• Data Nation

    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.

  • Write it like you see it - research figure

    Write It Like You See It

    Clinical notes are becoming an increasingly important data source for machine learning (ML) applications in healthcare. Prior research has shown that deploying ML models can perpetuate existing biases against racial minorities, as bias can be implicitly embedded in data. In this study, we investigate the level of implicit race information available to ML models and human experts and the implications of model-detectable differences in clinical notes. Read more.
    This paper was accepted by the AAAI / ACM Conference on AI, Ethics, and Society 2022.

  • Map of Boston

    Evaluating housing equity – a data-driven approach

    The ICSR Housing project team takes a data-driven approach in evaluating housing equity including evictions, redlining, and predatory lending.

  • Map of Boston

    Beyond Fairness: Reparative Algorithms to Address Historical Injustices of Housing Discrimination in the US

    Fairness in Machine Learning (ML) has mostly focused on interrogating the fairness of a particular decision point with assumptions made that the people represented in the data have been fairly treated throughout history. However, fairness cannot be ultimately achieved if such assumptions are not valid. This is the case for mortgage lending discrimination in the US, which should be critically understood as the result of historically accumulated injustices that were enacted through public policies and private practices including redlining, racial covenants, exclusionary zoning, and predatory inclusion, among others. To emphasize such issues, we introduce case studies using contemporary mortgage lending data as well as historical census data in the US. Read More.

  • Interventions

    Structural Factors and Racial Disparities in Evictions

    What are mechanisms that contribute to racial disparities in evictions? How can we use causal inference methods to evaluate policies that are aimed at reducing evictions? In this poster presentation, authors Aurora Zhang and Anette "Peko" Hosoi find that even after accounting for economic characteristics such as poverty rate, median income, median rent, that there is a significant relationship between neighborhood racial composition and eviction rate. Read more.

  • Camfield Tenants property

    Characterization of Affordable Housing Properties and Policy by Reported Concerns: Boston’s RentSmart Dataset

    Because of the Housing and Urban Renewal Act of 1983, contracts to receive rental subsidies are now renewable but cannot be awarded to new companies. The study hypothesizes that the experiences of residents with property managers are a function of the amount/lack of competition for contracts. Read more.

  • Protest with carboard sign that says End Systemic Racism

    Initiative on Combatting Systemic Racism

    The Initiative on Combatting Systemic Racism (ICSR) aims to launch and coordinate cross-disciplinary research on how to identify and overcome racially discriminatory processes across a range of American institutions and policy domains.

  • Anette Peko Hosoi, wearing a mask, points at a laptop screen

    Covid-19 Testing Impact Calculator

    A new modeling tool helps organizations build tailored Covid-19 testing strategies that can save money and reduce coronavirus spread.

  • graphic showing data at the center of social behavior, institutions, and physical/engineered systems

    Study at the intersection of data, engineering, & social science

    Address society's biggest challenges from an interdisciplinary lens, combining data analysis and social science. Apply for the Social & Engineering Systems PhD.

  • Real Time Mixture-Based Predictions

    As discussed in a previous post, we already know mixtures matter when it comes to COVID-19. Here, we take this idea a step further. We show that the spread of COVID-19 in the United States can meaningfully be described as the mixture of two outbreaks, and use this perspective to form predictions of how the pandemic will evolve. To show both the benefits and drawbacks of our curve-fitting method, we’ve developed a real-time, interactive dashboard for predictions.


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.


MIT News Office | December 5, 2022

A healthy wind

A new study co-authored by IDSS core faculty Noelle Selin and SES alum Minghao Qiu (PhD ’21) finds that the health benefits of using wind energy could quadruple if the most polluting power plants are selected for dialing down.

MIT News Office | November 30, 2022

A breakthrough on “loss and damage,” but also disappointment, at UN climate conference

Delegates from MIT including IDSS core faculty Jessika Trancik attended COP27 in Sharm el-Sheikh, Egypt, where international climate negotiations went down to the wire.

MIT News Office | November 22, 2022

Study: Automation drives income inequality

A newly published study co-authored by IDSS core faculty Daron Acemoglu quantifies the extent to which automation has contributed to income inequality in the U.S.

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