Work

Applying Interdisciplinary Frameworks to Understand Algorithmic Decision-Making

Year: 2023

Type: preprint

Abstract: 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.

Source: arXiv (Cornell University)

DOI: 10.48550/arxiv.2305.16700

Authors: Schmude, Timothée, Koesten, Laura, Möller, Torsten, Tschiatschek, Sebastian

Institution: University of Vienna

Language: English

Topic: Explainable Artificial Intelligence (XAI)

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: green

Funder: Vienna Science and Technology Fund

Awards: G4847369122, G5528860119, G8734310040

OpenAlex ID: W4378715536

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