Building off the success of the Bias in Big Data workshop, ISGMH’s CONNECT Research Program is leading a new monthly newsletter to connect ethically-minded scientists, researchers, students, and community members with insights, resources, and each other. This #BiasinBigData newsletter will promote the ethical and accurate use of data in understanding the health of populations by centering marginalized voices, amplifying leaders in ethical data science, breaking down silos, and highlighting action steps and resources that can be used today. Sign up here.
White Paper and Workshop
In the name of efficiency, our society increasingly relies on data to guide all forms of decision making. This cost-effective, data-led decision making, particularly when guided by unsupervised analytical methods, is often assumed to be free of human bias. However, there is growing concern about the potential misuse of these methods to further oppress already marginalized populations. From hiring decisions, to predictive policing, to auto insurance premiums, poor black and brown populations have been shown to be disproportionately impacted across a wide variety of domains. Less is known however about the impact of these systems on sexual and gender minority (SGM) populations.
Bias in Big Data was a workshop organized by the CONNECT Research Program. The workshop sought to stimulate intersectional discussion about the role of bias in big data and to explore, in particular, how bias in data and data science impacts the health of sexual and gender minority populations. The workshop was hosted in Chicago and live streamed to ensure broad and inclusive participation at no charge.
Video recordings, presentation slides, and digital programs from the 2019 Bias in Big Data workshop can be found here.
Following the workshop, CONNECT wanted to provide an accessible summary of the conversations that were had throughout the day, along with recommendations that were made by speakers and workshop attendees. This white paper is a living recollection for both the people that were present for the discussion and those who want to learn and do more to challenge bias in big data and data science.
While this document is catered towards data scientists, community members, researchers, policy makers, and academics, we encourage anyone who is interested in the topic of bias in big data to read on.
We hope this document provides an accurate summary of the workshop as well as allows greater understanding of how data may be used to further harm historically marginalized people, and inspires readers to take meaningful action wherever they are able.
The Bias in Big Data workshop aimed to bring together a diverse group of scientists, students, and community leaders at the intersection of technology, data science, and health equity to discuss bias in big data, how bias impacts all marginalized populations, and how bias may specifically impact sexual and gender minority communities.
The workshop was organized by Dr. Michelle Birkett, Assistant Professor of Medical Social Sciences and Director of the CONNECT Research Program on Complex Systems and Health Disparities at Northwestern University. CONNECT’s research focuses on understanding how multi-level mechanisms drive health disparities in stigmatized populations, with an emphasis on approaches using big data and network science, and on applying complex, cutting-edge methods to advance health equity research.