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The 2024–25 judicial reform in Mexico represents one of the most sweeping overhauls of a national judiciary in recent history. The reform replaced nearly all federal and state judges through direct popular elections, restructured the country’s highest court, and created new administrative and disciplinary bodies that expanded political influence over judicial decision-making. Here, in a conversation with Judicature International, AMANDA DRISCOLL, the Dr. J. Elizabeth Garraway Professor of Political Science at Florida State University, MICHAEL J. NELSON, Professor and Head of the Department of Political Science at Penn State, and JULIO RÍOS-FIGUEROA, Professor of Law at the Instituto Tecnológico Autónomo de México (ITAM) in Mexico City, bring their expertise on comparative judicial politics and the Mexican legal system. They analyze the design, implementation, and consequences of these reforms, assessing what they reveal about judicial independence, democratic legitimacy, and the potential risks of politicizing the judiciary.
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"Procurement As Policy: Administrative Process for Machine Learning."
At every level of government, officials contract for technical systems that employ machine learning-systems that perform tasks without using explicit instructions, relying on patterns and inference instead. These systems frequently displace discretion previously exercised by policymakers or individual front-end government employees with an opaque logic that bears no resemblance to the reasoning processes of agency personnel. However, because agencies acquire these systems through government procurement processes, they and the public have little input into-or even knowledge about-their design or how well that design aligns with public goals and values. This Article explains the ways that the decisions about goals, values, risk, and certainty, along with the elimination of case-by-case discretion, inherent in machine-learning system design create policies-not just once when they are designed, but over time as they adapt and change. When the adoption of these systems is governed by procurement, the policies they embed receive little or no agency or outside expertise beyond that provided by the vendor. Design decisions are left to private third-party developers. There is no public participation, no reasoned deliberation, and no factual record, which abdicates Government responsibility for policymaking. This Article then argues for a move from a procurement mindset to policymaking mindset. When policy decisions are made through system design, processes suitable for substantive administrative determinations should be used: processes that foster deliberation reflecting both technocratic demands for reason and rationality informed by expertise, and democratic demands for public participation and political accountability. Specifically, the Article proposes administrative law as the framework to guide the adoption of machine learning governance, describing specific ways that the policy choices embedded in machine learning system design fail the prohibition against arbitrary and capricious agency actions absent a reasoned decision-making process that both enlists the expertise necessary for reasoned deliberation about, and justification for, such choices, and makes visible the political choices being made. Finally, this Article sketches models for machine-learning adoption processes that satisfy the prohibition against arbitrary and capricious agency actions. It explores processes by which agencies might garner technical expertise and overcome problems of system opacity, satisfying administrative law's technocratic demand for reasoned expert deliberation. It further proposes both institutional and engineering design solutions to the challenge of policymaking opacity, offering process paradigms to ensure the "political visibility" required for public input and political oversight. In doing so, it also proposes the importance of using "contestable design"-design that exposes value-laden features and parameters and provides for iterative human involvement in system evolution and deployment. Together, these institutional and design approaches further both administrative law's technocratic and democratic mandates.
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- Award ID(s):
- 1650589
- PAR ID:
- 10214677
- Date Published:
- Journal Name:
- Berkeley technology law journal
- Volume:
- 34
- Issue:
- 3
- ISSN:
- 1086-3818
- Page Range / eLocation ID:
- 773-852
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
-
Accepted Manuscript
- Journal Article:
- https://doi.org/10.2139/ssrn.3464203
