mlsanityData to 7 Oct 2026

Human-in-the-Loop Interpretability Prior

arXiv 1805.11571 ↗First posted 29 May 2018stat.ML cs.LGcited by 35

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Abstract (arXiv)

We often desire our models to be interpretable as well as accurate. Prior work on optimizing models for interpretability has relied on easy-to-quantify proxies for interpretability, such as sparsity or the number of operations required. In this work, we optimize for interpretability by directly including humans in the optimization loop. We develop an algorithm that minimizes the number of user studies to find models that are both predictive and interpretable and demonstrate our approach on several data sets. Our human subjects results show trends towards different proxy notions of interpretability on different datasets, which suggests that different proxies are preferred on different tasks.

Key facts. Cited by 35 papers in the map (data through 7 Oct 2026). First posted on arXiv 29 May 2018.

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Citing papers per year: 2018: 1; 2019: 0; 2020: 0; 2021: 13; 2022: 6; 2023: 7; 2024: 4; 2025: 2; 2026: 2.

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  1. 2018Scalable agent alignment via reward modeling: a research directioncited by 438
  2. 2022From Anecdotal Evidence to Quantitative Evaluation Methods: A Systematic Review on Evaluating Explainable AIcited by 299
  3. 2021HIVE: Evaluating the Human Interpretability of Visual Explanationscited by 105
  4. 2021Synthetic Benchmarks for Scientific Research in Explainable Machine Learningcited by 59
  5. 2021Taxonomy of Machine Learning Safety: A Survey and Primercited by 22
  6. 2021Self-Interpretable Model with TransformationEquivariant Interpretationcited by 21
  7. 2024On the Faithfulness of Vision Transformer Explanationscited by 19
  8. 2021Scrutinizing XAI using linear ground-truth data with suppressor variablescited by 18
  9. 2021Evaluating the Interpretability of Generative Models by Interactive Reconstructioncited by 16
  10. 2021Robust Semantic Interpretability: Revisiting Concept Activation Vectorscited by 15
  11. 2021Keep CALM and Improve Visual Feature Attributioncited by 15
  12. 2022POETREE: Interpretable Policy Learning with Adaptive Decision Treescited by 14
23 more
  1. 2021Interactive Dimensionality Reduction for Comparative Analysiscited by 12
  2. 2021Improving Human Sequential Decision-Making with Reinforcement Learningcited by 12
  3. 2023On the Interpretability of Part-Prototype Based Classifiers: A Human Centric Analysiscited by 11
  4. 2025Explainability in Practice: A Survey of Explainable NLP Across Various Domainscited by 11
  5. 2022Perspectives on Incorporating Expert Feedback into Model Updatescited by 7
  6. 2021A Review of Explainable Artificial Intelligence in Manufacturingcited by 5
  7. 2023A New Perspective on Evaluation Methods for Explainable Artificial Intelligence (XAI)cited by 5
  8. 2023Diagrammatization and Abduction to Improve AI Interpretability With Domain-Aligned Explanations for Medical Diagnosiscited by 4
  9. 2023Improving Prototypical Visual Explanations with Reward Reweighing, Reselection, and Retrainingcited by 3
  10. 2025Unraveling Human-AI Teaming: A Review and Outlookcited by 3
  11. 2023Good-looking but Lacking Faithfulness: Understanding Local Explanation Methods through Trend-based Testingcited by 2
  12. 2024A Sim2Real Approach for Identifying Task-Relevant Properties in Interpretable Machine Learningcited by 2
  13. 2024LCE: A Framework for Explainability of DNNs for Ultrasound Image Based on Concept Discoverycited by 2
  14. 2021Bandits for Learning to Explain from Explanationscited by 1
  15. 2022Enriching Artificial Intelligence Explanations with Knowledge Fragmentscited by 1
  16. 2024Automatic explanation of the classification of Spanish legal judgments in jurisdiction-dependent law categories with tree estimatorscited by 1
  17. 2021Towards Model-informed Precision Dosing with Expert-in-the-loop Machine Learningcited by 0
  18. 2022Debiased-CAM to mitigate systematic error with faithful visual explanations of machine learningcited by 0
  19. 2022Measuring algorithmic interpretability: A human-learning-based framework and the corresponding cognitive complexity scorecited by 0
  20. 2023Improving Opioid Use Disorder Risk Modelling through Behavioral and Genetic Feature Integrationcited by 0
  21. 2023Explanation-based Training with Differentiable Insertion/Deletion Metric-aware Regularizerscited by 0
  22. 2026Rules or Weights? Comparing User Understanding of Explainable AI Techniques with the Cognitive XAI-Adaptive Modelcited by 0
  23. 2026CoAX: Cognitive-Oriented Attribution eXplanation User Model of Human Understanding of AI Explanationscited by 0