Applications of emerging technology like artificial intelligence (AI) and machine learning (ML) are expanding in environmental, health, and social science, and researchers must be informed, not only of these tools, but of practical and ethical considerations for their use. SSEHRI joins an interdisciplinary group of scholars in hosting a webinar series, “AI/ML Fairness and Bias with Applications to Environmental Justice” about emerging technologies and their ethical implications. These webinars are part of an NIEHS-funded project that aims to address a shortage of AI/ML-trained researchers and introduce scientists to data ethics principles. This series is led by Dr. Phil Brown and SSEHRI postdoc Kim Garrett.

Webinar 1: Intro to Artificial Intelligence & Machine Learning, Creating an Ethics Ecosystem, and Applications to Environmental Science

The inaugural webinar features introductions to AI/ML, big data, and ethical frameworks for addressing technology and data in research. We also hear from two researchers using AI and ML in the field, sharing their perspectives on data justice, exposure science, and their connections to social justice.
This session features:
Justin Manjourides, Northeastern University Department of Health Sciences, OHDSI Center, and the Roux Institute
John Basl, Northeastern University Department of Philosophy and Religion, Northeastern Ethics Institute
Lourdes Vera, University of Buffalo Department of Sociology
Abhishek Viswanathan, University of Pittsburgh School of Computer and Information Science

Transcript forthcoming

Webinar 2: Datafying Justice and Accessibility in Design

This session highlights the need for ethical approaches to data and technology through the lens of the philosophy of science and disability justice. Our speakers share their research and lived experience with human-technology interaction and provide insight into making science and technology accessible for all.
This session features:
Lorena Jaume-Palasì, The Ethical Tech Society
Emily Ackerman, Harvard Medical School

Transcript available at