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Sociotechnical systems within cities are now equipped with machine learningalgorithms in hopes to increase efficiency and functionality by modeling andpredicting trends. Machine learning algorithms have been applied in thesedomains to address challenges such as balancing the distribution of bikesthroughout a city and identifying demand hotspots for ride sharing drivers.However, these algorithms applied to challenges in sociotechnical systems haveexacerbated social inequalities due to previous bias in data sets or the lackof data from marginalized communities. In this paper, I will address how smartmobility initiatives in cities use machine learning algorithms to addresschallenges. I will also address how these algorithms unintentionallydiscriminate against features such as socioeconomic status to motivate theimportance of algorithmic fairness. Using the bike sharing program inPittsburgh, PA, I will present a position on how discrimination can beeliminated from the pipeline using Bayesian Optimization.

Reducing Discrimination in Learning Algorithms for Social Good in Sociotechnical Systems