Evaluating Fairness of Machine Learning Models Under Uncertain and Incomplete Information

In the realm of machine learning, ensuring fairness and equity in predictive models is paramount, especially in sensitive domains such as income prediction. This project delves into the evaluation of fairness in machine learning models operating under conditions of uncertainty and incomplete information. Leveraging a dataset encompassing features such as hours-per-week, age, capital-gain, capital-loss, workclass, education, education-num, marital-status, relationship, race, gender, native-country, and occupation, the study employs a diverse set of algorithms including Gradient Boosting, Linear Regression, Logistic Regression, XGBoost Regressor, and a Hybrid Algorithm.

The primary objective is to assess the fairness of these models in predicting individuals' income levels. By scrutinizing their performance against a backdrop of incomplete and uncertain information, the project aims to shed light on potential biases and disparities inherent in the predictive outcomes. The analysis accounts for various dimensions of fairness, including demographic parity, equal opportunity, and disparate impact, to provide a comprehensive understanding of model behavior.

Through rigorous evaluation and comparison, this research contributes to the advancement of fair and equitable machine learning practices. The findings offer insights into the strengths and limitations of different algorithms under varying conditions of data incompleteness and uncertainty. Ultimately, this project strives to foster the development of more transparent, accountable, and socially responsible machine learning systems.

Keywords: Fairness evaluation, Machine learning, Uncertainty, Incomplete information, Socio-demographic features, Bias mitigation, Gradient Boosting, Logistic Regression, XGBoost.