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Cited by 222Artificial Intelligence in LawDigitalization, Law, and RegulationEthics and Social Impacts of AI

Algorithmic regulation and the rule of law

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Hildebrandt, Mireille·2018·Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences
DOI 10.1098/rsta.2017.0355
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Abstract

In this brief contribution, I distinguish between code-driven and data-driven regulation as novel instantiations of legal regulation. Before moving deeper into data-driven regulation, I explain the difference between law and regulation, and the relevance of such a difference for the rule of law. I discuss artificial legal intelligence (ALI) as a means to enable quantified legal prediction and argumentation mining which are both based on machine learning. This raises the question of whether the implementation of such technologies should count as law or as regulation, and what this means for their further development. Finally, I propose the concept of ‘agonistic machine learning’ as a means to bring data-driven regulation under the rule of law. This entails obligating developers, lawyers and those subject to the decisions of ALI to re-introduce adversarial interrogation at the level of its computational architecture. This article is part of a discussion meeting issue ‘The growing ubiquity of algorithms in society: implications, impacts and innovations'.

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  • Algorithmic decision-making is usually regarded as incapable of exercising discretion, since there is no room for manoeuvre in a deterministic system.

    “Algorithmic decision-making is usually regarded as incapable of exercising this kind of discretion, since there is no room for manoeuvre in a deterministic system.”

    From Human Judgment in algorithmic loops: Individual justice and automated <scp>decision‐making</scp> (2022) · cites this paper as Hildebrandt 2018

    Section 2 says code-driven decision systems are deterministic and have “no discretion whatsoever,” supporting the claim for that type of algorithmic decision-making. But the section also distinguishes data-driven systems and describes a new type of discretion in their design, so the claim is broader than the paper's account. (LLM, conf=0.84)

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Cites

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Research works printed but not linked: 30

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Cited by

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Authors
Hildebrandt, M.
Affiliations
Vrije Universiteit Brussel (BE)
Corresponding author
Hildebrandt, Mireille
ORCID
Hildebrandt, Mireille
Venue
Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences
Publisher
The Royal Society
Published
2018-08-06
Volume
376
Issue
2128
Pages
20170355
ISSN
  • 1364-503X (ISSN-L)
  • 1471-2962
DOI
10.1098/rsta.2017.0355
OpenAlex
W2887660369
Cited by
222
References
9
Citations by year
2026: 20 · 2025: 25 · 2024: 27 · 2023: 70 · 2022: 31 · 2021: 17 · 2020: 15 · 2019: 15 · 2018: 2
FWCI
53.31
Type
journal article
Language
en
Open access
Licence
royalsociety.org/journals/ethics-policies/data-sharing-mining/ (tdm)
Locations
  • Philosophical Transactions of the Royal Society A Mathematical Physical and Engineering Sciences (publishedVersion)
  • PubMed (publishedVersion)
  • Radboud Repository (Radboud University) (submittedVersion, other-oa)
  • Radboud Repository (Radboud University) (submittedVersion)
  • VUBIR (Vrije Universiteit Brussel) (submittedVersion)
Topics
  • Social Sciences › Political Science and International Relations › Artificial Intelligence in Law
  • Social Sciences › Law › Digitalization, Law, and Regulation
  • Social Sciences › Safety Research › Ethics and Social Impacts of AI
Keywords
algorithmic regulation, rule of law, data-driven regulation, legal prediction, argument mining, machine learning, legal technology

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