The Police or the Public? 911 Calls and Racial Discriminations in Policing
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.READ MORE
Tradeoffs in Hotspot Predictive Policing
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.READ MORE
Understanding the Causal Effect of Race in the Criminal Justice System
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.READ MORE
A Data Generative Approach to Criminal Justice Records Linkage Problems
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.READ MORE
Open Data Initiative on Criminal Justice
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.READ MORE
Simplistic Collection and Labeling Practices Limit the Generalizability of Benchmark Datasets for Twitter Bot Detection
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.READ MORE
Systemic, socio-demographic differences of content and ads exposure on social media
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.READ MORE
Trajectories of YouTube consumption based on content exposure over time
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.READ MORE
The relations of online consumption and offline behavior: reinforcement of inherent inequalities?
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.READ MORE
Podcast: Data Nation
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!READ MORE
Institute for Data, Systems, and Society (IDSS) is committed to addressing complex societal challenges by advancing education and research at the intersection of statistics, data science, information and decision systems, and social sciences.
The 2023 graduates of IDSS
Congrats to the IDSS graduates of 2023, from our PhDs in Social & Engineering Systems, Master's students in the Technology and Policy Program, and the undergrad and grad students in the MIT Statistics and Data Science Center.
LIDS researchers receive IEEE Transactions on Robotics Best Paper Award
Team including IDSS core faculty Luca Carlone received the award for their paper “Kimera-Multi: Robust, Distributed, Dense Metric-Semantic SLAM for Multi-Robot Systems.”
From ad credibility to shopping habits
SES and IDPS student Amir Tohidi’s research combines economic and psychological insights with advanced computational methods to address interesting real-world problems.