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Algorithmic decision-making using rankings— prevalent in areas from hiring and bail to university admissions— raises concerns of potential bias. In this paper, we explore the alignment between people’s perceptions of fairness and two popular fairness metrics designed for rankings. In a crowdsourced experiment with 480 participants, people rated the perceived fairness of a hypothetical scholarship distribution scenario. Results suggest a strong inclination towards relying on explicit score values. There is also evidence of people’s preference for one fairness metric, NDKL, over the other metric, ARP. Qualitative results paint a more complex picture: some participants endorse meritocratic award schemes and express concerns about fairness metrics being used to modify rankings; while other participants acknowledge socio-economic factors in score-based rankings as justification for adjusting rankings. In summary, we find that operationalizing algorithmic fairness in practice is a balancing act between mitigating harms towards marginalized groups and societal conventions of leveraging traditional performance scores such as grades in decision-making contexts.
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Do Crowdsourced Fairness Preferences Correlate with Risk Perceptions?
With the increasing prevalence of automatic decision-making systems, concerns regarding the fairness of these systems also arise. Without a universally agreed-upon definition of fairness, given an automated decision-making scenario, researchers often adopt a crowdsourced approach to solicit people’s preferences across multiple fairness definitions. However, it is often found that crowdsourced fairness preferences are highly context-dependent, making it intriguing to explore the driving factors behind these preferences. One plausible hypothesis is that people’s fairness preferences reflect their perceived risk levels for different decision-making mistakes, such that the fairness definition that equalizes across groups the type of mistakes that are perceived as most serious will be preferred. To test this conjecture, we conduct a human-subject study (𝑁 =213) to study people’s fairness perceptions in three societal contexts. In particular, these three societal contexts differ on the expected level of risk associated with different types of decision mistakes, and we elicit both people’s fairness preferences and risk perceptions for each context. Our results show that people can often distinguish between different levels of decision risks across different societal contexts. However, we find that people’s fairness preferences do not vary significantly across the three selected societal contexts, except for within a certain subgroup of people (e.g., people with a certain racial background). As such, we observe minimal evidence suggesting that people’s risk perceptions of decision mistakes correlate with their fairness preference. These results highlight that fairness preferences are highly subjective and nuanced, and they might be primarily affected by factors other than the perceived risks of decision mistakes.
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- Award ID(s):
- 1939728
- PAR ID:
- 10549624
- Publisher / Repository:
- ACM
- Date Published:
- ISBN:
- 9798400705083
- Page Range / eLocation ID:
- 304 to 324
- Subject(s) / Keyword(s):
- Automated Decision Making Perceived Fairness Crowdsourcing
- Format(s):
- Medium: X
- Location:
- Greenville SC USA
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1145/3640543.3645209
