# Explainable AI is Dead, Long Live Explainable AI! Hypothesis-driven decision support arXiv: https://arxiv.org/abs/2302.12389 ยท HTML: https://mlsanity.com/paper/2302.12389/ ## Key facts - Cited by 66 papers in the map (data through 14 Oct 2026). - Builds on 12 of its 66 references that are in the map. - First posted on arXiv 24 Feb 2023. ## Builds on (most cited first, 12 of 12) - [Explanation in Artificial Intelligence: Insights from the Social Sciences](https://mlsanity.com/paper/1706.07269/) (2017) - [Effect of Confidence and Explanation on Accuracy and Trust Calibration in AI-Assisted Decision Making](https://mlsanity.com/paper/2001.02114/) (2020) - [Manipulating and Measuring Model Interpretability](https://mlsanity.com/paper/1802.07810/) (2018) - [Explainable Reinforcement Learning Through a Causal Lens](https://mlsanity.com/paper/1905.10958/) (2019) - [Formalizing Trust in Artificial Intelligence: Prerequisites, Causes and Goals of Human Trust in AI](https://mlsanity.com/paper/2010.07487/) (2020) - [Human-Centered Explainable AI (XAI): From Algorithms to User Experiences](https://mlsanity.com/paper/2110.10790/) (2021) - [Contrastive Explanation: A Structural-Model Approach](https://mlsanity.com/paper/1811.03163/) (2018) - [Explanation in Human-AI Systems: A Literature Meta-Review, Synopsis of Key Ideas and Publications, and Bibliography for Explainable AI](https://mlsanity.com/paper/1902.01876/) (2019) - [Do People Engage Cognitively with AI? Impact of AI Assistance on Incidental Learning](https://mlsanity.com/paper/2202.05402/) (2022) - [Ignore, Trust, or Negotiate: Understanding Clinician Acceptance of AI-Based Treatment Recommendations in Health Care](https://mlsanity.com/paper/2302.00096/) (2023) - [Sensible AI: Re-imagining Interpretability and Explainability using Sensemaking Theory](https://mlsanity.com/paper/2205.05057/) (2022) - [From Human Explanation to Model Interpretability: A Framework Based on Weight of Evidence](https://mlsanity.com/paper/2104.13299/) (2021) ## Built on by (most cited first, 30 of 66) - [Explainable Artificial Intelligence (XAI) 2.0: A Manifesto of Open Challenges and Interdisciplinary Research Directions](https://mlsanity.com/paper/2310.19775/) (2023) - [Evaluating Language Models for Mathematics through Interactions](https://mlsanity.com/paper/2306.01694/) (2023) - [In Search of Verifiability: Explanations Rarely Enable Complementary Performance in AI-Advised Decision Making](https://mlsanity.com/paper/2305.07722/) (2023) - [Adversarial attacks and defenses in explainable artificial intelligence: A survey](https://mlsanity.com/paper/2306.06123/) (2023) - [Towards Human-AI Deliberation: Design and Evaluation of LLM-Empowered Deliberative AI for AI-Assisted Decision-Making](https://mlsanity.com/paper/2403.16812/) (2024) - [Argumentative Large Language Models for Explainable and Contestable Claim Verification](https://mlsanity.com/paper/2405.02079/) (2024) - [Is Conversational XAI All You Need? Human-AI Decision Making With a Conversational XAI Assistant](https://mlsanity.com/paper/2501.17546/) (2025) - [Physician Detection of Clinical Harm in Machine Translation: Quality Estimation Aids in Reliance and Backtranslation Identifies Critical Errors](https://mlsanity.com/paper/2310.16924/) (2023) - [A Systematic Review on Fostering Appropriate Trust in Human-AI Interaction](https://mlsanity.com/paper/2311.06305/) (2023) - [LLM-Generated Black-box Explanations Can Be Adversarially Helpful](https://mlsanity.com/paper/2405.06800/) (2024) - [Impact Of Explainable AI On Cognitive Load: Insights From An Empirical Study](https://mlsanity.com/paper/2304.08861/) (2023) - [Contrastive Explanations That Anticipate Human Misconceptions Can Improve Human Decision-Making Skills](https://mlsanity.com/paper/2410.04253/) (2024) - [Artificial Intelligence Should Genuinely Support Clinical Reasoning and Decision Making To Bridge the Translational Gap](https://mlsanity.com/paper/2506.05030/) (2025) - [Towards