How can we understand and manage the impact of adaptive algorithms that respond to people’s behavior and also influence what people do?
From predictive policing to video recommendations, these algorithms shape outcomes including criminal justice, economic inequality, public health, and social change. Since 2016, CAT Lab has worked alongside communities to investigate the challenges of governing adaptive algorithms, whose interactions with human behavior cannot yet be reliably predicted or assessed.
Impact Assessment of Human-Algorithm Feedback Loops
This review, published by Just Tech at the Social Science Research Council, is a practical tool for regulators, advocates, journalists, scientists, and engineers who are working to assess the impact of adaptive algorithms for social justice.
The review begins by describing areas of social justice impacted by adaptive algorithms. It then describes human-algorithm feedback, names common feedback patterns, and links those patterns to long-standing injustices and opportunities for social change. It outlines fundamental advances that are still needed for effective impact assessment, including new forms of knowledge, new interventions for change, and governance that involves affected communities. The review concludes with high-level recommendations for anyone working to assess the impact of human-algorithm feedback.
- Matias, Nathan and Lucas Wright. “Impact Assessment of Human-Algorithm Feedback Loops.” Just Tech. Social Science Research Council. March 1, 2022. DOI: https://doi.org/10.35650/JT.3028.d.2022
- Open access Zotero library of academic and media resources on the topic.
Suggestions for the U.S. Federal Strategy on AI Research & Development
In March 2020, Lucas Wright and J. Nathan Matias submitted comments based on this work to the White House Office of Science and Technology Policy with recommendations for the U.S. Federal AI Research & Development Strategy. You can read them below:
- Wright, L., Matias, J.N. (2022) Three Suggestions for the US Federal Strategy on AI R&D. Citizens and Technology Lab.
Governing Human-Algorithm Behavior (COMM 4940)
Algorithms that monitor and influence human behavior are everywhere—directing the behavior of law enforcement, managing the world’s financial systems, shaping our cultures, and flipping a coin on the success or failure of movements for change. Since human-algorithm feedback is already a basic pattern in society, we urgently need ways to assess the impact of attempts to steer that feedback toward justice.
In this course for upper-level undergraduates and PhD students, you will learn about the design of adaptive algorithms and the feedback patterns they create with human behavior. You will learn about the challenge they represent for social policy, about ways to research their behavior, and about emerging policy ideas for governing these complex patterns. You will get first-hand experience at diagnosing and attempting to change a feedback system. Along the way, you will hear from pioneers in policy, advocacy, and scholarship.
This course is an excellent stepping stone for anyone interested in a career in policy, advocacy, academia, or industry research.
- Matias, J.N. (2022) COMM 4940: Governing Human-Algorithm Behavior. Citizens and Technology Lab.