Net versus relative impacts in public policy automation: a conjoint
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The use of algorithms and automated systems, especially those leveraging artificial intelligence (AI), has been exploding in
the public sector, but their use has been controversial. Ethicists, public advocates, and legal scholars have debated whether
biases in AI systems should bar their use or if the potential net benefits, especially toward traditionally disadvantaged groups,
justify even greater expansion. While this debate has become voluminous, no scholars of which we are aware have conducted
experiments with the groups affected by these policies about how they view the trade-offs. We conduct a set of two conjoint
experiments with a high-quality sample of 973 Americans who identify as Black or African American in which we randomize
the levels of inter-group disparity in outcomes and the net effect on such adverse outcomes in two highly controversial contexts:
pre-trial detention and traffic camera ticketing. The results suggest that respondents are willing to tolerate some level
of disparity in outcomes in exchange for certain net improvements for their community. These results turn this debate from
an abstract ethical argument into an evaluation of political feasibility and policy design based on empirics.
For more information on the following article, please contact:
Dr. Michael O. Adams
Founding Director, eMPA Program
Professor, Political Science
Co-author
Texas Southern University
Phone: 713-418-0987
Email: Michael.Adams@tsu.edu
