Thinking Outside the Box: Orthogonal Approach to Equalizing Protected Attributes
Abstract: There is growing concern that the potential of black box AI may exacerbate health-related disparities and biases such as gender and ethnicity in clinical decision-making. Biased decisions can arise from data availability and collection processes, as well as from the underlying confounding effects of the protected attributes themselves. This work proposes a machine learning-based orthogonal approach aiming to analyze and suppress the effect of the confounder through discriminant dimensionality reduction and orthogonalization of the protected attributes against the primary attribute information. By doing so, the impact of the protected attributes on disease diagnosis can be realized, undesirable feature correlations can be mitigated, and the model prediction performance can be enhanced.
- Ethical machine learning in healthcare. Annual Review of Biomedical Data Science, 4:123–144, 2021.
- Artificial intelligence and machine learning technologies in cancer care: Addressing disparities, bias, and data diversity. Cancer Discovery, 12(6):1423–1427, 2022.
- Ai for radiographic covid-19 detection selects shortcuts over signal. Nature Machine Intelligence, 3(7):610–619, 2021.
- An optimal set of discriminant vectors. IEEE Transactions on Computers, 100(3):281–289, 1975.
- AI recognition of patient race in medical imaging: a modelling study. The Lancet Digital Health, 4(6):e406–e414, 2022.
- Algorithmic encoding of protected characteristics in image-based models for disease detection. arXiv Preprint arXiv:2110.14755, 2021.
- Towards gender equity in artificial intelligence and machine learning applications in dermatology. Journal of the American Medical Informatics Association, 29(2):400–403, 2022.
- GO-LDA: Generalised optimal linear discriminant analysis. arXiv preprint arXiv:2305.14568, 2023.
- Challenging common assumptions in the unsupervised learning of disentangled representations. In International Conference on Machine Learning, pages 4114–4124. PMLR, 2019.
- Unsupervised domain adaptation using feature disentanglement and gcns for medical image classification. In European Conference on Computer Vision, pages 735–748. Springer, 2022.
- Shaina Raza. A machine learning model for predicting, diagnosing, and mitigating health disparities in hospital readmission. Healthcare Analytics, 2:100100, 2022.
- Radiology “forensics”: determination of age and sex from chest radiographs using deep learning. Emergency Radiology, 28:949–954, 2021.
- Improving the fairness of chest x-ray classifiers. In Conference on Health, Inference, and Learning, pages 204–233. PMLR, 2022.
Paper Prompts
Sign up for free to create and run prompts on this paper.