Effects of Fairness and Explanation on Trust in Ethical AI

Alessa Angerschmid, Kevin Theuermann, Andreas Holzinger, Fang Chen, Jianlong Zhou*

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference paperpeer-review

Abstract

AI ethics has been a much discussed topic in recent years. Fairness and explainability are two important ethical principles for trustworthy AI. In this paper, the impact of AI explainability and fairness on user trust in AI-assisted decisions is investigated. For this purpose, a user study was conducted simulating AI-assisted decision making in a health insurance scenario. The study results demonstrated that fairness only affects user trust when the fairness level is low, with a low fairness level reducing user trust. However, adding explanations helped users increase their trust in AI-assisted decision making. The results show that the use of AI explanations and fairness statements in AI applications is complex: we need to consider not only the type of explanations, but also the level of fairness introduced. This is a strong motivation for further work.

Original languageEnglish
Title of host publicationMachine Learning and Knowledge Extraction - 6th IFIP TC 5, TC 12, WG 8.4, WG 8.9, WG 12.9 International Cross-Domain Conference, CD-MAKE 2022, Proceedings
EditorsAndreas Holzinger, Andreas Holzinger, Andreas Holzinger, Peter Kieseberg, A Min Tjoa, Edgar Weippl, Edgar Weippl
PublisherSpringer Science and Business Media Deutschland GmbH
Pages51-67
Number of pages17
ISBN (Print)9783031144622
DOIs
Publication statusPublished - 2022
Event6th IFIP TC 5, TC 12, WG 8.4, WG 8.9, WG 12.9 International Cross-Domain Conference for Machine Learning and Knowledge Extraction, held in conjunction with the 17th International Conference on Availability, Reliability and Security: ARES 2022 - Vienna, Austria
Duration: 23 Aug 202226 Aug 2022

Publication series

NameLecture Notes in Computer Science
Volume13480 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference6th IFIP TC 5, TC 12, WG 8.4, WG 8.9, WG 12.9 International Cross-Domain Conference for Machine Learning and Knowledge Extraction, held in conjunction with the 17th International Conference on Availability, Reliability and Security
Country/TerritoryAustria
CityVienna
Period23/08/2226/08/22

Keywords

  • AI ethics
  • AI explanation
  • AI fairness
  • Trust

ASJC Scopus subject areas

  • Theoretical Computer Science
  • General Computer Science

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