A large proportion of police-citizen interactions are initiated by 911, and thus pairing 911 call data with police stop data provides a step toward a more systematic causal framework for estimating racial bias.

Predictive policing systems use narrowly scoped data and narrowly defined objectives that lead to 'hotspot' policing — disproportionate policing of small areas. What impact does this have on communities beyond how it effects crime? We examine how algorithms can lead to changes in police practices and policies.

Applying a systematic framework to causally understand the effect of race on policing, the policing team looks at linked data quantitatively to estimate how different races benefit or suffer differently from the same policy interventions.

Linking law enforcement data, which is collected through heterogeneous administrative processes, into a stitched data set will provide a more systematic characterization and insightful understanding of law enforcement.

Researchers are building datasets from various law enforcement-related sources, like body camera images, cell phone mobility data, and social media posts. With interfaces that support users from different programming backgrounds, this initiative will benefit law enforcement researchers across the US.

Bot detection is an important part of keeping social media platforms safe. State-of-the-art computing tools appear to have high accuracy at this task on benchmark datasets, but this is attributable to limitations in data collection and labeling strategies. This raises doubts about the effectiveness of the tools social platforms use.

Studies have indicated that ads on social platforms can be delivered to demographic groups at different rates. Few studies explore this by collecting data about what users are seeing. A novel dataset gives researchers a clearer picture of how ads for housing, jobs, and credit proliferate to different racial groups.

What influence do YouTube algorithms have on political thought and radicalization? Researchers take a new approach, mapping content and users in a multi-dimensional space based on more granular information about their consumption than previous studies – in particular, the signals embedded in videos' scripts.

If people are exposed to discriminatory content online, it may potentially reinforce harmful behavior offline. This project seeks to understand whether people’s identities and attributes contribute to the relationship between online consumption and offline behavior.

IDSS faculty and industry experts unpack how data can be used to lead, mislead, manipulate, and inform the public’s viewpoints and decisions. Season 2 has begun!