blue) and usage (red). This process outlines the steps from model requirements and data preprocessing through variations in training datasets and hyperparameters, ultimately resulting in multiple models. From these models, Rashomon sets are identified, allowing comparison and selection for predictions. While no… view at source ↗
Figure 1
Figure 1. variations in training data and differing model configurations. Since the [PITH_FULL_IMAGE:figures/full_fig_p009_1.png] view at source ↗
Figure 2
Figure 2. Example of a Chernoff Bot, featuring callouts describing various design aspects. Each variation referenced in [PITH_FULL_IMAGE:figures/full_fig_p010_2.png] view at source ↗
Figures from the paper (2 more)
Figure 3
Figure 3. Figure 3: An example bar chart using Chernoff Bots to represent each predicted sample. The x-axis shows the possible labels for the task. The height of each bar indicates the frequency at which each label was predicted so that users can get an immediate impression of the most ag…
Figure 4
Figure 4. Figure 4: Visualization of model multiplicity predictions on a regular and inverted MNIST sample. The top section shows multiple models predicting the digit “3” from a regular MNIST image, where they all agree on the prediction. The middle section illustrates the same models pre…

Discussion (0). Continue with ORCID to comment.

Reference graph

Works this paper leans on

39 extracted references · 21 canonical work pages

  1. [1]

    In: 2019 IEEE/ACM 41st International Conference on Software Engineering: Software Engineering in Practice (ICSE-SEIP)

    Amershi, S., Begel, A., Bird, C., DeLine, R., Gall, H., Kamar, E., Na- gappan, N., Nushi, B., Zimmermann, T.: Software engineering for machine learning: A case study. In: 2019 IEEE/ACM 41st International Conference on Software Engineering: Software Engineering in Practice (ICSE-SEIP). pp. 291–300 (2019).https://doi.org/10.1109/ICSE-SEIP.2019.00042

    arXiv 2019

  2. [2]

    In: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems

    Amershi, S., Fogarty, J., Kapoor, A., Tan, D.: Examining multiple poten- tial models in end-user interactive concept learning. In: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems. pp. 1357– 1360 (2010)

    work page 2010

  3. [3]

    In: Proceed- ings of the 2019 CHI Conference on Human Factors in Computing Sys- tems

    Amershi, S., Weld, D., Vorvoreanu, M., Fourney, A., Nushi, B., Collis- son, P., Suh, J., Iqbal, S., Bennett, P.N., Inkpen, K., Teevan, J., Kikin- Gil, R., Horvitz, E.: Guidelines for human-ai interaction. In: Proceed- ings of the 2019 CHI Conference on Human Factors in Computing Sys- tems. p. 1–13. CHI ’19, Association for Computing Machinery, New York, N...

    arXiv 2019

  4. [4]

    In: Pro- ceedings of the 7th International Conference on Human Computer Inter- action with Mobile Devices & Services

    Antifakos, S., Kern, N., Schiele, B., Schwaninger, A.: Towards improving trust in context-aware systems by displaying system confidence. In: Pro- ceedings of the 7th International Conference on Human Computer Inter- action with Mobile Devices & Services. p. 9. ACM Press (2005).https: //doi.org/10.1145/1085777.1085780

    work page arXiv 2005

  5. [5]

    Journal of medical Internet research 22(6), e15154 (2020)

    Asan, O., Bayrak, A.E., Choudhury, A.: Artificial intelligence and human trust in healthcare: focus on clinicians. Journal of medical Internet research 22(6), e15154 (2020)

    work page 2020

  6. [6]

    In: Neural networks: Tricks of the trade: Second edition, pp

    Bengio, Y.: Practical recommendations for gradient-based training of deep architectures. In: Neural networks: Tricks of the trade: Second edition, pp. 437–478. Springer (2012)

    work page 2012

  7. [7]

