creator Sarkar, Mousam 2023 Spring 2023 Thesis Illinois Institute of Technology CS / Computer Science advisor Glavic, Boris Computer science Data Debugging Explanations Fairness Interpretability Machine Learning Provenance en At present, Artificial intelligence has been contributing to the decision-making process heavily. Bias in machine learning models has existed throughout and present studies’ direct usage of eXplainable Artificial Intelligence (XAI) approaches to identify and study bias. To solve the problem of locating bias and then mitigating it has been achieved by Gopher [1]. It generates interpretable top-k explanations for the unfairness of the model and it also identifies subsets of training data that are the root cause of this unfair behavior. We utilize this system to study the effect of pre-processing on bias through provenance. The concept of data lineage through tagging of data points during and after the pre-processing stage is implemented. Our methodology and results provide a useful point of reference for studying the relation of pre-processing data with the unfairness of the machine learning model. born digital application/pdf In Copyright http://rightsstatements.org/page/InC/1.0/ Restricted Access http://hdl.handle.net/10560/islandora:1024344