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.

翻译：许多政府机构正在采用人工智能策略进行决策。特别是，强化学习已被用于设计政策，一旦实施，公民将被期望遵循这些政策。大量强化学习工作假设公民会遵循这些政策，并基于此进行评估。然而，我们从先前的研究中了解到，若缺乏制度信任，公民将不会遵循政府制定的政策。在本研究中，我们开发了一种面向社区资源分配的信任感知强化学习算法。我们以人道主义工程为例，其中组织旨在向社区成员分配某种技术或资源。我们采用深度确定性策略梯度方法，学习符合组织需求的资源分配策略。随后，我们根据学习到的策略模拟资源分配过程，并建模社区成员制度信任的变化。我们研究了制度信任的纳入如何影响结果，并探讨当信任值为私有信息时，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策略，特别是在组织目标较不确定的情况下。我们发现更保守的信任估计会提高公平性和社区平均信任水平，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，当组织服务不足够的社区成员时，其效用会降低。我们发现这种干预措施在某些情况下可以提高社区的公平性和信任度，同时降低组织的成功率。本研究强调了制度信任在算法设计与实施中的重要性，并揭示了组织成功与社区福祉之间的张力关系。