Fairness and Explanation in AI-Informed Decision Making

Angerschmid, A; Zhou, J; Theuermann, K; Chen, F; Holzinger, A

Fairness and Explanation in AI-Informed Decision Making

Angerschmid, A Zhou, J

Theuermann, K Chen, F

Holzinger, A

Permalink

Publisher:: MDPI AG
Publication Type:: Journal Article
Citation:: Machine Learning and Knowledge Extraction, 2022, 4, (2), pp. 556-579
Issue Date:: 2022-06-16

Open Access

Copyright Clearance Process

Recently Added
In Progress
Open Access

This item is open access.

Adobe PDF

Download Published VersionAdobe PDF (1.53 MB)

View on publisher's site

View statistics

Full metadata record

Field	Value	Language
dc.contributor.author	Angerschmid, A
dc.contributor.author	Zhou, J https://orcid.org/0000-0001-6034-644X
dc.contributor.author	Theuermann, K
dc.contributor.author	Chen, F https://orcid.org/0000-0003-4971-8729
dc.contributor.author	Holzinger, A
dc.date.accessioned	2022-07-26T11:59:54Z
dc.date.available	2022-07-26T11:59:54Z
dc.date.issued	2022-06-16
dc.identifier.citation	Machine Learning and Knowledge Extraction, 2022, 4, (2), pp. 556-579
dc.identifier.issn	1365-8190
dc.identifier.issn	2504-4990
dc.identifier.uri	http://hdl.handle.net/10453/159185
dc.description.abstract	<jats:p>AI-assisted decision-making that impacts individuals raises critical questions about transparency and fairness in artificial intelligence (AI). Much research has highlighted the reciprocal relationships between the transparency/explanation and fairness in AI-assisted decision-making. Thus, considering their impact on user trust or perceived fairness simultaneously benefits responsible use of socio-technical AI systems, but currently receives little attention. In this paper, we investigate the effects of AI explanations and fairness on human-AI trust and perceived fairness, respectively, in specific AI-based decision-making scenarios. A user study simulating AI-assisted decision-making in two health insurance and medical treatment decision-making scenarios provided important insights. Due to the global pandemic and restrictions thereof, the user studies were conducted as online surveys. From the participant’s trust perspective, fairness was found to affect user trust only under the condition of a low fairness level, with the low fairness level reducing user trust. However, adding explanations helped users increase their trust in AI-assisted decision-making. From the perspective of perceived fairness, our work found that low levels of introduced fairness decreased users’ perceptions of fairness, while high levels of introduced fairness increased users’ perceptions of fairness. The addition of explanations definitely increased the perception of fairness. Furthermore, we found that application scenarios influenced trust and perceptions of fairness. 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 and the degree of fairness introduced, but also the scenarios in which AI-assisted decision-making is used.</jats:p>
dc.language	en
dc.publisher	MDPI AG
dc.relation.ispartof	Machine Learning and Knowledge Extraction
dc.relation.isbasedon	10.3390/make4020026
dc.rights	info:eu-repo/semantics/openAccess
dc.title	Fairness and Explanation in AI-Informed Decision Making
dc.type	Journal Article
utslib.citation.volume	4
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
utslib.copyright.status	open_access	*
dc.date.updated	2022-07-26T11:59:52Z
pubs.issue	2
pubs.publication-status	Published online
pubs.volume	4
utslib.citation.issue	2

Abstract:

AI-assisted decision-making that impacts individuals raises critical questions about transparency and fairness in artificial intelligence (AI). Much research has highlighted the reciprocal relationships between the transparency/explanation and fairness in AI-assisted decision-making. Thus, considering their impact on user trust or perceived fairness simultaneously benefits responsible use of socio-technical AI systems, but currently receives little attention. In this paper, we investigate the effects of AI explanations and fairness on human-AI trust and perceived fairness, respectively, in specific AI-based decision-making scenarios. A user study simulating AI-assisted decision-making in two health insurance and medical treatment decision-making scenarios provided important insights. Due to the global pandemic and restrictions thereof, the user studies were conducted as online surveys. From the participant’s trust perspective, fairness was found to affect user trust only under the condition of a low fairness level, with the low fairness level reducing user trust. However, adding explanations helped users increase their trust in AI-assisted decision-making. From the perspective of perceived fairness, our work found that low levels of introduced fairness decreased users’ perceptions of fairness, while high levels of introduced fairness increased users’ perceptions of fairness. The addition of explanations definitely increased the perception of fairness. Furthermore, we found that application scenarios influenced trust and perceptions of fairness. 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 and the degree of fairness introduced, but also the scenarios in which AI-assisted decision-making is used.

Please use this identifier to cite or link to this item:

http://hdl.handle.net/10453/159185