Machine learning (ML) technologies are known to be riddled with ethical and
operational problems, however, we are witnessing an increasing thrust by
businesses to deploy them in sensitive applications. One major issue among many
is that ML models do not perform equally well for underrepresented groups. This
puts vulnerable populations in an even disadvantaged and unfavorable position.
We propose an approach that leverages the power of web search and generative
models to alleviate some of the shortcomings of discriminative models. We
demonstrate our method on an image classification problem using ImageNet's
People Subtree subset, and show that it is effective in enhancing robustness
and mitigating bias in certain classes that represent vulnerable populations
(e.g., female doctor of color). Our new method is able to (1) identify weak
decision boundaries for such classes; (2) construct search queries for Google
as well as text for generating images through DALL-E 2 and Stable Diffusion;
and (3) show how these newly captured training samples could alleviate
population bias issue. While still improving the model's overall performance
considerably, we achieve a significant reduction (77.30\%) in the model's
gender accuracy disparity. In addition to these improvements, we observed a
notable enhancement in the classifier's decision boundary, as it is
characterized by fewer weakspots and an increased separation between classes.
Although we showcase our method on vulnerable populations in this study, the
proposed technique is extendable to a wide range of problems and domains.

机器学习（ML）技术已知存在伦理和运营问题，但我们正见证企业在敏感应用中部署它们的增长势头。我们提出了一种利用网络搜索和生成模型来缓解判别模型缺陷的方法。我们在 ImageNet 的 People Subtree 子集上展示了我们的方法，并展示其在某些代表弱势群体的类别中增强了鲁棒性和减轻了偏见（例如，有色人种女医生）。虽然在极大程度上提高了模型的整体性能，我们在模型的性别准确性差异方面实现了显著降低（77.30%）。除了这些改进，我们观察到分类器的决策边界得到了明显的增强，它具有较少的弱点和类别之间的更大差异。虽然在本研究中我们展示了针对弱势群体的方法，但该技术可以扩展到各种问题和领域。