Reducing Discrimination in Learning Algorithms for Social Good in Sociotechnical Systems

TL;DR

This paper discusses how machine learning algorithms in urban sociotechnical systems can unintentionally reinforce social inequalities and proposes Bayesian Optimization as a method to mitigate discrimination, focusing on smart mobility initiatives.

Contribution

It introduces a position on eliminating algorithmic discrimination in city mobility systems using Bayesian Optimization, highlighting its potential to promote fairness.

Findings

Bayesian Optimization can reduce socioeconomic discrimination in mobility algorithms.

Smart mobility systems often unintentionally reinforce social inequalities.

The approach is demonstrated using Pittsburgh's bike sharing data.

Abstract

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.