Fairness is critical for artificial intelligence systems, especially for
those deployed in high-stakes applications such as hiring and justice. Existing
efforts toward fairness in machine learning fairness require retraining or
fine-tuning the neural network weights to meet the fairness criteria. However,
this is often not feasible in practice for regular model users due to the
inability to access and modify model weights. In this paper, we propose a more
flexible fairness paradigm, Inference-Time Rule Eraser, or simply Eraser, which
considers the case where model weights can not be accessed and tackles fairness
issues from the perspective of biased rules removal at inference-time. We first
verified the feasibility of modifying the model output to wipe the biased rule
through Bayesian analysis, and deduced Inference-Time Rule Eraser via
subtracting the logarithmic value associated with unfair rules (i.e., the
model's response to biased features) from the model's logits output as a means
of removing biased rules. Moreover, we present a specific implementation of
Rule Eraser that involves two stages: (1) limited queries are performed on the
model with inaccessible weights to distill its biased rules into an additional
patched model, and (2) during inference time, the biased rules already
distilled into the patched model are excluded from the output of the original
model, guided by the removal strategy outlined in Rule Eraser. Exhaustive
experimental evaluation demonstrates the effectiveness and superior performance
of the proposed Rule Eraser in addressing fairness concerns.

在高风险应用（如招聘和司法）中，公平对于人工智能系统至关重要。本文提出了一种更灵活的公平范式，即推断时间规则擦除器（Eraser），它考虑了无法访问模型权重的情况，从有偏见规则的移除角度解决公平问题。通过贝叶斯分析验证了修改模型输出以擦除有偏见规则的可行性，并通过推断时间规则擦除器从模型的逻辑输出中减去与不公平规则（即对有偏见特征的模型响应）相关的对数值来删除有偏见规则。此外，本文还提出了具体的规则擦除器实现，包括两个阶段：（1）在无法访问权重的模型上执行有限查询，以提取其有偏见规则并创建附加的修正模型；（2）在推断时间，根据规则擦除器中概述的移除策略，从原始模型的输出中排除已提取到修正模型中的有偏见规则。详尽的实验评估表明，所提出的规则擦除器在解决公平问题方面具有有效性和优越性能。