Considerable research effort has been guided towards algorithmic fairness but
real-world adoption of bias reduction techniques is still scarce. Existing
methods are either metric- or model-specific, require access to sensitive
attributes at inference time, or carry high development or deployment costs.
This work explores the unfairness that emerges when optimizing ML models solely
for predictive performance, and how to mitigate it with a simple and easily
deployed intervention: fairness-aware hyperparameter optimization (HO). We
propose and evaluate fairness-aware variants of three popular HO algorithms:
Fair Random Search, Fair TPE, and Fairband. We validate our approach on a
real-world bank account opening fraud case-study, as well as on three datasets
from the fairness literature. Results show that, without extra training cost,
it is feasible to find models with 111% mean fairness increase and just 6%
decrease in performance when compared with fairness-blind HO.

本文探讨了为了预测性能而优化机器学习模型时出现的不公平现象，以及如何通过公平感知的超参数优化来减轻这种不公平现象。我们提出并评估了三种受欢迎的公平感知的超参数优化算法的变种：Fair Random Search、Fair TPE 和 Fairband，并在实际案例研究和公平性文献中的三个数据集上验证了我们的方法，结果表明，无需额外的训练成本，可以找到 111% 平均公平性增加和仅 6% 性能下降的模型。

通过超参数优化促进公平性

Promoting Fairness through Hyperparameter Optimization

Models for Visual Question Answering (VQA) are notorious for their tendency
to rely on dataset biases, as the large and unbalanced diversity of questions
and concepts involved and tends to prevent models from learning to reason,
leading them to perform educated guesses instead. In this paper, we claim that
the standard evaluation metric, which consists in measuring the overall
in-domain accuracy, is misleading. Since questions and concepts are unbalanced,
this tends to favor models which exploit subtle training set statistics.
Alternatively, naively introducing artificial distribution shifts between train
and test splits is also not completely satisfying. First, the shifts do not
reflect real-world tendencies, resulting in unsuitable models; second, since
the shifts are handcrafted, trained models are specifically designed for this
particular setting, and do not generalize to other configurations. We propose
the GQA-OOD benchmark designed to overcome these concerns: we measure and
compare accuracy over both rare and frequent question-answer pairs, and argue
that the former is better suited to the evaluation of reasoning abilities,
which we experimentally validate with models trained to more or less exploit
biases. In a large-scale study involving 7 VQA models and 3 bias reduction
techniques, we also experimentally demonstrate that these models fail to
address questions involving infrequent concepts and provide recommendations for
future directions of research.

本文提出了 GQA-OOD 基准来评估视觉问答模型的推理能力，旨在解决现有基准存在的数据集偏差和评估方法的不足，并通过大规模实验验证了现有模型在处理罕见概念时的不足，提出了减少偏差的技术用于未来研究。