Effect of information presentation on fairness perceptions of machine learning predictors

van Berkel, Niels; Goncalves, Jorge; Russo, Daniel; Hosio, Simo; Skov, Mikael B. (2021-05-20)
 
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https://doi.org/10.1145/3411764.3445365

van Berkel, Niels
Goncalves, Jorge
Russo, Daniel
Hosio, Simo
Skov, Mikael B.
Association for Computing Machinery
20.05.2021

Niels van Berkel, Jorge Goncalves, Daniel Russo, Simo Hosio, and Mikael B. Skov. 2021. Effect of Information Presentation on Fairness Perceptions of Machine Learning Predictors. In Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems (CHI '21). Association for Computing Machinery, New York, NY, USA, Article 245, 1–13. DOI:https://doi.org/10.1145/3411764.3445365

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© 2021 Copyright held by the owner/author(s). Publication rights licensed to ACM. This is the author's version of the work. It is posted here for your personal use. Not for redistribution. The definitive Version of Record was published in Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems, https://doi.org/10.1145/3411764.3445365.
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doi:https://doi.org/10.1145/3411764.3445365
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Abstract

The uptake of artificial intelligence-based applications raises concerns about the fairness and transparency of AI behaviour. Consequently, the Computer Science community calls for the involvement of the general public in the design and evaluation of AI systems. Assessing the fairness of individual predictors is an essential step in the development of equitable algorithms. In this study, we evaluate the effect of two common visualisation techniques (text-based and scatterplot) and the display of the outcome information (i.e., ground-truth) on the perceived fairness of predictors. Our results from an online crowdsourcing study (N = 80) show that the chosen visualisation technique significantly alters people’s fairness perception and that the presented scenario, as well as the participant’s gender and past education, influence perceived fairness. Based on these results we draw recommendations for future work that seeks to involve non-experts in AI fairness evaluations.

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