Governing Digital Legal Systems: Insights on Artificial Intelligence and Rules as Code · MIT Computational Law Report

AI and Doctrinal Collapse

Artificial intelligence runs on data. But the two legal regimes that govern data—information privacy law and copyright law—are under pressure. Formally, each re

Automated Justice: Issues, Benefits and Risks in the Use of Artificial Intelligence and Its Algorithms in Access to Justice and Law Enforcement

The use of artificial intelligenceArtificial Intelligence (AI) (AI) in the field of law has generated many hopes. Some have seen it as a way of relieving courts’ congestion, facilitating investigations, and making sentences for certain offences more consistent—and therefore fairer. But while it is true that the work of investigators and judges can be facilitated by these tools, particularly in terms of finding evidenceEvidence during the investigative process, or preparing legal summaries, the panorama of current uses is far from rosy, as it often clashes with the reality of field usage and raises serious questions regarding human rightsHuman rights. This chapter will use the RobodebtRobodebt Case to explore some of the problems with introducing automationAutomation into legal systems with little human oversight. AI—especially if it is poorly designed—has biases in its data and learning pathways which need to be corrected. The infrastructures that carry these tools may fail, introducing novel bias. All these elements are poorly understood by the legal world and can lead to misuse. In this context, there is a need to identify both the users of AIArtificial Intelligence (AI) in the area of law and the uses made of it, as well as a need for transparencyTransparency, the rules and contours of which have yet to be established.

Automated Justice: Issues, Benefits and Risks in the Use of Artificial Intelligence and Its Algorithms in Access to Justice and Law Enforcement

The use of artificial intelligenceArtificial Intelligence (AI) (AI) in the field of law has generated many hopes. Some have seen it as a way of relieving courts’ congestion, facilitating investigations, and making sentences for certain offences more consistent—and therefore fairer. But while it is true that the work of investigators and judges can be facilitated by these tools, particularly in terms of finding evidenceEvidence during the investigative process, or preparing legal summaries, the panorama of current uses is far from rosy, as it often clashes with the reality of field usage and raises serious questions regarding human rightsHuman rights. This chapter will use the RobodebtRobodebt Case to explore some of the problems with introducing automationAutomation into legal systems with little human oversight. AI—especially if it is poorly designed—has biases in its data and learning pathways which need to be corrected. The infrastructures that carry these tools may fail, introducing novel bias. All these elements are poorly understood by the legal world and can lead to misuse. In this context, there is a need to identify both the users of AIArtificial Intelligence (AI) in the area of law and the uses made of it, as well as a need for transparencyTransparency, the rules and contours of which have yet to be established.

Legislation as Code | Version: 2

Deploy documents as digital infrastructure

Going beyond the “common suspects”: to be presumed innocent in the era of algorithms, big data and artificial intelligence

This article explores the trend of increasing automation in law enforcement and criminal justice settings through three use cases: predictive policing, machine evidence and recidivism algorithms. The focus lies on artificial-intelligence-driven tools and technologies employed, whether at pre-investigation stages or within criminal proceedings, in order to decode human behaviour and facilitate decision-making as to whom to investigate, arrest, prosecute, and eventually punish. In this context, this article first underlines the existence of a persistent dilemma between the goal of increasing the operational efficiency of police and judicial authorities and that of safeguarding fundamental rights of the affected individuals. Subsequently, it shifts the focus onto key principles of criminal procedure and the presumption of innocence in particular. Using Article 6 ECHR and the Directive (EU) 2016/343 as a starting point, it discusses challenges relating to the protective scope of presumption of innocence, the burden of proof rule and the in dubio pro reo principle as core elements of it. Given the transformations law enforcement and criminal proceedings go through in the era of algorithms, big data and artificial intelligence, this article advocates the adoption of specific procedural safeguards that will uphold rule of law requirements, and particularly transparency, fairness and explainability. In doing so, it also takes into account EU legislative initiatives, including the reform of the EU data protection acquis, the E-evidence Proposal, and the Proposal for an EU AI Act. Additionally, it argues in favour of revisiting the protective scope of key fundamental rights, considering, inter alia, the new dimensions suspicion has acquired.

