Do You Trust the Process?: Modeling Institutional Trust for Community Adoption of Reinforcement Learning Policies
This work addresses the challenge of designing AI policies for governmental bodies that citizens will actually follow, highlighting a tension between organizational efficiency and community well-being, though it is incremental as it builds on existing RL methods with a trust-aware adaptation.
The paper tackles the problem of ensuring community adoption of reinforcement learning policies by incorporating institutional trust into algorithm design, finding that trust-aware RL leads to more successful policies when organizational goals are uncertain and that conservative trust estimates increase fairness and average community trust at the cost of organizational success.
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.