mlsanityData to 7 Oct 2026

Towards an Argumentative Foundation for Evaluative AI

arXiv 2608.07473 ↗First posted 25 Apr 2026cs.AI cs.MAcited by 1

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Abstract (arXiv)

Evaluative AI (EAI) has been recently proposed as a way to support human decision-making, not by producing a single recommendation, but by presenting competing hypotheses together with evidence for and against each. In this position paper, we advocate (computational) argumentation as a particularly suitable paradigm to provide a formal, computable foundation for forms of EAI that are explainable and contestable, setting the ground for a long-term research agenda towards distributed and human-centred EAI systems.

Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 17 of its 43 references that are in the map. First posted on arXiv 25 Apr 2026.

Builds on

17 of 43 references.

References · 17

  1. 2017A Unified Approach to Interpreting Model Predictionscited by 6,488
  2. 2016"Why Should I Trust You?": Explaining the Predictions of Any Classifiercited by 5,040
  3. 2017Counterfactual Explanations without Opening the Black Box: Automated Decisions and the GDPRcited by 1,086
  4. 2023Encouraging Divergent Thinking in Large Language Models through Multi-Agent Debatecited by 995
  5. 2021Argumentative XAI: A Surveycited by 66
  6. 2023Explainable AI is Dead, Long Live Explainable AI! Hypothesis-driven decision supportcited by 65
  7. 2022Do People Engage Cognitively with AI? Impact of AI Assistance on Incidental Learningcited by 53
  8. 2020Interpreting Neural Networks as Gradual Argumentation Frameworks (Including Proof Appendix)cited by 38
  9. 2023Ignore, Trust, or Negotiate: Understanding Clinician Acceptance of AI-Based Treatment Recommendations in Health Carecited by 37
  10. 2023Interactive Explanations by Conflict Resolution via Argumentative Exchangescited by 24
  11. 2018Modular Semantics and Characteristics for Bipolar Weighted Argumentation Graphscited by 23
  12. 2023SpArX: Sparse Argumentative Explanations for Neural Networks [Technical Report]cited by 18
5 more
  1. 2021From Human Explanation to Model Interpretability: A Framework Based on Weight of Evidencecited by 17
  2. 2025Retrieval- and Argumentation-Enhanced Multi-Agent LLMs for Judgmental Forecasting (Extended Version with Supplementary Material)cited by 8
  3. 2024Impact Measures for Gradual Argumentation Semanticscited by 5
  4. 2025Change in Quantitative Bipolar Argumentation: Sufficient, Necessary, and Counterfactual Explanationscited by 4
  5. 2025Set Contribution Functions for Quantitative Bipolar Argumentation and their Principlescited by 2

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  1. 2026Contrastive Explanations in Quantitative Bipolar Argumentation Frameworkscited by 0