Algorithmic fairness as a human-technology interaction problem.

Bridging Formal and Perceived Fairness: Development of an Interdisciplinary Framework in Algorithmic Decision-Making

While fairness has become a central concern in research on algorithmic systems, the field remains predominantly shaped by Computer Science, resulting in a strong emphasis on formal fairness metrics and bias mitigation strategies. Nevertheless, this focus may obscure a fundamental challenge: fairness is not merely a tec...

Algorithmic Fairness, Meritocracy, and Institutional Justice

Fair Like Us? Auditing LLM Alignment in Resource Allocation

It is found that large language models tend to prefer stricter fairness constraints than humans, show more self-interested behavior, are sensitive to how information is framed, and are difficult to align with human judgments using fine-tuning with current datasets.

Technical fairness and social legitimacy: a conceptual mapping of algorithmic fairness, procedural justice, and explainable AI

This study introduces a fairness-by-design framework that integrates stakeholder involvement and explainability into the development lifecycle and operationalizes this framework through the Fairness Process Card, a practical tool for documenting procedural justice mechanisms.

Against the causal account of algorithmic fairness

According to the causal account of algorithmic fairness, disparities in error rates across socially salient groups are unfair only if they are causally explained by group membership. This paper argues that the causal account of algorithmic fairness fails to correctly label cases of algorithmic redlining as instances of...

Evolution of fairness in multi-objective reinforcement learning framework

This work proposes a multi-objective reinforcement learning framework that models the evolution of fairness as a dynamic trade-off between material payoff maximization and fairness-driven moral behavior, regulated by a fairness pressure coefficient.