The Common Threats of Artificial Intelligence and Privatization

Administrative agencies’ growing use of automated decisionmaking tools poses threats to core democratic values, such as agency flexibility, expertise, fairness, transparency, and accountability. But decades of privatization have wrought similar, lasting harms to the United States’ public institutions. This Article argues that the thoughtful criticisms and prescriptions from the burgeoning literature on the government’s use of artificial intelligence should be used to strengthen the scrutiny accorded to privatization. Specifically, this Article challenges the perception that automated decisionmaking poses a greater threat to public values than privatization. Indeed, the two share several characteristics and goals. These include, for example, a fixation on efficiency, reliance on oversimplified cost-benefit analyses, erosion of agency expertise and resources, and separation between public officials and their decisions’ impact on individuals. In that separation, algorithms and private actors make important decisions often carrying political and fairness consequences. As policymakers adapt the latest expert guidance regarding algorithms to the problems of privatization, they should prioritize the needs and voices of the marginalized individuals who have been most harmed by the privatization movement.

Authors

Publication Details

Journal
Texas A&M Law Review
Published
2025-03-01
DOI
https://doi.org/10.37419/lr.v12.i2.8
Citations
1
Primary Topic
Digital Transformation in Law
Type
article
Field-Weighted Citation Impact
3.09
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article

The Common Threats of Artificial Intelligence and Privatization

Landyn Rookard
1 citations
Texas A&M Law Review
Digital Transformation in Law
3.09
article

The Common Threats of Artificial Intelligence and Privatization

Landyn Rookard
article en
1 citations

Abstract

Administrative agencies’ growing use of automated decisionmaking tools poses threats to core democratic values, such as agency flexibility, expertise, fairness, transparency, and accountability. But decades of privatization have wrought similar, lasting harms to the United States’ public institutions. This Article argues that the thoughtful criticisms and prescriptions from the burgeoning literature on the government’s use of artificial intelligence should be used to strengthen the scrutiny accorded to privatization. Specifically, this Article challenges the perception that automated decisionmaking poses a greater threat to public values than privatization. Indeed, the two share several characteristics and goals. These include, for example, a fixation on efficiency, reliance on oversimplified cost-benefit analyses, erosion of agency expertise and resources, and separation between public officials and their decisions’ impact on individuals. In that separation, algorithms and private actors make important decisions often carrying political and fairness consequences. As policymakers adapt the latest expert guidance regarding algorithms to the problems of privatization, they should prioritize the needs and voices of the marginalized individuals who have been most harmed by the privatization movement.

Texas A&M Law ReviewVol. 12(2)
Openalex Percentile: Top 13%
Digital Transformation in Law
3.09
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The Common Threats of Artificial Intelligence and Privatization — Landyn Rookard · Texas A&M Law Review (2025) | TGRS Research Map | TGRS