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An Empirical Examination of the Evaluative AI Framework

arXiv 2411.08583 ↗First posted 13 Nov 2024cs.HC cs.AIcited by 0

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Abstract (arXiv)

This study empirically examines the "Evaluative AI" framework, which aims to enhance the decision-making process for AI users by transitioning from a recommendation-based approach to a hypothesis-driven one. Rather than offering direct recommendations, this framework presents users pro and con evidence for hypotheses to support more informed decisions. However, findings from the current behavioral experiment reveal no significant improvement in decision-making performance and limited user engagement with the evidence provided, resulting in cognitive processes similar to those observed in traditional AI systems. Despite these results, the framework still holds promise for further exploration in future research.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 32 of its 83 references that are in the map. First posted on arXiv 13 Nov 2024.

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References · 32

  1. 2017A Unified Approach to Interpreting Model Predictionscited by 6,488
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  3. 2018A Survey Of Methods For Explaining Black Box Modelscited by 970
  4. 2023Capabilities of GPT-4 on Medical Challenge Problemscited by 630
  5. 2020Does the Whole Exceed its Parts? The Effect of AI Explanations on Complementary Team Performancecited by 396
  6. 2020Effect of Confidence and Explanation on Accuracy and Trust Calibration in AI-Assisted Decision Makingcited by 281
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  8. 2018On Human Predictions with Explanations and Predictions of Machine Learning Models: A Case Study on Deception Detectioncited by 159
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  12. 2023Appropriate Reliance on AI Advice: Conceptualization and the Effect of Explanationscited by 80
20 more
  1. 2023Understanding the Role of Human Intuition on Reliance in Human-AI Decision-Making with Explanationscited by 73
  2. 2023Who Should I Trust: AI or Myself? Leveraging Human and AI Correctness Likelihood to Promote Appropriate Trust in AI-Assisted Decision-Makingcited by 67
  3. 2023Explainable AI is Dead, Long Live Explainable AI! Hypothesis-driven decision supportcited by 65
  4. 2023LLM-Assisted Content Analysis: Using Large Language Models to Support Deductive Codingcited by 64
  5. 2021Understanding the Effect of Out-of-distribution Examples and Interactive Explanations on Human-AI Decision Makingcited by 58
  6. 2022Do People Engage Cognitively with AI? Impact of AI Assistance on Incidental Learningcited by 53
  7. 2024Conformal Prediction Sets Improve Human Decision Makingcited by 35
  8. 2024Complementarity in Human-AI Collaboration: Concept, Sources, and Evidencecited by 35
  9. 2022A Meta-Analysis of the Utility of Explainable Artificial Intelligence in Human-AI Decision-Makingcited by 29
  10. 2023Selective Explanations: Leveraging Human Input to Align Explainable AIcited by 29
  11. 2019Assessing the Local Interpretability of Machine Learning Modelscited by 23
  12. 2020Explainable Active Learning (XAL): An Empirical Study of How Local Explanations Impact Annotator Experiencecited by 20
  13. 2023Impact Of Explainable AI On Cognitive Load: Insights From An Empirical Studycited by 15
  14. 2024Contrastive Explanations That Anticipate Human Misconceptions Can Improve Human Decision-Making Skillscited by 11
  15. 2023Explaining Model Confidence Using Counterfactualscited by 4
  16. 2023Evaluative Item-Contrastive Explanations in Rankingscited by 3
  17. 2024Beyond Recommendations: From Backward to Forward AI Support of Pilots' Decision-Making Processcited by 3
  18. 2023Explain To Decide: A Human-Centric Review on the Role of Explainable Artificial Intelligence in AI-assisted Decision Makingcited by 2
  19. 2024Visual Evaluative AI: A Hypothesis-Driven Tool with Concept-Based Explanations and Weight of Evidencecited by 1
  20. 2024Algorithm, Expert, or Both? Evaluating the Role of Feature Selection Methods on User Preferences and Reliancecited by 1

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