你信任这个过程吗？：为社区采纳强化学习策略建模制度信任
Do You Trust the Process?: Modeling Institutional Trust for Community Adoption of Reinforcement Learning Policies
AI总结：
本研究提出信任感知的强化学习算法，在人道主义资源分配中建模制度信任，发现纳入信任可提升政策效果，并探讨配额制度以平衡公平与组织目标。
摘要：
许多政府机构正在采用人工智能政策进行决策。特别是，强化学习已被用于设计政策，这些政策如果实施，公民将被期望遵循。许多强化学习工作假设公民遵循这些政策，并据此对其进行评估。然而，我们从先前的工作中得知，如果没有制度信任，公民不会遵循政府制定的政策。在这项工作中，我们开发了一种用于社区资源分配的信任感知强化学习算法。我们考虑了人道主义工程的情况，即组织旨在向社区成员分发某种技术或资源。我们使用深度确定性策略梯度方法来学习符合组织需求的资源分配。然后，我们根据学习到的策略模拟资源分配，并模拟社区成员制度信任的变化。我们研究了这种制度信任的纳入如何影响结果，并询问如果信任值是私有的，组织能多有效地学习策略。我们发现，将信任纳入强化学习算法可以带来更成功的政策，特别是当组织目标不太确定时。我们发现，更保守的信任估计会导致公平性和平均社区信任度提高，尽管组织成功率会受到影响。最后，我们探索了一种防止社区不公平结果的策略。我们通过外部实体实施配额制度，当组织未服务足够多的社区成员时，该制度会降低组织的效用。我们发现，这种干预在某些情况下可以提高社区间的公平性和信任度，同时降低组织的成功率。这项工作强调了制度信任在算法设计和实现中的重要性，并指出了组织成功与社区福祉之间的张力。
英文摘要：
Many governmental bodies are adopting AI policies for decision-making. In particular, Reinforcement Learning has been used to design policies that citizens would be expected to follow if implemented. Much RL work assumes that citizens follow these policies, and evaluate them with this in mind. However, we know from prior work that without institutional trust, citizens will not follow policies put in place by governments. In this work, we develop a trust-aware RL algorithm for resource allocation in communities. We consider the case of humanitarian engineering, where the organization is aiming to distribute some technology or resource to community members. We use a Deep Deterministic Policy Gradient approach to learn a resource allocation that fits the needs of the organization. Then, we simulate resource allocation according to the learned policy, and model the changes in institutional trust of community members. We investigate how this incorporation of institutional trust affects outcomes, and ask how effectively an organization can learn policies if trust values are private. We find that incorporating trust into RL algorithms can lead to more successful policies, specifically when the organization's goals are less certain. We find more conservative trust estimates lead to increased fairness and average community trust, though organization success suffers. Finally, we explore a strategy to prevent unfair outcomes to communities. We implement a quota system by an external entity which decreases the organization's utility when it does not serve enough community members. We find this intervention can improve fairness and trust among communities in some cases, while decreasing the success of the organization. This work underscores the importance of institutional trust in algorithm design and implementation, and identifies a tension between organization success and community well-being.
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。
