Date of Award


Degree Type


Degree Name

Master of Science in Computer Science


Computer Science and Statistics

First Advisor

Sarah M SMB Brown


The study aims to introduce a model agnostic, filter based framework for fairfeature selection. While in the field of fairness, numerous models have been deployed to achieve fair and unbiased results but questions that arise are (i) How often the tasks are designed in association with fairness concepts? (ii) How often we check for dependency between protected attributes (also known as sensitive attributes; e.g. race, age, sex etc.) and other features in the data to assess fairness instead of employing an algorithm right away? (iii) How often we use a framework that yields a fair set of features? An obvious answer could be that fairness techniques and fairness criteria are not yet central to the industrial work and we tend to substitute temporary solutions for the sake of attaining accuracy in the results. Another reason could be that the classifier-independent methodologies are not available explicitly as an implementable solution in a straightforward setting. Fair ML is a field concerned with making data and prediction fair but comes with some level of trade-off between accuracy and fairness. Fairness is a subjective concept and has diverse definitions associated to it, it also depends on different scenarios concerning an individual or a group. The majority of technical fairness interventions are algorithmic, which makes them computationally expensive and complex to implement. Our goal is to answer the questions mentioned above by developing a model-agnostic, fast, and easily implementable feature selection framework based on task level formulations using information theory. This feature selection technique will focus on selecting a subset of features based on the reliance of protected attributes (race, sex, age etc.) on other random variables present in the data and maximal dependency of features with the target.



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