Links to literature that we used, as well as relevant research that has been done

Mehrabi, Ninareh, et al. “A Survey on Bias and Fairness in Machine Learning.”

Link
ACM Computing Surveys, vol. 54, no. 6, 2021, pp. 1–35., https://doi.org/10.1145/3457607.
(Discusses different types of biases and how intervention techniques work to address them)

Jeanselme, Vincent, et al. “Imputation Strategies under Clinical Presence: Impact on Algorithmic Fairness.”

Link
ArXiv.org, 11 Nov. 2022, https://arxiv.org/abs/2208.06648.
(Explains missingness with regards to fairness and how missingness should be handled)

Salimi, Babak, et al. “Capuchin: Causal Database Repair for Algorithmic Fairness.”

Link
ArXiv.org, 1 Oct. 2019, https://arxiv.org/abs/1902.08283.
(Covers different pre-, in-, and post-processing intervention techniques and how they address improving fairness)