In this paper, we study the prevalence of bias in machine learning; we explore the life cycle phases where bias is potentially introduced into a machine learning model; and lastly, we present how adversarial learning can be leveraged to measure unwanted bias and unfair behavior from a machine learning algorithm. This study focuses particularly on the topics of age bias in predicting employee attrition and presents a practical approach for how adversarial learning can be successful in mitigating age bias. To measure bias, we calculate group fairness metrics across five-year age groups and evaluate fairness between a baseline predictive model and an adversarial model. Comparing the fairness metrics of demographic parity and equality of odds, as well as model accuracy, the adversarial model demonstrates a definite improvement on the demographic parity measurement across all age groups in relation to the baseline model.
Garcia de Alford, Adriana Solange; Hayden, Steven K.; Wittlin, Nicole; and Atwood, Amy
"Reducing Age Bias in Machine Learning: An Algorithmic Approach,"
SMU Data Science Review: Vol. 3:
2, Article 11.
Available at: https://scholar.smu.edu/datasciencereview/vol3/iss2/11
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