mlsanityData to 13 Oct 2026

Can AI Explanations Make You Change Your Mind?

arXiv 2508.08158 ↗First posted 11 Aug 2025cs.HC cs.AIcited by 2

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Abstract (arXiv)

In the context of AI-based decision support systems, explanations can help users to judge when to trust the AI's suggestion, and when to question it. In this way, human oversight can prevent AI errors and biased decision-making. However, this rests on the assumption that users will consider explanations in enough detail to be able to catch such errors. We conducted an online study on trust in explainable DSS, and were surprised to find that in many cases, participants spent little time on the explanation and did not always consider it in detail. We present an exploratory analysis of this data, investigating what factors impact how carefully study participants consider AI explanations, and how this in turn impacts whether they are open to changing their mind based on what the AI suggests.

Key facts. Cited by 2 papers in the map (data through 13 Oct 2026). Builds on 12 of its 32 references that are in the map. First posted on arXiv 11 Aug 2025.

Builds on

12 of 32 references.

References · 12

  1. 2019Explainable Artificial Intelligence (XAI): Concepts, Taxonomies, Opportunities and Challenges toward Responsible AIcited by 1,547
  2. 2018Metrics for Explainable AI: Challenges and Prospectscited by 258
  3. 2020Questioning the AI: Informing Design Practices for Explainable AI User Experiencescited by 252
  4. 2020Formalizing Trust in Artificial Intelligence: Prerequisites, Causes and Goals of Human Trust in AIcited by 166
  5. 2018On Human Predictions with Explanations and Predictions of Machine Learning Models: A Case Study on Deception Detectioncited by 159
  6. 2020Proxy Tasks and Subjective Measures Can Be Misleading in Evaluating Explainable AI Systemscited by 138
  7. 2021Reframing Human-AI Collaboration for Generating Free-Text Explanationscited by 125
  8. 2017Explainable AI: Beware of Inmates Running the Asylum Or: How I Learnt to Stop Worrying and Love the Social and Behavioural Sciencescited by 84
  9. 2020Does Explainable Artificial Intelligence Improve Human Decision-Making?cited by 49
  10. 2023Knowing About Knowing: An Illusion of Human Competence Can Hinder Appropriate Reliance on AI Systemscited by 41
  11. 2023Ignore, Trust, or Negotiate: Understanding Clinician Acceptance of AI-Based Treatment Recommendations in Health Carecited by 37
  12. 2022Who Goes First? Influences of Human-AI Workflow on Decision Making in Clinical Imagingcited by 32

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Cited by year (2026 to 13 Oct)

Citing papers per year: 2025: 1; 2026: 1.

2 papers.

  1. 2026Interpretability Can Be Actionablecited by 7
  2. 2025T-FIX: Text-Based Explanations with Features Interpretable to eXpertscited by 2