Applying Interdisciplinary Frameworks to Understand Algorithmic Decision-Making
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Abstract (arXiv)
We argue that explanations for "algorithmic decision-making" (ADM) systems can profit by adopting practices that are already used in the learning sciences. We shortly introduce the importance of explaining ADM systems, give a brief overview of approaches drawing from other disciplines to improve explanations, and present the results of our qualitative task-based study incorporating the "six facets of understanding" framework. We close with questions guiding the discussion of how future studies can leverage an interdisciplinary approach.
Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 7 of its 24 references that are in the map. First posted on arXiv 26 May 2023.
Builds on
7 of 24 references.
References · 7
- 2017Explanation in Artificial Intelligence: Insights from the Social Sciencescited by 1,206
- 2016Fair prediction with disparate impact: A study of bias in recidivism prediction instrumentscited by 672
- 2021What Do We Want From Explainable Artificial Intelligence (XAI)? -- A Stakeholder Perspective on XAI and a Conceptual Model Guiding Interdisciplinary XAI Researchcited by 119
- 2022How Different Groups Prioritize Ethical Values for Responsible AIcited by 49
- 2022Sensible AI: Re-imagining Interpretability and Explainability using Sensemaking Theorycited by 32
- 2023On the Impact of Explanations on Understanding of Algorithmic Decision-Makingcited by 9
- 2022Towards a Learner-Centered Explainable AI: Lessons from the learning sciencescited by 3
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