Original title: Bridging Formal and Perceived Fairness: Development of an Interdisciplinary Framework in Algorithmic Decision-Making
Maike Lindermayr, Mattia Cerrato, Luisa Hübner, Johannes Kraus · September 2026
Computer programs increasingly make important decisions about people. Computer scientists usually treat fairness as a technical problem. They write math rules and metrics to reduce bias. However, human fairness is a subjective judgment. People judge fairness based on mental habits, personal expectations, and social context. Because of this, an algorithm can pass technical fairness tests but still feel deeply unjust to real people. When this happens, people will not trust or accept the system. To fix this gap, researchers are building a conceptual framework. Their model connects mathematical computer science rules with social science research on how humans perceive fairness. They are developing this framework using literature reviews, interdisciplinary workshops, and stakeholder interviews. Their goal is to help designers build systems that combine computational fairness audits with user feedback, helping affected people form well-calibrated fairness judgments.
You choose a gym because it has a top safety rating. But when you visit, the equipment looks old and the space is crowded, so you don't feel safe. The official rating says it's fine, but your experience says otherwise. In this paper, algorithms can have perfect fairness scores on paper, but if people perceive them as unfair, they won't trust them. The framework helps align what's technically fair with what feels fair to humans.
People affected by algorithmic decisions may distrust or reject systems that are technically fair but perceived as unfair. This undermines the legitimacy and adoption of these systems in real-world settings like hiring, lending, or policing.
This is a work-in-progress conceptual framework. It has not been empirically tested or validated. The methods described are synthesis and qualitative, not providing concrete implementation guidelines or solutions.
Ask anything about this paper. Answers come from its abstract, and say so when the abstract doesn't cover your question.
Bridging Formal and Perceived Fairness: Development of an Interdisciplinary Framework in Algorithmic Decision-Making — Maike Lindermayr, Mattia Cerrato, Luisa Hübner et al.
Abstract licence: CC0
Explanation generated by Synthesis from the abstract — it is not the original paper. This is a preprint, so it hasn't necessarily been peer reviewed.