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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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This content will become publicly available on October 20, 2026
Exploring “Just Noticeable” Group Fairness in Rankings
The plethora of fairness metrics developed for ranking-based decision-making raises the question: which metrics align best with people’s perceptions of fairness, and why? Most prior studies examining people’s perceptions of fairness metrics tend to use ordinal rating scales (e.g., Likert scales). However, such scales can be ambiguous in their interpretation across participants, and can be influenced by interface features used to capture responses.We address this gap by exploring the use of two-alternative forced choice methodologies— used extensively outside the fairness community for comparing visual stimuli— to quantitatively compare participant perceptions across fairness metrics and ranking characteristics. We report a crowdsourced experiment with 224 participants across four conditions: two alternative rank fairness metrics, ARP and NDKL, and two ranking characteristics, lists of 20 and 100 candidates, resulting in over 170,000 individual judgments. Quantitative results show systematic differences in how people interpert these metrics, and surprising exceptions where fairness metrics disagree with people’s perceptions. Qualitative analyses of participant comments reveals an interplay between cognitive and visual strategies that affects people’s perceptions of fairness. From these results, we discuss future work in aligning fairness metrics with people’s perceptions, and highlight the need and benefits of expanding methodologies for fairness studies.
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- Award ID(s):
- 2007932
- PAR ID:
- 10634931
- Publisher / Repository:
- Association for the Advancement of Artificial Intelligence (www.aaai.org).
- Date Published:
- Format(s):
- Medium: X
- Location:
- AIES 2025, Madrid, Spain
- Sponsoring Org:
- National Science Foundation
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