Identifying and examining machine learning biases on Adult dataset
Sahil Girhepuje
This research delves into the reduction of machine learning model bias
through Ensemble Learning. Our rigorous methodology comprehensively assesses
bias across various categorical variables, ultimately revealing a pronounced
gender attribute bias. The empirical evidence unveils a substantial
gender-based wage prediction disparity: wages predicted for males, initially at
\$902.91, significantly decrease to \$774.31 when the gender attribute is
alternated to females. Notably, Kullback-Leibler divergence scores point to
gender bias, with values exceeding 0.13, predominantly within tree-based
models. Employing Ensemble Learning elucidates the quest for fairness and
transparency. Intriguingly, our findings reveal that the stacked model aligns
with individual models, confirming the resilience of model bias. This study
underscores ethical considerations and advocates the implementation of hybrid
models for a data-driven society marked by impartiality and inclusivity.