A philosophy of technology for computational law
This chapter confronts the foundational challenges posed to legal theory and legal philosophy by the rise of computational ‘law’. Two types will be distinguished, noting that they can be combined into hybrid systems. On the one hand, the use of machine learning in the legal realm will be addressed under the heading of data-driven ‘law’. On the other hand, knowledge- or logic-based expert systems, self-executing contracts or regulation on a blockchain and Rules as Code will be addressed as code-driven ‘law’, which underlies much of automated decision-making. Data-driven ‘law’ raises problems due to its autonomic operations and the ensuing opacity of its reasoning. Code-driven ‘law’ presents us with a conflation of regulation, execution and adjudication. Though such implications are very different, both types of computational ‘law’ share the assumption that legal practice and legal research are computable. Before addressing the implications of these assumptions, the chapter will investig
This chapter confronts the foundational challenges posed to legal theory and legal philosophy by the rise of computational ‘law’. Two types will be distinguished, noting that they can be combined into hybrid systems. On the one hand, the use of machine learning in the legal realm will be addressed under the heading of data-driven ‘law’. On the other hand, knowledge- or logic-based expert systems, self-executing contracts or regulation on a blockchain and Rules as Code will be addressed as code-driven ‘law’, which underlies much of automated decision-making. Data-driven ‘law’ raises problems due to its autonomic operations and the ensuing opacity of its reasoning. Code-driven ‘law’ presents us with a conflation of regulation, execution and adjudication. Though such implications are very different, both types of computational ‘law’ share the assumption that legal practice and legal research are computable. Before addressing the implications of these assumptions, the chapter will investigate the affordances of current, text-driven law, explaining how they relate to the core tenets of the Rule of Law and the kind of legal protection it offers. This will be followed by an enquiry into what computational law would afford in terms of legal protection, assuming that one of the core functions of law and the Rule of Law is to protect what is not computable.
Executive Summary
The article 'A philosophy of technology for computational law' explores the foundational challenges that computational law presents to legal theory and philosophy. It distinguishes between data-driven 'law', which involves machine learning and raises issues of opacity and autonomic operations, and code-driven 'law', which includes expert systems, self-executing contracts, and blockchain-based regulation, conflating regulation, execution, and adjudication. The article argues that both types of computational law assume the computability of legal practice and research, and it investigates the affordances of current text-driven law in relation to the Rule of Law. It then examines what computational law would afford in terms of legal protection, highlighting the need to protect what is not computable.
Key Points
- ▸ Distinction between data-driven and code-driven computational law
- ▸ Challenges posed by the opacity and autonomic operations of data-driven law
- ▸ Conflation of regulation, execution, and adjudication in code-driven law
- ▸ Assumption of computability in legal practice and research
- ▸ Investigation of affordances of text-driven law and the Rule of Law
- ▸ Exploration of legal protection in computational law
Merits
Comprehensive Analysis
The article provides a thorough examination of the different types of computational law and their implications for legal theory and practice.
Critical Perspective
It offers a critical perspective on the assumptions underlying computational law, particularly the notion of computability in legal practice.
Relevance to Rule of Law
The article effectively relates the discussion to the core tenets of the Rule of Law, making it highly relevant to contemporary legal debates.
Demerits
Lack of Empirical Evidence
The article relies heavily on theoretical analysis and could benefit from empirical evidence or case studies to support its arguments.
Limited Practical Solutions
While it identifies challenges, the article does not provide concrete solutions or recommendations for addressing the issues raised.
Complexity for Non-Specialists
The depth of the analysis may make it less accessible to readers who are not well-versed in legal theory or computational law.
Expert Commentary
The article 'A philosophy of technology for computational law' provides a rigorous and well-reasoned analysis of the foundational challenges posed by computational law to legal theory and philosophy. By distinguishing between data-driven and code-driven computational law, the article offers a nuanced understanding of the different types of computational law and their implications. The discussion on the opacity and autonomic operations of data-driven law, as well as the conflation of regulation, execution, and adjudication in code-driven law, highlights the complex nature of these technologies and their potential impact on legal practice. The article's critical perspective on the assumption of computability in legal practice is particularly insightful, as it challenges the notion that all aspects of law can be reduced to algorithms and code. The exploration of the affordances of text-driven law and the Rule of Law, as well as the potential of computational law to enhance legal protection, is highly relevant to contemporary debates about the role of technology in law. However, the article could benefit from empirical evidence or case studies to support its arguments and provide more concrete solutions or recommendations for addressing the identified challenges. Overall, the article makes a valuable contribution to the ongoing discourse on computational law and its implications for legal theory and practice.
Recommendations
- ✓ Incorporate empirical evidence or case studies to strengthen the arguments and provide practical insights
- ✓ Offer concrete recommendations or frameworks for addressing the challenges posed by computational law
- ✓ Simplify the language and provide clear definitions to make the article more accessible to a broader audience
