Machine learning and the re-enchantment of the administrative state

Machine learning algorithms present substantial promise for more effective decision-making by administrative agencies.However, some of these algorithms are inscrutable, namely, they produce predictions that humans cannot understand or explain. This trait is in tension with the emphasis on reason-giv...

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Bibliographic Details
Published in:Modern law review 2024-03, Vol.87 (2), p.371-397
Main Authors: Sarid, Eden, Ben‐Zvi, Omri
Format: Article
Language:English
Subjects:
Administrative agencies
Administrative law
Algorithms
Artificial intelligence
Decision making
Machine learning
Public law
Public life
Weber, Max (1864-1920)
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Summary:Machine learning algorithms present substantial promise for more effective decision-making by administrative agencies.However, some of these algorithms are inscrutable, namely, they produce predictions that humans cannot understand or explain. This trait is in tension with the emphasis on reason-giving in administrative law. The article explores this tension, advancing two interrelated arguments. First, providing adequate reasons is a significant facet of respecting individuals' agency. Incorporating inscrutable algorithmic predictions into administrative decision-making compromises this normative ideal. Second, as a long-term concern, the use of inscrutable algorithms by administrative agencies may generate systemic effects by gradually reducing the realm of the humanly explainable in public life, a phenomenon Max Weber termed 're-enchantment'. As a result, the use of inscrutable machine learning algorithms might trigger a special kind of re-enchantment,making us comprehend less rather than more of shared human experience, and consequently altering the way we understand the administrative state and experience public life.
ISSN:0026-7961
1468-2230
DOI:10.1111/1468-2230.12843