    In: 2022 ACM Conference on Fairness, Account- ability, and Transparency

    Black, E., Raghavan, M., Barocas, S.: Model multiplicity: Opportunities, concerns, and solutions. In: 2022 ACM Conference on Fairness, Account- ability, and Transparency. FAccT ’22, ACM (Jun 2022).https://doi.org/ 10.1145/3531146.3533149

    arXiv 2022

  8. [8]

    Statistical Science16(3) (Aug 2001)

    Breiman, L.: Statistical modeling: The two cultures (with comments and a rejoinder by the author). Statistical Science16(3) (Aug 2001). https: //doi.org/10.1214/ss/1009213726, http://dx.doi.org/10.1214/ss/ 1009213726

    arXiv 2001

Show all 39 references
  1. [9]

    Advances in Neural Informa- tion Processing Systems35, 7810–7823 (2022)

    Brunet, M.E., Anderson, A., Zemel, R.: Implications of model indetermi- nacy for explanations of automated decisions. Advances in Neural Informa- tion Processing Systems35, 7810–7823 (2022)

    2022

  2. [10]

    In: Proceedings of the 21th ACM SIGKDD international con- ference on knowledge discovery and data mining

    Caruana, R., Lou, Y., Gehrke, J., Koch, P., Sturm, M., Elhadad, N.: Intelli- gible models for healthcare: Predicting pneumonia risk and hospital 30-day readmission. In: Proceedings of the 21th ACM SIGKDD international con- ference on knowledge discovery and data mining. pp. 17...

    2015

  3. [11]

    Journal of the American Statistical Association68(342), 361– 368 (1973), http://www.jstor.org/stable/2284077

    Chernoff, H.: The use of faces to represent points in k-dimensional space graphically. Journal of the American Statistical Association68(342), 361– 368 (1973), http://www.jstor.org/stable/2284077

    1973

  4. [12]

    In: 2017 International Joint Conference on Neu- ral Networks (IJCNN)

    Cohen, G., Afshar, S., Tapson, J., van Schaik, A.: Emnist: Extending mnist to handwritten letters. In: 2017 International Joint Conference on Neu- ral Networks (IJCNN). pp. 2921–2926 (2017).https://doi.org/10.1109/ IJCNN.2017.7966217

    2017

  5. [13]

    In: Proceedings of the 2018 CHI Conference on Hu- man Factors in Computing Systems - CHI ’18

    Coppers, S., Van den Bergh, J., Luyten, K., Coninx, K., van der Lek-Ciudin, I., Vanallemeersch, T., Vandeghinste, V.: Intellingo: An Intelligible Trans- lation Environment. In: Proceedings of the 2018 CHI Conference on Hu- man Factors in Computing Systems - CHI ’18. pp. 1–13. ...

    2018

  6. [14]

    Coppers, S., Vanacken, D., Luyten, K.: Fortniot: Intelligible predictions to improve user understanding of smart home behavior. Proc. ACM Interact. Mob. Wearable Ubiquitous Technol.4(4) (dec 2020). https://doi.org/ 10.1145/3432225

    2020 doi

  7. [15]

    IEEETransactionsonVisualizationandComputerGraphics 24(1),382–391 (2018)

    El-Assady, M., Sevastjanova, R., Sperrle, F., Keim, D., Collins, C.: Progres- sive learning of topic modeling parameters: A visual analytics framework. IEEETransactionsonVisualizationandComputerGraphics 24(1),382–391 (2018). https://doi.org/10.1109/TVCG.2017.2745080

    2018

  8. [16]

    In: 2011 IEEE Conference on Visual Analytics Science and Technology (VAST)

    van den Elzen, S., van Wijk, J.J.: Baobabview: Interactive construction and analysis of decision trees. In: 2011 IEEE Conference on Visual Analytics Science and Technology (VAST). pp. 151–160 (2011).https://doi.org/ 10.1109/VAST.2011.6102453

