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Explanation in Artificial Intelligence: Insights from the Social Sciences

Written for the Research library (claude-opus-5-5) from the paper text, in our own words: the license "arXiv — non-exclusive license to distribute" does not allow redistributing the text itself. Original — arxiv.org/pdf/1706.07269

Summary

Explainable AI (XAI) has regained attention because users often distrust or reject AI systems they cannot understand. Yet most XAI work relies on researchers' own intuitions about what makes an explanation useful. Miller argues that this gap can be closed by drawing on long-standing research into how humans explain things to one another.

The paper is a survey, not an empirical study. It examined more than 250 publications from philosophy, cognitive psychology/science and social psychology. It then discusses a curated subset covering how people define, produce, choose, judge and communicate explanations, along with the experimental evidence behind these theories. For each area, it suggests how the findings could shape the design of explanatory agents.

The central outcome is four lessons:

  • why-questions carry an implicit contrast;
  • people pick explanations selectively and with bias;
  • explaining is a social exchange;
  • causal links matter more to people than probabilities.

The work matters because it reframes XAI as a human-agent interaction problem. Solving it requires collaboration with social scientists and HCI researchers, not simply more AI.

Key points

  • Over 250 social-science publications on explanation were surveyed. A smaller set was chosen for presentation based on currency and relevance.
  • XAI is positioned inside human-agent interaction, the field where social science, human-computer interaction (HCI) and artificial intelligence intersect.
  • An earlier small-sample study (Miller et al.) found that XAI papers rarely cite social-science frameworks of explanation. It warns of "the inmates running the asylum", meaning experts designing explanations for lay users.
  • Presenting full causal chains risks "dilution": less relevant causes can obscure the ones that answer the user's actual question. Explanation selection and relevance are therefore critical.
  • The author notes that recent explainable machine learning has largely neglected interactive, dialogue-based explanation. Early symbolic systems, such as those by Cawsey and by Moore and Paris, embraced it more naturally.

Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.

Fields: Other

Cited by

155 papers of our library cite it (of the 140673 whose bibliographies we read); 4 of them have a page here, 151 not yet.

Cite

The paper itself, not this page. BibTeX from the library's record of the work. DOI 10.48550/arxiv.1706.07269 · arXiv 1706.07269

@misc{miller2017explanation,
  title = {{Explanation in Artificial Intelligence: Insights from the Social Sciences}},
  author = {Miller, Tim},
  year = {2017},
  doi = {10.48550/arxiv.1706.07269},
  eprint = {1706.07269},
  archivePrefix = {arXiv},
  url = {https://doi.org/10.48550/arxiv.1706.07269}
}

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