This episode delves into “multi-value responsibility” in AI, exploring how agents are attributed responsibility for outcomes based on contributions to multiple, possibly conflicting values. Key properties for a multi-value responsibility framework are discussed: consistency (an agent is responsible only if they could achieve all values concurrently), completeness (responsibility should reflect all outcomes), and acceptance of weak excuses (justifiable suboptimal actions).

The authors introduce two responsibility concepts:

• Passive Responsibility: Prioritizes consistency and completeness but may penalize justifiable actions.

• Weak Responsibility: A more nuanced approach satisfying all properties, accounting for justifiable actions.

The episode highlights that agents should minimize both passive and weak responsibility, optimizing for regret-minimization and non-dominance in strategy. This approach enables ethically aware, accountable AI systems capable of making justifiable decisions in complex multi-value contexts.

https://arxiv.org/pdf/2410.17229