    2011

  9. [17]

    https://doi.org/10.48550/ARXIV

    Fisher, A., Rudin, C., Dominici, F.: All models are wrong, but many are use- ful: Learning a variable’s importance by studying an entire class of predic- tion models simultaneously (2018). https://doi.org/10.48550/ARXIV. 1801.01489, https://arxiv.org/abs/1801.01489

    2018 doi arXiv

  10. [18]

    Journal of the American Statistical Asso- ciation 76(376), 757–765 (1981)

    Flury, B., Riedwyl, H.: Graphical representation of multivariate data by means of asymmetrical faces. Journal of the American Statistical Asso- ciation 76(376), 757–765 (1981). https://doi.org/10.1080/01621459. 1981.10477718

    1981

  11. [19]

    In: Proceedings of the sigchi conference on human factors in computing systems

    Fogarty, J., Tan, D., Kapoor, A., Winder, S.: Cueflik: interactive concept learning in image search. In: Proceedings of the sigchi conference on human factors in computing systems. pp. 29–38 (2008)

    2008

  12. [20]

    2024 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV) pp

    Ganesh, P.: An empirical investigation into benchmarking model multi- plicity for trustworthy machine learning: A case study on image classifi- cation. 2024 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV) pp. 4476–4485 (2023), https://api.semanticscholar. ...

    2023

  13. [21]

    In: Computer Graphics Forum

    Gleicher, M., Barve, A., Yu, X., Heimerl, F.: Boxer: Interactive comparison of classifier results. In: Computer Graphics Forum. vol. 39, pp. 181–193. Wiley Online Library (2020)

    2020

  14. [22]

    Keskar, N.S., Mudigere, D., Nocedal, J., Smelyanskiy, M., Tang, P.T.P.: On large-batch training for deep learning: Generalization gap and sharp 20 Eerlings G., Vanbrabant S., et al. minima. In: International Conference on Learning Representations (2017), https://openreview.net...

    2017

  15. [23]

    Proceedings of the IEEE86(11), 2278–2324 (1998)

    Lecun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning ap- plied to document recognition. Proceedings of the IEEE86(11), 2278–2324 (1998). https://doi.org/10.1109/5.726791

    1998 doi

  16. [24]

    Visual In- formatics 4(2), 122–131 (2020)

    Li, Y., Fujiwara, T., Choi, Y.K., Kim, K.K., Ma, K.L.: A visual analytics system for multi-model comparison on clinical data predictions. Visual In- formatics 4(2), 122–131 (2020). https://doi.org/https://doi.org/10. 1016/j.visinf.2020.04.005, pacificVis 2020 Workshop on Visua...

    2020

  17. [25]

    Queue16(3), 31–57 (Jun 2018)

    Lipton, Z.C.: The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue16(3), 31–57 (Jun 2018). https://doi.org/10.1145/3236386.3241340, https: //doi.org/10.1145/3236386.3241340

    2018

  18. [26]

    In: 2008 Eighth IEEE International Conference on Data Mining

    Liu, F.T., Ting, K.M., Zhou, Z.H.: Isolation forest. In: 2008 Eighth IEEE International Conference on Data Mining. pp. 413–422 (2008).https:// doi.org/10.1109/ICDM.2008.17

    2008 doi

  19. [27]

    In: Proceedings of the 31st International Conference on Neural In- formation Processing Systems

    Lundberg, S.M., Lee, S.I.: A unified approach to interpreting model predic- tions. In: Proceedings of the 31st International Conference on Neural In- formation Processing Systems. p. 4768–4777. NIPS’17, Curran Associates Inc., Red Hook, NY, USA (2017)

    2017

  20. [28]

    Theoretical issues in the study of trust and human intervention in automated systems

    Muir, B.M.: Trust in automation: Part I. Theoretical issues in the study of trust and human intervention in automated systems. Ergonomics37(11), 1905–1922 (Nov 1994). https://doi.org/10.1080/00140139408964957, http://www.tandfonline.com/doi/abs/10.1080/00140139408964957

