mlsanityData to 7 Oct 2026

GAM Coach: Towards Interactive and User-centered Algorithmic Recourse

arXiv 2302.14165 ↗First posted 27 Feb 2023cs.LG cs.AI cs.HCcited by 9

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Abstract (arXiv)

Machine learning (ML) recourse techniques are increasingly used in high-stakes domains, providing end users with actions to alter ML predictions, but they assume ML developers understand what input variables can be changed. However, a recourse plan's actionability is subjective and unlikely to match developers' expectations completely. We present GAM Coach, a novel open-source system that adapts integer linear programming to generate customizable counterfactual explanations for Generalized Additive Models (GAMs), and leverages interactive visualizations to enable end users to iteratively generate recourse plans meeting their needs. A quantitative user study with 41 participants shows our tool is usable and useful, and users prefer personalized recourse plans over generic plans. Through a log analysis, we explore how users discover satisfactory recourse plans, and provide empirical evidence that transparency can lead to more opportunities for everyday users to discover counterintuitive patterns in ML models. GAM Coach is available at: https://poloclub.github.io/gam-coach/.

Key facts. Cited by 9 papers in the map (data through 7 Oct 2026). Builds on 49 of its 107 references that are in the map. First posted on arXiv 27 Feb 2023.

Builds on

49 of 107 references.

References · 49

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37 more
  1. 2021Polyjuice: Generating Counterfactuals for Explaining, Evaluating, and Improving Modelscited by 219
  2. 2019The What-If Tool: Interactive Probing of Machine Learning Modelscited by 207
  3. 2019Model-Agnostic Counterfactual Explanations for Consequential Decisionscited by 204
  4. 2019Preserving Causal Constraints in Counterfactual Explanations for Machine Learning Classifierscited by 143
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Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 1; 2024: 4; 2025: 3; 2026: 1.

9 papers.

  1. 2024MeMemo: On-device Retrieval Augmentation for Private and Personalized Text Generationcited by 12
  2. 2024Exploiting Preference Elicitation in Interactive and User-centered Algorithmic Recourse: An Initial Explorationcited by 8
  3. 2023Prediction without Preclusion: Recourse Verification with Reachable Setscited by 4
  4. 2024AI-Spectra: A Visual Dashboard for Model Multiplicity to Enhance Informed and Transparent Decision-Makingcited by 4
  5. 2024Reassessing Evaluation Functions in Algorithmic Recourse: An Empirical Study from a Human-Centered Perspectivecited by 3
  6. 2026From Universal to Individualized Actionability: Revisiting Personalization in Algorithmic Recoursecited by 1
  7. 2025Evaluating Simplification Algorithms for Interpretability of Time Series Classificationcited by 0
  8. 2025UGCE: User-Guided Incremental Counterfactual Explorationcited by 0
  9. 2025Designing User-Centric Metrics for Evaluation of Counterfactual Explanationscited by 0