arXiv:2606.17756v1 Announce Type: new Abstract: Fairness has become a central concern in ranking problems involving individuals or social groups, particularly under the Responsible Artificial Intelligence agenda. In Multi-Criteria Decision Analysis, Stochastic Multicriteria Acceptability Analysis (SMAA) provides a robust framework for handling uncertainty and incomplete preference information, but it does not explicitly address fairness in the resulting rankings. This paper proposes SMAA-Fair, a fairness-aware extension of SMAA for ranking problems. The approach reweights the simulated ranking
The increasing deployment of AI in critical decision-making contexts has amplified concerns about fairness, driven by public scrutiny and regulatory pressures for Responsible AI.
This paper offers a technical solution to embed ethical considerations directly into ranking algorithms, which is crucial for the legitimate and responsible adoption of AI across various sectors.
The ability to integrate fairness explicitly into multi-criteria decision analysis algorithms changes how AI systems can be designed and evaluated for ethical outcomes, moving beyond post-hoc auditing.
- · AI ethicists and researchers
- · Regulatory bodies
- · Industries deploying AI for ranking/decision-making
- · Individuals/Social Groups impacted by AI rankings
- · Organizations ignoring AI fairness
- · Simplistic AI deployment strategies
Improved fairness in AI-driven ranking and decision systems.
Increased trust and adoption of AI in sensitive applications where bias is a major concern.
Potential for new regulatory frameworks explicitly requiring fairness-aware algorithmic design based on such methodologies.
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Read at arXiv cs.LG