4 citations · 17 across the 8 of their papers we have counts for
9 papers
BiasBuster: a Neural Approach for Accurate Estimation of Population Statistics using Biased Location Data
Sepanta Zeighami, Cyrus Shahabi
While extremely useful (e.g., for COVID-19 forecasting and policy-making, urban mobility analysis and marketing, and obtaining business insights), location data collected from mobi…
Holistic Survey of Privacy and Fairness in Machine Learning
Sina Shaham, Arash Hajisafi, Minh K Quan +6
Privacy and fairness are two crucial pillars of responsible Artificial Intelligence (AI) and trustworthy Machine Learning (ML). Each objective has been independently studied in the…
Fair Spatial Indexing: A paradigm for Group Spatial Fairness
Sina Shaham, Gabriel Ghinita, Cyrus Shahabi
Machine learning (ML) is playing an increasing role in decision-making tasks that directly affect individuals, e.g., loan approvals, or job applicant screening. Significant concern…
Supporting Secure Dynamic Alert Zones Using Searchable Encryption and Graph Embedding
Sina Shaham, Gabriel Ghinita, Cyrus Shahabi
Location-based alerts have gained increasing popularity in recent years, whether in the context of healthcare (e.g., COVID-19 contact tracing), marketing (e.g., location-based adve…
Unveiling and Mitigating Bias in Ride-Hailing Pricing for Equitable Policy Making
Nripsuta Ani Saxena, Wenbin Zhang, Cyrus Shahabi
Ride-hailing services have skyrocketed in popularity due to the convenience they offer, but recent research has shown that their pricing strategies can have a disparate impact on s…
A Neural Approach to Spatio-Temporal Data Release with User-Level Differential Privacy
Ritesh Ahuja, Sepanta Zeighami, Gabriel Ghinita +1
Several companies (e.g., Meta, Google) have initiated "data-for-good" projects where aggregate location data are first sanitized and released publicly, which is useful to many appl…