arXiv:2508.14311v2 Announce Type: replace Abstract: There is an increasing need to enforce multiple, often competing, measures of fairness within automated decision systems. The appropriate weighting of these fairness objectives is typically unknown a priori, may change over time and, in our setting, must be learned adaptively through sequential interactions. In this work, we address this challenge in a bandit setting, where decisions are made with graph-structured feedback.
The increasing deployment of AI in critical decision-making systems highlights the urgent need for robust, adaptive, and fair algorithms, as regulatory and ethical scrutiny intensifies.
This research addresses a core challenge in AI deployment by proposing a mechanism to incorporate and adapt multiple, potentially conflicting, fairness objectives in real-time, crucial for trustworthy AI.
The ability to dynamically learn and balance various fairness criteria within online decision systems mitigates ethical risks and broadens AI's applicability in sensitive domains.
- · AI developers
- · Ethical AI frameworks
- · Regulators
- · Sectors using automated decision systems
- · Systems with rigid fairness policies
- · Black-box AI without fairness considerations
More adaptable and auditable AI systems are developed and deployed, reducing bias-related incidents.
Increased public and regulatory trust in AI leads to wider adoption across diverse and sensitive applications.
New standards and best practices for multi-objective fairness optimization emerge, influencing future AI policy and design.
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