SMU Data Science Review

Reducing Age Bias in Machine Learning: An Algorithmic Approach

Adriana Solange Garcia de Alford, Southern Methodist UniversityFollow
Steven K. Hayden, Southern Methodist UniversityFollow
Nicole Wittlin, Southern Methodist UniversityFollow
Amy AtwoodFollow

Abstract

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.

Recommended Citation

Garcia de Alford, Adriana Solange; Hayden, Steven K.; Wittlin, Nicole; and Atwood, Amy (2020) "Reducing Age Bias in Machine Learning: An Algorithmic Approach," SMU Data Science Review: Vol. 3: No. 2, Article 11.
Available at: https://scholar.smu.edu/datasciencereview/vol3/iss2/11