Name
Abstract | ||
|---|---|---|
Systems aiming to aid consumers in their decision-making (e.g., by implementing persuasive techniques) are more likely to be effective when consumers trust them. However, recent research has demonstrated that the machine learning algorithms that often underlie such technology can act unfairly towards specific groups (e.g., by making more favorable predictions for men than for women). An undesired disparate impact resulting from this kind of algorithmic unfairness could diminish consumer trust and thereby undermine the purpose of the system. We studied this effect by conducting a between-subjects user study investigating how (gender-related) disparate impact affected consumer trust in an app designed to improve consumers' financial decision-making. Our results show that disparate impact decreased consumers' trust in the system and made them less likely to use it. Moreover, we find that trust was affected to the same degree across consumer groups (i.e., advantaged and disadvantaged users) despite both of these consumer groups recognizing their respective levels of personal benefit. Our findings highlight the importance of fairness in consumer-oriented artificial intelligence systems. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1007/978-3-030-79460-6_11 | PERSUASIVE TECHNOLOGY (PERSUASIVE 2021) |
Keywords | DocType | Volume |
Disparate impact, Algorithmic fairness, Consumer trust | Conference | 12684 |
ISSN | Citations | PageRank |
0302-9743 | 0 | 0.34 |
References | Authors | |
0 | 6 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Tim Draws | 1 | 1 | 2.05 |
| Zoltán Szlávik | 2 | 116 | 21.40 |
| Benjamin Timmermans | 3 | 1 | 0.70 |
| Nava Tintarev | 4 | 1 | 1.38 |
| Kush R. Varshney | 5 | 0 | 0.34 |
| Michael Hind | 6 | 0 | 0.34 |