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

DOI zum Zitieren der Version auf EPub Bayreuth: https://doi.org/10.15495/EPub_UBT_00007998
URN zum Zitieren der Version auf EPub Bayreuth: urn:nbn:de:bvb:703-epub-7998-0

Titelangaben

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. Bd. 26 (2024) Heft 2 . - No. 40.
ISSN 1572-8439
DOI der Verlagsversion: https://doi.org/10.1007/s10676-024-09769-3

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Angaben zu Projekten

Projektfinanzierung: Deutsche Forschungsgemeinschaft
VolkswagenStiftung

Abstract

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.

Weitere Angaben

Publikationsform: Artikel in einer Zeitschrift
Keywords: Explainability; Explainable AI; XAI; Understanding; Abilities; Evaluation; Conceptualization
Themengebiete aus DDC: 000 Informatik,Informationswissenschaft, allgemeine Werke > 004 Informatik
100 Philosophie und Psychologie > 100 Philosophie
Institutionen der Universität: Fakultäten > Kulturwissenschaftliche Fakultät > Institut für Philosophie > Lehrstuhl Philosophie, Informatik und Künstliche Intelligenz
Fakultäten
Fakultäten > Kulturwissenschaftliche Fakultät
Fakultäten > Kulturwissenschaftliche Fakultät > Institut für Philosophie
Sprache: Englisch
Titel an der UBT entstanden: Ja
URN: urn:nbn:de:bvb:703-epub-7998-0
Eingestellt am: 15 Okt 2024 07:34
Letzte Änderung: 15 Okt 2024 07:35
URI: https://epub.uni-bayreuth.de/id/eprint/7998

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