一种用于管理偏差的决策理论方法
A Decision-Theoretic Approach for Managing Misalignment
AI总结：
本文提出了一种决策理论框架，用于在不确定情况下评估人工智能代理的偏差是否足够，以决定是否委托决策。
摘要：
当应该将决策委托给人工智能系统时？尽管价值对齐文献已经发展出塑造人工智能价值观的技术，但较少关注如何在不确定的情况下确定，当不完全对齐是否足够好以证明委托。我们主张理性委托需要在代理的价值（偏差）与其认知准确性及其影响力（它有可用的行动）之间进行平衡。本文介绍了一个正式的、决策理论的框架来分析这种权衡，精确地考虑了主管对这些因素的不确定性。我们的分析揭示了两种委托场景之间的鲜明区别。首先，普遍委托（信任一个代理处理任何问题）要求近乎完美的价值对齐和完全的认知信任，这些条件在实践中很少满足。其次，我们证明，即使有显著的偏差，情境特定的委托也可以是最优的。代理的更高准确性或扩展的影响力可能使其能够访问更好的整体决策问题，使委托在期望上变得合理。我们开发了一个新的评分框架来量化这种事前决策。最终，我们的工作提供了一种原则性的方法来确定人工智能在给定情境中对齐到足以的程度，将焦点从实现完美对齐转移到在不确定性下管理委托的风险和回报。
英文摘要：
When should we delegate decisions to AI systems? While the value alignment literature has developed techniques for shaping AI values, less attention has been paid to how to determine, under uncertainty, when imperfect alignment is good enough to justify delegation. We argue that rational delegation requires balancing an agent's value (mis)alignment with its epistemic accuracy and its reach (the acts it has available). This paper introduces a formal, decision-theoretic framework to analyze this tradeoff precisely accounting for a principal's uncertainty about these factors. Our analysis reveals a sharp distinction between two delegation scenarios. First, universal delegation (trusting an agent with any problem) demands near-perfect value alignment and total epistemic trust, conditions rarely met in practice. Second, we show that context-specific delegation can be optimal even with significant misalignment. An agent's superior accuracy or expanded reach may grant access to better overall decision problems, making delegation rational in expectation. We develop a novel scoring framework to quantify this ex ante decision. Ultimately, our work provides a principled method for determining when an AI is aligned enough for a given context, shifting the focus from achieving perfect alignment to managing the risks and rewards of delegation under uncertainty.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
