Motivation

Did you know that higher-income neighborhoods on average have more access to smart mobility options than lower-income neighborhoods? After I evaluated the availability of Healthy Ride bikes throughout Pittsburgh using geospatial analysis, I discovered that only 5% of the bike stations were located in neighborhoods with poor housing conditions; the other 95% are located in census tracts that collectively had a median household income greater than $50,000 in 2015.

Smart mobility initiatives aim to provide affordable, on-demand, reliable modes of transportation such as bikes, electric scooters, or car sharing. The access to reliable transportation is vital to the stability and growth of a city connecting citizens to reliable jobs, medical facilities, education, and food. However, if the affordable, on-demand transportation is lacking presence in low-income neighborhoods, it excludes them from these amenities and opportunities.

Researchers have designed learning algorithms to tackle the classical resource allocation problem that planners and policymakers face when deciding where to allocate or redistribute resources, such as bikes or electric scooters. This pipeline unfortunately is vulnerable to biased predictions because the learning algorithms ultimately learn the biases in the data sets they are trained on before deployment. Biased predictions results in altering behaviors and making the data even more biased. This negative feedback loop is the sole motivation behind my CURF project this semester.

Related Works

There are very few academic contributions that have investigated or incorporated fairness in learning algorithms that aim to redistribute resources fairly. For example, [Yan and Howe, 2020] incorporate their own definition of fairness into their learning algorithm to fairly predict demand of bikes in Seattle. However, several works have investigated how to efficiently redistribute transportation resources to maximize usage and profit instead of equity across sensitive attributes such as race, income, or age [Luo et al., 2020; Lesmana et al., 2019; Rong et al., 2019; Cardona et al., 2017; Garcia et al., 2017]. A common technique used to determine where to redistribute bikes, or any limited resource, is to predict the demand of that resource at a given location and time [Yan and Howe, 2020]. Predicting this demand at a given space and time requires a data set of historical demand data for that given resource over time. This approach limits the model to learn biases inherent in the demand data, and it does not allow for exploring locations with no historical data [Mitchell, 2020].

Previous research has made significant contributions to the fair machine learning community. Researchers have been exploring how to mitigate discrimination and biases in learning algorithms by statistically defining fairness [Barocas et al., 2019; Hardt et al., 2016; Zemel et al., 2013; Dwork et al., 2011]. Some researchers have incorporated these defined notions of fairness into their frameworks to increase equity or opportunity among a protected attribute [Chen et al., 2020; Gölz et al., 2019; Donahue et al., 2020; Claure et al., 2020]. However, these works do not focus on resources in urban settings. The lack of fair learning algorithms for resource distribution in urban settings opened doors for further research.

CURF Project

My CURF project, under the advisement of Prof. Malihe Alikhani, aims to create a fair learning algorithm to provide insight as to where resources should be distributed to balance overall transportation equity across census tracts. This approach cannot and will not solely rely on historical demand data. Instead, we propose a fairness constraint on the demand prediction to achieve the two conflicting objectives of exploring uncharted areas and exploiting historically popular bike stations. The framework that we are developing takes into consideration attributes such as socioeconomic status, demographics, and location, that may not be represented in the demand data. Ultimately, the goal is to design a framework that can generalize to other resources such as dispatching law enforcement or dispatching food and water across remote villages.