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Mapping the Potential of Explainable Artificial Intelligence (XAI) for Fairness Along the AI Lifecycle

Title data

Deck, Luca ; Schomäcker, Astrid ; Speith, Timo ; Schöffer, Jakob ; Kästner, Lena ; Kühl, Niklas:
Mapping the Potential of Explainable Artificial Intelligence (XAI) for Fairness Along the AI Lifecycle.
2024
Event: Third European Workshop on Algorithmic Fairness (EWAF'24) , July 1-3, 2024 , Mainz, Germany.
(Conference item: Conference , Speech )
DOI: https://doi.org/10.48550/arXiv.2404.18736

Official URL: Volltext

Project information

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Project's official title
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ABBA
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Abstract in another language

The widespread use of artificial intelligence (AI) systems across various domains is increasingly highlighting issues related to algorithmic fairness, especially in high-stakes scenarios. Thus, critical considerations of how fairness in AI systems might be improved, and what measures are available to aid this process, are overdue. Many researchers and policymakers see explainable AI (XAI) as a promising way to increase fairness in AI systems.  However, there is a wide variety of XAI methods and fairness conceptions expressing different desiderata, and the precise connections between XAI and fairness remain largely nebulous. Besides, different measures to increase algorithmic fairness might be applicable at different points throughout an AI system's lifecycle. Yet, there currently is no coherent mapping of fairness desiderata along the AI lifecycle. In this paper, we set out to bridge both these gaps: We distill eight fairness desiderata, map them along the AI lifecycle, and discuss how XAI could help address each of them. We hope to provide orientation for practical applications and to inspire XAI research specifically focused on these fairness desiderata.

Further data

Item Type: Conference item (Speech)
Refereed: Yes
Keywords: Explainable AI; Algorithmic Fairness; Fairness Desiderata; AI Lifecycle
Institutions of the University: Faculties > Faculty of Law, Business and Economics > Department of Business Administration
Research Institutions
Research Institutions > Affiliated Institutes
Research Institutions > Affiliated Institutes > Branch Business and Information Systems Engineering of Fraunhofer FIT
Research Institutions > Affiliated Institutes > FIM Research Center for Information Management
Result of work at the UBT: Yes
DDC Subjects: 000 Computer Science, information, general works > 004 Computer science
300 Social sciences > 330 Economics
Date Deposited: 12 Jun 2024 09:38
Last Modified: 12 Jun 2024 09:38
URI: https://eref.uni-bayreuth.de/id/eprint/89749