  Deep neural networks (DNNs) have made significant progress, but often suffer
from fairness issues, as deep models typically show distinct accuracy
differences among certain subgroups (e.g., males and females). Existing
research addresses this critical issue by employing fairness-aware loss
functions to constrain the last-layer outputs and directly regularize DNNs.
Although the fairness of DNNs is improved, it is unclear how the trained
network makes a fair prediction, which limits future fairness improvements. In
this paper, we investigate fairness from the perspective of decision rationale
and define the parameter parity score to characterize the fair decision process
of networks by analyzing neuron influence in various subgroups. Extensive
empirical studies show that the unfair issue could arise from the unaligned
decision rationales of subgroups. Existing fairness regularization terms fail
to achieve decision rationale alignment because they only constrain last-layer
outputs while ignoring intermediate neuron alignment. To address the issue, we
formulate the fairness as a new task, i.e., decision rationale alignment that
requires DNNs' neurons to have consistent responses on subgroups at both
intermediate processes and the final prediction. To make this idea practical
during optimization, we relax the naive objective function and propose
gradient-guided parity alignment, which encourages gradient-weighted
consistency of neurons across subgroups. Extensive experiments on a variety of
datasets show that our method can significantly enhance fairness while
sustaining a high level of accuracy and outperforming other approaches by a
wide margin.
