The IDSS/SSRC Combatting Systemic Racism Seed Fund Program supports innovative, early-stage cross-disciplinary research projects with a focus on combatting systemic racism. Through these grants, IDSS and SSRC seek to encourage researchers from across MIT to collaborate in bringing together new ideas from information and decision systems; data sciences and statistics; and the social sciences to identify and overcome racially discriminatory processes and outcomes across a range of U.S. institutions and policy domains. Proposals addressing a broad range of systemic racism challenges are eligible, including but not limited to housing, healthcare, policing and education.
For AY 2022-2023, we will support the following projects:
An Ethics, Equity, and Justice Audit and Reimagination of Engineering Education
PIs: Catherine D’Ignazio, Cynthia Breazeal, Maria Yang
Contrary to the common belief that engineering systems are objective in their design and functions, occasionally their design is such that it perpetuates systemic racism. With the current way of teaching engineering design, students are under-equipped to consider the societal impacts of their work, and often do not recognize these forms of ingrained racism. Through a series of surveys and analyses, this project aims to dismantle the racist and colonial values historically incorporated and currently perpetuated in technological design, and to reevaluate engineering teaching in regards to the societal impact.
EVDT Integrated Modeling Framework Applied to Measure Environmental Injustice and Socioeconomic Disparities in Prison Landscapes
PIs: Danielle Wood, Justin Steil, Dara Entekhabi
Various studies from recent years have drawn attention to the inadequate environmental conditions of prisons, as they are often exposed to environmental hazards such as air pollution, poor water quality, proximity to hazardous waste facilities, etc. However, this area of research lacks empirical evidence. This project will use geospatial satellite data analysis and, focusing specifically on flood risk, extreme temperatures and air pollution, will be the first comprehensive large-scale study of the risks of these prison environments.
Detecting and Mitigating Multi-Modal medical Misinformation
PIs: Marzyeh Ghassemi, David Rand
Misinformation is becoming prevalent and easily spread on social media. This is especially dangerous when it comes to medical misinformation, as was the case with information about Covid-19 vaccines in recent years. In order to combat this, many social media companies have created AI to detect this misinformation; however, these focus mainly on text-based posts, while memes–or images with embedded text which depend heavily on context– pose a challenge for AIs to identify. This study seeks to better detect memes and holistically interpret their meaning, with the objective to curb the spread of medical misinformation on social media platforms.
Tradeoffs in hotspot predictive policing
PIs: Manish Raghavan, Fotini Christia
In recent years, predictive policing has become the prevalent method of law enforcement, focusing mainly on hotspot policing: sending large amounts of police sources to small areas which are statistically high in crime. However, this method of policing may lead to harmful conflicts of interest with the residents of these areas, which are often areas of low income and minority populations. This study aims to evaluate emerging questions regarding hotspot policing practices, and assess the possible bias of the data upon which these policing practices are carried out.