While the awareness for discrimination biases based on genders or ethnicities has grown a lot in recent years, especially in the context of fairness in Machine Learning, many datasets come with certain biases of which the researcher is not aware and hence uses to train sub-optimal models. Selection Bias identification and mitigation strategies, general enough to be a fixed component of every Machine Learning pipeline, are crucial to enable the researcher to improve the data quality by mitigating the bias instead of training a biased model.

Our research focuses on developing new algorithms that identify or mitigate bias in data sets and Machine Learning models. We are also interested in the impact of bias in Machine Learning models on real-world applications and limitations caused by bias.