Algorithmic regulation and the rule of law
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 ubiquit
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'.
Executive Summary
The article 'Algorithmic regulation and the rule of law' explores the intersection of technology and legal systems, distinguishing between code-driven and data-driven regulation. It delves into the differences between law and regulation, emphasizing their relevance to the rule of law. The author introduces artificial legal intelligence (ALI) and its applications in quantified legal prediction and argumentation mining, raising questions about the classification of these technologies as law or regulation. The concept of 'agonistic machine learning' is proposed to ensure data-driven regulation adheres to the rule of law, advocating for adversarial interrogation at the computational architecture level.
Key Points
- ▸ Distinction between code-driven and data-driven regulation
- ▸ Relevance of law and regulation to the rule of law
- ▸ Introduction and implications of artificial legal intelligence (ALI)
- ▸ Proposal of 'agonistic machine learning' for rule of law compliance
Merits
Comprehensive Analysis
The article provides a thorough examination of the evolving landscape of legal regulation, offering a clear distinction between code-driven and data-driven regulation.
Innovative Concept
The introduction of 'agonistic machine learning' is a novel and thought-provoking idea that could significantly impact the development and implementation of ALI.
Demerits
Lack of Empirical Evidence
The article lacks empirical data to support the proposed concepts and their potential impact on legal systems.
Complexity
The concepts discussed are highly complex and may be difficult for non-specialists to grasp, limiting the article's accessibility.
Expert Commentary
The article presents a compelling exploration of the intersection between algorithmic regulation and the rule of law. The distinction between code-driven and data-driven regulation is particularly insightful, as it highlights the evolving nature of legal systems in the face of technological advancements. The introduction of artificial legal intelligence (ALI) and its potential applications in quantified legal prediction and argumentation mining raises important questions about the classification and regulation of these technologies. The proposal of 'agonistic machine learning' as a means to ensure compliance with the rule of law is innovative and could have significant implications for the development and implementation of ALI. However, the article would benefit from empirical evidence to support these concepts and their potential impact on legal systems. Additionally, the complexity of the discussed concepts may limit the article's accessibility to non-specialists. Overall, the article contributes valuable insights to the ongoing discussion on the integration of AI in legal practices and the need for robust regulatory frameworks.
Recommendations
- ✓ Further empirical research should be conducted to validate the proposed concepts and their potential impact on legal systems.
- ✓ Efforts should be made to simplify the presentation of complex concepts to enhance the article's accessibility to a broader audience.
