Fairness in Reinforcement Learning (1611.03071v4)

Abstract: We initiate the study of fairness in reinforcement learning, where the actions of a learning algorithm may affect its environment and future rewards. Our fairness constraint requires that an algorithm never prefers one action over another if the long-term (discounted) reward of choosing the latter action is higher. Our first result is negative: despite the fact that fairness is consistent with the optimal policy, any learning algorithm satisfying fairness must take time exponential in the number of states to achieve non-trivial approximation to the optimal policy. We then provide a provably fair polynomial time algorithm under an approximate notion of fairness, thus establishing an exponential gap between exact and approximate fairness

• The paper presents a novel framework that integrates fairness constraints into reinforcement learning by defining approximate-choice and approximate-action fairness.
• The authors introduce the Fair-E algorithm, which balances ethical considerations with computational efficiency by relaxing strict fairness requirements.
• The findings underscore significant implications for ethical decision-making in applications like hiring, motivating further research into scalable fairness solutions.

Fairness in Reinforcement Learning: A Comprehensive Study

This paper explores the integration of fairness in reinforcement learning (RL), an area critical for ensuring ethical automated decision-making as machine learning systems increasingly impact various societal domains. The authors present both theoretical developments and algorithmic solutions to address fairness in environments where learning algorithms modify their surroundings and influence future rewards.

Key Concepts and Definitions

The paper introduces fairness constraints within RL, asserting that a learning algorithm must not prefer an action with lesser long-term rewards over one offering more, based on the state-action value function $Q^*$. While fairness denotes not favoring suboptimal choices, the authors extend this by outlining approximate fairness notions: approximate-choice and approximate-action fairness. These definitions aim to balance ethical requirements with practical constraints, allowing slight deviations in choice probabilities or quality estimations.

Theoretical Insights and Algorithmic Contributions

The research begins with a negative result, exposing the high computational demand for exact fairness—a fair algorithm may require exponential time in the number of states to approximate optimal policies. This reality establishes an exponential performance gap between exact and approximate fairness, motivating the design of realistic algorithms featuring relaxed fairness principles.

To navigate the computational challenges, the authors develop a novel algorithm, Fair-E, under approximate-action fairness. Fair-E performs in polynomial time relative to most MDP parameters while exhibiting expected exponential behavior concerning $1/(1-\gamma)$, where $\gamma$ is the discount factor—a necessary condition evidenced by lower bounds demonstrating the impossibility of polynomial time solutions for exact fairness. Fair-E utilizes a robust definition of known states, supporting accurate reward estimations to ensure decisions do not violate fairness standards.

Implications and Future Directions

This work offers significant implications for the ethical deployment of RL systems. The insights into fairness constraints inform practical applications like hiring processes, where historical biases might propagate unless correctly managed. By introducing these constraints, RL algorithms can make choices that are ethical and respectful of long-term consequences.

The exponential complexity tied to discount factors in Fair-E encourages future exploration of alternative fairness definitions that might dissolve this dependency. Additionally, while approximate-action fairness marks progress, future work may seek to enhance fairness guarantees across temporal dimensions without sacrificing performance—a critical challenge for RL's long-term integration into sensitive domains.

In conclusion, the paper establishes a foundational framework for fairness in RL, merging ethics with algorithmic efficiency while inviting further research into methodological advances that can shape the future landscape of machine learning ethics and fairness in automated decision-making.