Related papers: MobilityMirror: Bias-Adjusted Transportation Datas…

This paper describes a differentially private post-processing algorithm for learning fair regressors satisfying statistical parity, addressing privacy concerns of machine learning models trained on sensitive data, as well as fairness…

Despite the potential of online sharing economy platforms such as Uber, Lyft, or Foodora to democratize the labor market, these services are often accused of fostering unfair working conditions and low wages. These problems have been…

This paper addresses the often overlooked issue of fairness in the autonomous driving domain, particularly in vision-based perception and prediction systems, which play a pivotal role in the overall functioning of Autonomous Vehicles (AVs).…

Shared mobility-on-demand services are expanding rapidly in cities around the world. As a prominent example, app-based ridesourcing is becoming an integral part of many urban transportation ecosystems. Despite the centrality, limited public…

As calls for fair and unbiased algorithmic systems increase, so too does the number of individuals working on algorithmic fairness in industry. However, these practitioners often do not have access to the demographic data they feel they…

Location data collected from mobile devices represent mobility behaviors at individual and societal levels. These data have important applications ranging from transportation planning to epidemic modeling. However, issues must be overcome…

The enormous efforts spent on collecting naturalistic driving data in the recent years has resulted in an expansion of publicly available traffic datasets, which has the potential to assist the development of the self-driving vehicles.…

Human trajectory data, which plays a crucial role in various applications such as crowd management and epidemic prevention, is challenging to obtain due to practical constraints and privacy concerns. In this context, synthetic human…

Biases in existing datasets used to train algorithmic decision rules can raise ethical and economic concerns due to the resulting disparate treatment of different groups. We propose an algorithm for sequentially debiasing such datasets…

Optimal transport has been used extensively in resource matching to promote the efficiency of resources usages by matching sources to targets. However, it requires a significant amount of computations and storage spaces for large-scale…

Understanding human mobility is of vital importance for urban planning, epidemiology, and many other fields that aim to draw policies from the activities of humans in space. Despite recent availability of large scale data sets related to…

We introduce a fairness-aware dataset for job recommendations in advertising, designed to foster research in algorithmic fairness within real-world scenarios. It was collected and prepared to comply with privacy standards and business…

Rideshare is one way to share mobility in transportation without increasing traffic demand, where travel mobility and usage of vehicle capacity can be improved. However, current literature on rideshare has allowed only one-modal trips and…

As mobile devices and location-based services are increasingly developed in different smart city scenarios and applications, many unexpected privacy leakages have arisen due to geolocated data collection and sharing. User re-identification…

Algorithmic fairness has aroused considerable interests in data mining and machine learning communities recently. So far the existing research has been mostly focusing on the development of quantitative metrics to measure algorithm…

Preserving user privacy is paramount when it comes to publicly disclosed datasets that contain fine-grained data about large populations. The problem is especially critical in the case of mobile traffic datasets collected by cellular…

We introduce a new landmark recognition dataset, which is created with a focus on fair worldwide representation. While previous work proposes to collect as many images as possible from web repositories, we instead argue that such approaches…

Predictive models learned from historical data are widely used to help companies and organizations make decisions. However, they may digitally unfairly treat unwanted groups, raising concerns about fairness and discrimination. In this…

Making evidence based decisions requires data. However for real-world applications, the privacy of data is critical. Using synthetic data which reflects certain statistical properties of the original data preserves the privacy of the…

Ride-sourcing drivers as individual service suppliers can freely adopt their own relocation strategies including waiting, cruising freely, or following the platform recommendations. These decisions substantially impact the balance between…