Disaggregated Interventions to Reduce Inequality.
Lucius Bynum, Joshua R. Loftus & Julia Stoyanovich. ACM Conference on Equity and Access in Algorithms, Mechanisms, and Optimization (2021).
The authors propose an “impact remediation framework” that measures real-world disparities and discovers optimal intervention policies that could help improve equity or access to opportunity for those who are underserved with respect to an outcome of interest. The framework draws on insights from the social sciences brought into the realm of causal modeling and constrained optimization. Read the article.
A Silicon Valley Love Triangle: Hiring Algorithms, Pseudo-Science, and the Quest for Auditability
Sloane, M., Moss, E., & Chowdhury, R. (2021), A Silicon Valley Love Triangle: Hiring Algorithms, Pseudo-Science, and the Quest for Auditability. Computers and Society.
Mona Sloane and her co-authors suggest a matrix to expose underlying assumptions rooted in pseudoscientific understandings of human nature and capability, and to critically investigate emerging auditing standards and practices that fail to address these assumptions. Read the article
COVID-19 Brings Data Equity Challenges to the Fore
H.V. Jagadish, Julia Stoyanovich & Bill Howe. ACM Digital Government Research and Practice (2021).
The COVID-19 pandemic is compelling us to make crucial data-driven decisions quickly, bringing together diverse and unreliable sources of information without the usual quality control mechanisms we may employ. These decisions are consequential, and they may give rise to, reinforce, and propagate significant inequities. In this article, the authors propose a framework, called FIDES, for surfacing and reasoning about data equity in such systems. Read the article.
Teaching Responsible Data Science: Charting New Pedagogical Territory
Armanda Lewis & Julia Stoyanovich. International Journal of Artificial Intelligence in Education. 2021.
The authors recount the experience of developing and teaching a technical course focused on responsible data science, which tackles the issues of ethics in AI, legal compliance, and data protection among other areas. They also propose pedagogical methods for responsible data science education. Read the article.