Optimizing Human-Centric Objectives in AI-Assisted Decision-Making With Offline Reinforcement Learning](https://mlsanity.com/paper/2403.05911/) (2024) - [One vs. Many: Comprehending Accurate Information from Multiple Erroneous and Inconsistent AI Generations](https://mlsanity.com/paper/2405.05581/) (2024) - [Contestable AI needs Computational Argumentation](https://mlsanity.com/paper/2405.10729/) (2024) - [AI, Help Me Thinkunicodex2014but for Myself: Assisting People in Complex Decision-Making by Providing Different Kinds of Cognitive Support](https://mlsanity.com/paper/2504.06771/) (2025) - [DiscipLink: Unfolding Interdisciplinary Information Seeking Process via Human-AI Co-Exploration](https://mlsanity.com/paper/2408.00447/) (2024) - [Toward Reliable Scientific Hypothesis Generation: Evaluating Truthfulness and Hallucination in Large Language Models](https://mlsanity.com/paper/2505.14599/) (2025) - [Detecting music deepfakes is easy but actually hard](https://mlsanity.com/paper/2405.04181/) (2024) - [Navigating Explanatory Multiverse Through Counterfactual Path Geometry](https://mlsanity.com/paper/2306.02786/) (2023) - [Beyond Recommender: An Exploratory Study of the Effects of Different AI Roles in AI-Assisted Decision Making](https://mlsanity.com/paper/2403.01791/) (2024) - [Epistemology gives a Future to Complementarity in Human-AI Interactions](https://mlsanity.com/paper/2601.09871/) (2026) - [Two-Stage Holistic and Contrastive Explanation of Image Classification](https://mlsanity.com/paper/2306.06339/) (2023) - [Forms of Understanding for XAI-Explanations](https://mlsanity.com/paper/2311.08760/) (2023) - [Evaluative Item-Contrastive Explanations in Rankings](https://mlsanity.com/paper/2312.10094/) (2023) - [Information That Matters: Exploring Information Needs of People Affected by Algorithmic Decisions](https://mlsanity.com/paper/2401.13324/) (2024) - [From Evidence to Decision: Exploring Evaluative AI](https://mlsanity.com/paper/2402.01292/) (2024) - [Beyond Recommendations: From Backward to Forward AI Support of Pilots' Decision-Making Process](https://mlsanity.com/paper/2406.08959/) (2024) - [What Did My Car Say? Impact of Autonomous Vehicle Explanation Errors and Driving Context On Comfort, Reliance, Satisfaction, and Driving Confidence](https://mlsanity.com/paper/2409.05731/) (2024) ## Abstract (arXiv) In this paper, we argue for a paradigm shift from the current model of explainable artificial intelligence (XAI), which may be counter-productive to better human decision making. In early decision support systems, we assumed that we could give people recommendations and that they would consider them, and then follow them when required. However, research found that people often ignore recommendations because they do not trust them; or perhaps even worse, people follow them blindly, even when the recommendations are wrong. Explainable artificial intelligence mitigates this by helping people to understand how and why models give certain recommendations. However, recent research shows that people do not always engage with explainability tools enough to help improve decision making. The assumption that people will engage with recommendations and explanations has proven to be unfounded. We argue this is because we have failed to account for two things. First, recommendations (and their explanations) take control from human decision makers, limiting their agency. Second, giving recommendations and explanations does not align with the cognitive processes employed by people making decisions. This position paper proposes a new conceptual framework called Evaluative AI for explainable decision support. This is a machine-in-the-loop paradigm in which decision support tools provide evidence for and against decisions made by people, rather than provide recommendations to accept or reject. We argue that this mitigates issues of over- and under-reliance on decision support tools, and better leverages human expertise in decision making.