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.
@article{arxiv.2305.16700,
title = {Applying Interdisciplinary Frameworks to Understand Algorithmic Decision-Making},
author = {Timothée Schmude and Laura Koesten and Torsten Möller and Sebastian Tschiatschek},
journal= {arXiv preprint arXiv:2305.16700},
year = {2023}
}