    1905 doi

  21. [29]

    IEEE Computer Graphics and Applications39(5), 47–59 (2019)

    Murugesan, S., Malik, S., Du, F., Koh, E., Lai, T.M.: Deepcompare: Vi- sual and interactive comparison of deep learning model performance. IEEE Computer Graphics and Applications39(5), 47–59 (2019). https://doi. org/10.1109/MCG.2019.2919033

    2019

  22. [30]

    IEEE Transactions on Vi- sualization and Computer Graphics24(1), 174–183 (2018).https://doi

    Mühlbacher, T., Linhardt, L., Möller, T., Piringer, H.: Treepod: Sensitivity- aware selection of pareto-optimal decision trees. IEEE Transactions on Vi- sualization and Computer Graphics24(1), 174–183 (2018).https://doi. org/10.1109/TVCG.2017.2745158

    2018

  23. [31]

    whyshoulditrustyou?

    Ribeiro,M.T.,Singh,S.,Guestrin,C.:"whyshoulditrustyou?":Explaining the predictions of any classifier. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. p. 1135–1144. KDD ’16, Association for Computing Machinery, New York, N...

    2016

  24. [32]

    Nature – Na- ture Machine Intelligence1, 206–215 (2019)

    Rudin, C.: Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead. Nature – Na- ture Machine Intelligence1, 206–215 (2019). https://doi.org/10.1038/ s42256-019-0048-x

    2019

  25. [33]

    Cambridge University Press (2008) AI-Spectra: A Visual Dashboard for Model Multiplicity 21

    Shoham, Y., Leyton-Brown, K.: Multiagent Systems: Algorithmic, Game- Theoretic, and Logical Foundations. Cambridge University Press (2008) AI-Spectra: A Visual Dashboard for Model Multiplicity 21

    2008

  26. [35]

    Scientific Reports14(1), 1670 (2024)

    Uddin, S., Lu, H.: Dataset meta-level and statistical features affect machine learning performance. Scientific Reports14(1), 1670 (2024)

    2024

  27. [36]

    In: Proceed- ings of the 2023 CHI Conference on Human Factors in Computing Sys- tems

    Wang, Z.J., Wortman Vaughan, J., Caruana, R., Chau, D.H.: Gam coach: Towards interactive and user-centered algorithmic recourse. In: Proceed- ings of the 2023 CHI Conference on Human Factors in Computing Sys- tems. CHI ’23, Association for Computing Machinery, New York, NY, US...

    2023

  28. [37]

    In: 2022 IEEE Visualization Confer- ence (VIS) (2022)

    Wang, Z.J., Zhong, C., Xin, R., Takagi, T., Chen, Z., Chau, D.H., Rudin, C., Seltzer, M.: TimberTrek: Exploring and Curating Trustworthy Decision Trees with Interactive Visualization. In: 2022 IEEE Visualization Confer- ence (VIS) (2022)

    2022

  29. [38]

    IEEE Transactions on Visualization and Computer Graphics26(1), 56–65 (2020)

    Wexler, J., Pushkarna, M., Bolukbasi, T., Wattenberg, M., Viégas, F., Wil- son, J.: The what-if tool: Interactive probing of machine learning models. IEEE Transactions on Visualization and Computer Graphics26(1), 56–65 (2020). https://doi.org/10.1109/TVCG.2019.2934619

    2020

  30. [39]

    Yang, F., Huang, Z., Scholtz, J., Arendt, D.L.: How do visual explanations foster end users’ appropriate trust in machine learning? In: Proceedings of the 25th International Conference on Intelligent User Interfaces. pp. 189–

  31. [201]

    https://doi.org/10.1145/3377325.3377480

    IUI ’20, Association for Computing Machinery, Cagliari, Italy (Mar 2020). https://doi.org/10.1145/3377325.3377480

    2020

Pith tools

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