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Conceptualizing understanding in explainable artificial intelligence (XAI) : an abilities-based approach

Title data

Speith, Timo ; Crook, Barnaby ; Mann, Sara ; Schomäcker, Astrid ; Langer, Markus:
Conceptualizing understanding in explainable artificial intelligence (XAI) : an abilities-based approach.
In: Ethics and Information Technology. Vol. 26 (2024) Issue 2 . - 40.
ISSN 1572-8439
DOI: https://doi.org/10.1007/s10676-024-09769-3

Official URL: Volltext

Project information

Project financing: Deutsche Forschungsgemeinschaft
VolkswagenStiftung

Abstract in another language

A central goal of research in explainable artificial intelligence (XAI) is to facilitate human understanding. However, understanding is an elusive concept that is difficult to target. In this paper, we argue that a useful way to conceptualize understanding within the realm of XAI is via certain human abilities. We present four criteria for a useful conceptualization of understanding in XAI and show that these are fulfilled by an abilities-based approach: First, thinking about understanding in terms of specific abilities is motivated by research from numerous disciplines involved in XAI. Second, an abilities-based approach is highly versatile and can capture different forms of understanding important in XAI application contexts. Third, abilities can be operationalized for empirical studies. Fourth, abilities can be used to clarify the link between explainability, understanding, and societal desiderata concerning AI, like fairness and trustworthiness. Conceptualizing understanding as abilities can therefore support interdisciplinary collaboration among XAI researchers, provide practical benefit across diverse XAI application contexts, facilitate the development and evaluation of explainability approaches, and contribute to satisfying the societal desiderata of different stakeholders concerning AI systems.

Further data

Item Type: Article in a journal
Refereed: Yes
Keywords: Explainability; Explainable AI; XAI; Understanding; Abilities; Evaluation; Conceptualization
Institutions of the University: Faculties > Faculty of Cultural Studies > Department of Philosophy > Chair Philosophy, Computer Science and Artificial Intelligence
Faculties
Faculties > Faculty of Cultural Studies
Faculties > Faculty of Cultural Studies > Department of Philosophy
Result of work at the UBT: Yes
DDC Subjects: 000 Computer Science, information, general works > 004 Computer science
100 Philosophy and psychology > 100 Philosophy
Date Deposited: 19 Oct 2024 21:00
Last Modified: 21 Oct 2024 09:38
URI: https://eref.uni-bayreuth.de/id/eprint/90768