Going beyond the “common suspects”: to be presumed innocent in the era of algorithms, big data and artificial intelligence

This article explores the trend of increasing automation in law enforcement and criminal justice settings through three use cases: predictive policing, machine evidence and recidivism algorithms. The focus lies on artificial-intelligence-driven tools and technologies employed, whether at pre-investigation stages or within criminal proceedings, in order to decode human behaviour and facilitate decision-making as to whom to investigate, arrest, prosecute, and eventually punish. In this context, this article first underlines the existence of a persistent dilemma between the goal of increasing the operational efficiency of police and judicial authorities and that of safeguarding fundamental rights of the affected individuals. Subsequently, it shifts the focus onto key principles of criminal procedure and the presumption of innocence in particular. Using Article 6 ECHR and the Directive (EU) 2016/343 as a starting point, it discusses challenges relating to the protective scope of presumption of innocence, the burden of proof rule and the in dubio pro reo principle as core elements of it. Given the transformations law enforcement and criminal proceedings go through in the era of algorithms, big data and artificial intelligence, this article advocates the adoption of specific procedural safeguards that will uphold rule of law requirements, and particularly transparency, fairness and explainability. In doing so, it also takes into account EU legislative initiatives, including the reform of the EU data protection acquis, the E-evidence Proposal, and the Proposal for an EU AI Act. Additionally, it argues in favour of revisiting the protective scope of key fundamental rights, considering, inter alia, the new dimensions suspicion has acquired.

Where AI Regulation Stands Today

The White House has released a National Artificial Intelligence Legislative Framework and new executive orders aiming to establish a single, nationwide standard for AI regulation...

Framework Convention on Artificial Intelligence

The Framework Convention on Artificial Intelligence and Human Rights, Democracy and the Rule of Law (also called Framework Convention on Artificial Intelligence or AI convention) is an international treaty on artificial intelligence. It was adopted under the auspices of the Council of Europe (CoE) and signed on 5 September 2024.[1] The treaty aims to ensure that the development and use of AI technologies align with fundamental human rights, democratic values, and the rule of law, addressing risks such as misinformation, algorithmic discrimination, and threats to public institutions.[2]

Agentic AI Governance: A Strategic Framework for Autonomous Systems

Agentic AI is moving from chat to action. Learn how to govern autonomous systems using the "Digital Contractor" framework and the 3-Tiered Guardrail system.

Code of Practice on Transparency of AI-generated Content

This code of practice supports compliance with the AI Act transparency obligations related to marking and labelling of AI-generated content.

AI Agent Standards: Navigating New NIST Governance | Nemko Digital

NIST's new AI agent standards are here. Learn what they mean for AI governance, compliance, and liability. Get ahead of the new regulations.

Agentic AI Governance: Securing Autonomous AI Agents in Enterprise

When AI agents start making decisions, calling tools, and coordinating with other agents without waiting for human approval, the governance playbook most...

Can AI Be Governed? Only If We Build Normatively Competent AI

Hadfield's discussion brings the idea of AI governance back to the core idea of steering the behavior of an AI system. As she notes, this not only involves technical questions about how AI systems ar...

Artificial Intelligence Act

The Artificial Intelligence Act is a European Union regulation concerning artificial intelligence (AI). It establishes a common regulatory and legal framework for AI within the European Union (EU). The regulation entered into force on 1 August 2024, with provisions that shall come into operation gradually over the following 6 to 36 months.

Government to ease data consent rules for AI development | The Asahi Shimbun Asia & Japan Watch

To accelerate artificial intelligence development, the government plans to relax consent requirements for access to personal information while introducing tougher penalties for intentional misuse.