A Causal Lens for Learning Long-term Fair Policies
Jacob Lear, Lu Zhang
TL;DR
This paper introduces a causal framework for assessing and balancing long-term fairness in reinforcement learning policies, emphasizing the importance of considering delayed and direct impacts on different groups.
Contribution
It proposes a causal decomposition of long-term fairness into impact components and develops an approach to balance fairness notions in dynamic decision-making.
Findings
Decomposition of long-term fairness into direct, delayed, and spurious effects.
Connection between these components and benefit fairness.
Effective method for balancing multiple fairness criteria.
Abstract
Fairness-aware learning studies the development of algorithms that avoid discriminatory decision outcomes despite biased training data. While most studies have concentrated on immediate bias in static contexts, this paper highlights the importance of investigating long-term fairness in dynamic decision-making systems while simultaneously considering instantaneous fairness requirements. In the context of reinforcement learning, we propose a general framework where long-term fairness is measured by the difference in the average expected qualification gain that individuals from different groups could obtain.Then, through a causal lens, we decompose this metric into three components that represent the direct impact, the delayed impact, as well as the spurious effect the policy has on the qualification gain. We analyze the intrinsic connection between these components and an emerging…
Peer Reviews
Decision·ICLR 2025 Poster
1. The decomposition of long-term fairness into direct and delayed impact is interesting, although it is not surprising. The connection between benefit fairness and the proposed measure of fairness (through policy intervention in an MDP) is quite interesting. 2. The experimental section shows that the proposed modification of the PPO objective works, and in particular it can achieve approximate benefit fairness.
1. The authors claim that they introduce a general framework to study long-term fairness. However, the proposed framework is based on MDP which has been extensively studied in dynamic fairness. 2. The causal decomposition result is very similar to existing decomposition results of causal fairness for static settings. In fact, the proof is very similar and the only difference seems to be that the indirect effect is replaced with the delayed impact. 3. Finally, the main drawback of the work is t
-- The paper studies long-term fairness which is an important, yet understudied problem. -- The paper proposes a novel fairness metric for long-term fairness in causal reinforcement learning.
-- There are no technical algorithmic novelties in the paper as the optimization problem is inspired by PPO. This is not a major weakness as the paper introduces novel elements like the qualification gain function and proposes a causal decomposition of the qualification gain. -- The experimental setup does not clearly show the advantage of the approach over prior baselines. For example, the difference between the worst and best approach is a mere 0.2% in all experiments in Figure 2. Are there o
1. The long-term fairness under causal inference perspective seems novel 2. The fairness penalty decomposition is useful in practice
1. Theoretical guarantee not sufficient: (1). Convergence of the algorithm (2). How the penalty coefficients \beta^{KL} and \beta^{\Lambda} quantitatively impact fairness and model performance (e.g., precision/recall per round) 2. Despite fairness metrics improvement, it is difficult to tell if the model performance improved and if the fairness-aware optimization can result in a Pareto superior result for every group or every individual, there should be more theoretical and numerical evidence o
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TopicsQualitative Comparative Analysis Research