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Long-Term Fairness Strategies in Ranking with Continuous Sensitive Attributes
Luca Giuliani, Eleonora Misino, Roberta Calegari, Michele Lombardi
Proceedings of the 2nd Workshop on Fairness and Bias in AI co-located with 27th European Conference on Artificial Intelligence (ECAI 2024)
ottobre 2024
Recent advancements have made significant progress in addressing fair ranking and fairness with continuous sensitive attributes as separate challenges. However, their intersection remains underexplored, although crucial for guaranteeing a wider applicability of fairness requirements. In many real-world contexts, sensitive attributes such as age, weight, income, or degree of disability are measured on a continuous scale rather than in discrete categories. Addressing the continuous nature of these attributes is essential for ensuring effective fairness in such scenarios. This work aims to fill the gap in the existing literature by proposing a novel methodology that integrates state-of-the-art techniques to address long-term fairness in the presence of continuous protected attributes. We demonstrate the effectiveness and flexibility of our approach using real-world data.
AEQUITAS — Assessment and Engineering of eQuitable, Unbiased, Impartial and Trustworthy Ai Systems (01/11/2022–31/10/2025)