mlsanityData to 14 Oct 2026

The Influence of Explainable Artificial Intelligence: Nudging Behaviour or Boosting Capability?

arXiv 2210.02407 ↗First posted 5 Oct 2022cs.HC cs.AIcited by 1

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Abstract (arXiv)

This article aims to provide a theoretical account and corresponding paradigm for analysing how explainable artificial intelligence (XAI) influences people's behaviour and cognition. It uses insights from research on behaviour change. Two notable frameworks for thinking about behaviour change techniques are nudges - aimed at influencing behaviour - and boosts - aimed at fostering capability. It proposes that local and concept-based explanations are more adjacent to nudges, while global and counterfactual explanations are more adjacent to boosts. It outlines a method for measuring XAI influence and argues for the benefits of understanding it for optimal, safe and ethical human-AI collaboration.

Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 15 of its 54 references that are in the map. First posted on arXiv 5 Oct 2022.

Builds on

15 of 54 references.

References · 15

  1. 2017The (Un)reliability of saliency methodscited by 333
  2. 2020An Investigation of Why Overparameterization Exacerbates Spurious Correlationscited by 310
  3. 2018Metrics for Explainable AI: Challenges and Prospectscited by 260
  4. 2019Explainable Machine Learning in Deploymentcited by 211
  5. 2019"How do I fool you?": Manipulating User Trust via Misleading Black Box Explanationscited by 111
  6. 2017Runaway Feedback Loops in Predictive Policingcited by 97
  7. 2018How do Humans Understand Explanations from Machine Learning Systems? An Evaluation of the Human-Interpretability of Explanationcited by 72
  8. 2019An Evaluation of the Human-Interpretability of Explanationcited by 71
  9. 2020Robustness to Spurious Correlations via Human Annotationscited by 62
  10. 2020Now You See Me (CME): Concept-based Model Extractioncited by 51
  11. 2018Interpretable to Whom? A Role-based Model for Analyzing Interpretable Machine Learning Systemscited by 48
  12. 2021The Who in XAI: How AI Background Shapes Perceptions of AI Explanationscited by 48
3 more
  1. 2021A Survey on Understanding, Visualizations, and Explanation of Deep Neural Networkscited by 14
  2. 2022Recognising the importance of preference change: A call for a coordinated multidisciplinary research effort in the age of AIcited by 12
  3. 2018Machine Teaching of Active Sequential Learnerscited by 11

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  1. 2026Intelligent AI Delegationcited by 18