Reducing Discrimination in Learning Algorithms for Social Good in Sociotechnical Systems

• Main Author: Morrison, Katelyn
• Format: Journal Article
• Language: English
• Published: 27-11-2020
• Subjects: Computer Science - Computers and Society; Computer Science - Learning
• DOI: 10.48550/arxiv.2011.13988

Sociotechnical systems within cities are now equipped with machine learning
algorithms in hopes to increase efficiency and functionality by modeling and
predicting trends. Machine learning algorithms have been applied in these
domains to address challenges such as balancing the distribution of bikes
throughout a city and identifying demand hotspots for ride sharing drivers.
However, these algorithms applied to challenges in sociotechnical systems have
exacerbated social inequalities due to previous bias in data sets or the lack
of data from marginalized communities. In this paper, I will address how smart
mobility initiatives in cities use machine learning algorithms to address
challenges. I will also address how these algorithms unintentionally
discriminate against features such as socioeconomic status to motivate the
importance of algorithmic fairness. Using the bike sharing program in
Pittsburgh, PA, I will present a position on how discrimination can be
eliminated from the pipeline using Bayesian Optimization.