arXiv:2606.18111v1 Announce Type: cross Abstract: Fairness is an important aspect of decision-making in multi-objective reinforcement learning (MORL), where policies must ensure both optimality and equity across multiple, potentially conflicting objectives. While single-policy MORL methods can learn fair policies for fixed user preferences using welfare functions such as the generalized Gini welfare function (GGF), they fail to provide the diverse set of policies necessary for dynamic or unknown user preferences. To address this limitation, we formalize the fair optimization problem in multi-p
The increasing deployment of AI in critical decision-making necessitates robust methods for ensuring fairness, especially as societal demands for equitable outcomes grow.
Achieving fair and optimal AI policies addresses a fundamental challenge in AI adoption, potentially unlocking broader societal benefits and mitigating ethical risks.
The development of methods to learn diverse, fair, and pareto-optimal policies allows AI systems to adapt to varying preferences without retraining, improving flexibility and applicability.
- · AI ethicists
- · Organizations deploying MORL systems
- · Users of AI-driven decision systems
- · Developers of single-objective AI systems
- · Systems lacking adaptability to user preferences
AI systems will become more adaptable and trustworthy across a wider range of applications.
Increased trust in AI could accelerate its integration into sensitive sectors like healthcare and finance.
The development of 'plug-and-play' fair AI modules could become a new market segment within AI services.
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Read at arXiv cs.AI