mlsanityData to 7 Oct 2026

What can AI do for me: Evaluating Machine Learning Interpretations in Cooperative Play

arXiv 1810.09648 ↗First posted 23 Oct 2018cs.AIcited by 52

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Abstract (arXiv)

Machine learning is an important tool for decision making, but its ethical and responsible application requires rigorous vetting of its interpretability and utility: an understudied problem, particularly for natural language processing models. We propose an evaluation of interpretation on a real task with real human users, where the effectiveness of interpretation is measured by how much it improves human performance. We design a grounded, realistic human-computer cooperative setting using a question answering task, Quizbowl. We recruit both trivia experts and novices to play this game with computer as their teammate, who communicates its prediction via three different interpretations. We also provide design guidance for natural language processing human-in-the-loop settings.

Key facts. Cited by 52 papers in the map (data through 7 Oct 2026). First posted on arXiv 23 Oct 2018.

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Citing papers per year: 2021: 13; 2022: 13; 2023: 14; 2024: 8; 2025: 3; 2026: 1.

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  1. 2022Explanations Can Reduce Overreliance on AI Systems During Decision-Makingcited by 111
  2. 2024What Large Language Models Know and What People Think They Knowcited by 109
  3. 2021The effectiveness of feature attribution methods and its correlation with automatic evaluation scorescited by 87
  4. 2021Towards a Science of Human-AI Decision Making: A Survey of Empirical Studiescited by 85
  5. 2022Towards Human-centered Explainable AI: A Survey of User Studies for Model Explanationscited by 84
  6. 2021"Will You Find These Shortcuts?" A Protocol for Evaluating the Faithfulness of Input Salience Methods for Text Classificationcited by 66
  7. 2021Understanding the Effect of Out-of-distribution Examples and Interactive Explanations on Human-AI Decision Makingcited by 58
  8. 2021How can I choose an explainer? An Application-grounded Evaluation of Post-hoc Explanationscited by 58
  9. 2023Large Language Models Help Humans Verify Truthfulness -- Except When They Are Convincingly Wrongcited by 51
  10. 2023In Search of Verifiability: Explanations Rarely Enable Complementary Performance in AI-Advised Decision Makingcited by 50
  11. 2021Revisiting Methods for Finding Influential Examplescited by 39
  12. 2022Visual correspondence-based explanations improve AI robustness and human-AI team accuracycited by 37
38 more
  1. 2023Are Machine Rationales (Not) Useful to Humans? Measuring and Improving Human Utility of Free-Text Rationalescited by 37
  2. 2022Advancing Human-AI Complementarity: The Impact of User Expertise and Algorithmic Tuning on Joint Decision Makingcited by 33
  3. 2023Human-AI Collaboration: The Effect of AI Delegation on Human Task Performance and Task Satisfactioncited by 32
  4. 2023ConvXAI: Delivering Heterogeneous AI Explanations via Conversations to Support Human-AI Scientific Writingcited by 31
  5. 2021Teaching Humans When To Defer to a Classifier via Exemplarscited by 29
  6. 2022Square One Bias in NLP: Towards a Multi-Dimensional Exploration of the Research Manifoldcited by 29
  7. 2022The State of the Art in Enhancing Trust in Machine Learning Models with the Use of Visualizationscited by 29
  8. 2024A Decision Theoretic Framework for Measuring AI Reliancecited by 26
  9. 2022Human Interpretation of Saliency-based Explanation Over Textcited by 25
  10. 2022Machine Explanations and Human Understandingcited by 20
  11. 2023Effective Human-AI Teams via Learned Natural Language Rules and Onboardingcited by 20
  12. 2021Interpreting Deep Learning Models in Natural Language Processing: A Reviewcited by 19
  13. 2022Use-Case-Grounded Simulations for Explanation Evaluationcited by 19
  14. 2023Understanding the Effect of Counterfactual Explanations on Trust and Reliance on AI for Human-AI Collaborative Clinical Decision Makingcited by 18
  15. 2021Evaluating the Interpretability of Generative Models by Interactive Reconstructioncited by 16
  16. 2022Investigations of Performance and Bias in Human-AI Teamwork in Hiringcited by 16
  17. 2021Explainable Deep Learning in Healthcare: A Methodological Survey from an Attribution Viewcited by 15
  18. 2024Position: Towards Bidirectional Human-AI Alignmentcited by 15
  19. 2023Being Right for Whose Right Reasons?cited by 12
  20. 2023Using AI Uncertainty Quantification to Improve Human Decision-Makingcited by 11
  21. 2023What Else Do I Need to Know? The Effect of Background Information on Users' Reliance on QA Systemscited by 9
  22. 2024Decoding AI's Nudge: A Unified Framework to Predict Human Behavior in AI-assisted Decision Makingcited by 9
  23. 2024On Evaluating Explanation Utility for Human-AI Decision Making in NLPcited by 9
  24. 2024Comparing Zealous and Restrained AI Recommendations in a Real-World Human-AI Collaboration Taskcited by 8
  25. 2024Interactive Prompt Debugging with Sequence Saliencecited by 6
  26. 2023May I Ask a Follow-up Question? Understanding the Benefits of Conversations in Neural Network Explainabilitycited by 5
  27. 2021Explaining the Road Not Takencited by 3
  28. 2021A Framework for Evaluating Post Hoc Feature-Additive Explainerscited by 3
  29. 2023The Impact of Explanations on Fairness in Human-AI Decision-Making: Protected vs Proxy Featurescited by 3
  30. 2022Evaluating Deep Taylor Decomposition for Reliability Assessment in the Wildcited by 2
  31. 2022Responsibility: An Example-based Explainable AI approach via Training Process Inspectioncited by 2
  32. 2023Towards the Visualization of Aggregated Class Activation Maps to Analyse the Global Contribution of Class Featurescited by 2
  33. 2023How Well Do Feature-Additive Explainers Explain Feature-Additive Predictors?cited by 2
  34. 2024Unraveling the Dilemma of AI Errors: Exploring the Effectiveness of Human and Machine Explanations for Large Language Modelscited by 2
  35. 2026Incentive-Tuning: Understanding and Designing Incentives for Empirical Human-AI Decision-Making Studiescited by 2
  36. 2022Challenges in Explanation Quality Evaluationcited by 1
  37. 2025Adaptive Human-Agent Teaming: A Review of Empirical Studies from the Process Dynamics Perspectivecited by 1
  38. 2025Arguments that Alter Minds: LLM Rationales Sway Human (and LLM) Notions of Plausibilitycited by 1

50 most cited of 52 All 52 (JSON)