Does the Whole Exceed its Parts? The Effect of AI Explanations on Complementary Team Performance
Many researchers motivate explainable AI with studies showing that human-AI team performance on decision-making tasks improves when the AI explains its recommendations. However, prior studies observed improvements from explanations only when the AI, alone, outperformed both the human and the best team. Can explanations help lead to complementary performance, where team accuracy is higher than either the human or the AI working solo? We conduct mixed-method user studies on three datasets, where an AI with accuracy comparable to humans helps participants solve a task (explaining itself in some conditions). While we observed complementary improvements from AI augmentation, they were not increased by explanations. Rather, explanations increased the chance that humans will accept the AI's recommendation, regardless of its correctness. Our result poses new challenges for human-centered AI: Can we develop explanatory approaches that encourage appropriate trust in AI, and therefore help generate (or improve) complementary performance?
Code (0)
등록된 구현이 없습니다.
Tasks
Decision MakingQuestion AnsweringSentiment AnalysisSimilar Papers 제목 키워드 기반
Advancing Post Hoc Case Based Explanation with Feature Highlighting
Explainable AI (XAI) has been proposed as a valuable tool to assist in downstream tasks involving human and AI collaboration. Perhaps the most psychologically valid XAI techniques are case based approaches which display …
validAlike Parts: A Feature-Informed Approach to Local and Global Prototype Explanations
Prototype-based explanations offer an intuitive, example-based approach to support the interpretability of machine learning black box classifiers but often lack feature-level granularity. We introduce a framework that in…
Feature ImportanceScaling Vision Models Does Not Consistently Improve Localisation-Based Explanation Quality
Artificial intelligence models are increasingly scaled to improve predictive accuracy, yet it remains unclear whether scale improves the quality of post-hoc explanations. We investigate this relationship by evaluating 11…
CausalX: Causal Explanations and Block Multilinear Factor Analysis
By adhering to the dictum, "No causation without manipulation (treatment, intervention)", cause and effect data analysis represents changes in observed data in terms of changes in the causal factors. When causal factors …
Computational EfficiencycounterfactualObjectObject RecognitionHuman Interpretation of Saliency-based Explanation Over Text
While a lot of research in explainable AI focuses on producing effective explanations, less work is devoted to the question of how people understand and interpret the explanation. In this work, we focus on this question …