Abstract
The widespread adoption of Artificial Intelligence (AI) systems has profoundly reshaped decision-making in social, political, and commercial contexts. This paper explores the critical issue of fairness in AI-driven decision-making, particularly in allocating resources and tasks. By examining recent advancements and key questions in computational social choice, I highlight challenges and prospects in designing fair systems in collective decision-making that are scalable, adaptable to intricate environments, and are aligned with complex and diverse human preferences.
Keywords
- Game Theory and Economic Paradigms: GTEP: Fair division
- Game Theory and Economic Paradigms: GTEP: Mechanism design
- Agent-based and Multi-agent Systems: MAS: Resource allocation
