See the uncensored version on Nebula: https://go.nebula.tv/philosophytube Support my work on Patreon: https://www.patreon.com/PhilosophyTube Subscribe! http://tinyurl.com/pr99a46 Twitter: @PhilosophyTube Instagram, TikTok, Tumblr, BlueSky, Threads: @theabigailthorn Facebook: https://www.facebook.com/PhilosophyTube/ MERCH: https://store.nebula.tv/collections/philosophy-tube Email: philosophytubebusiness@gmail.com CHAPTERS: 00:00 - 01:19 Intro 01:19 - 05:51Trying to Define Law 05:51 - 08:42 Let's Ask A Lawyer! 08:42 - 14:44 Hart's Legal Positivism 14:44 - 34:44 British Policing & the Law 34:44 - 37:43 Rules with "Open Texture" 37:43 - 41:40 Case Study - Operation Soap 41:40 - 48:56 Vague Laws MUSIC: The Dark Glow of the Mountains by Chris Zabriskie is licensed under a Creative Commons Attribution 4.0 license. https://creativecommons.org/licenses/by/4.0/ Source: http://chriszabriskie.com/darkglow/ Artist: http://chriszabriskie.com/ 'Illegal' by The Cucumbers www.thecucumbers.net Corset Belt by Pritch London: https://pritchlondon.com/products/cut-out-corset-belt-pitch-black Latex Bikini by Dead Lotus Couture: https://www.deadlotuscouture.com/ BIBLIOGRAPHY: Giorgio Agamben, State of Exception Hrafn Asgeirsson, “On the Instrumental Value of Vagueness in the Law,” in Ethics John Austin, The Province of Jurisprudence Determined Iain Donnelly, Tango Juliet Foxtrot Ronald Dworkin, “Introduction,” in Taking Rights Seriously Ronald Dworkin, “Jurisprudence,” in Taking Rights Seriously Ronald Dworkin, “The Model of Rules I,” in Taking Rights Seriously Ronald Dworkin, “The Model of Rules II,” in Taking Rights Seriously Timothy Endicott, “The Value of Vagueness,” in The Philosophical Foundations of Language in the Law John Gardner, “Legal Positivism: 5 ½ Myths”, in American Journal of Jurisprudence John Gardner, “The Virtue of Justice and the Character of Law,” in Current Legal Problems Nadia Guidotto, “Looking Back: The Bathhouse Raids in Toronto, 1981,” in Captive Genders H.L.A. Hart, The Concept of Law Scott Hershovitz, “The End of Jurisprudence”, in Yale Law Journal LawExplorer Blog, “Dworkin’s ‘Law As Integrity’ Andrei Marmor, “Law as Authoritative Fiction,” in Law and Philosophy Jolyon Maugham, Bringing Down Goliath Jolyon Maugham, “No, The Legal System Isn’t Biased Against Men - It Allows them to R*pe with Near Impunity,” in New Statesman Tommi Avicolli Mecca, “Brushes with Lily Law,” in Captive Genders Michael Moore, “Hart’s Concluding Unscientific Postscript,” in Legal Theory Office of National Statistics, “Police powers and procedures: Stop and search and arrests, England and Wales, year ending 31 March 2022” Joseph Raz, “The Problem About the Nature of Law,” in Ethics In The Public Domain Joseph Raz, “Authority, Law, and Morality,” in Ethics In The Public Domain Joseph Raz, “The Politics of the Rule of Law,” in Ethics in the Public Domain Joseph Raz, “Legal Principles and the Limits of Law,” in Colombia Law School Scott J. Shapiro, “The “Hart-Dworkin” Debate: A Short Guide for the Perplexed” Judith Shklar, “Political Theory and the Rule of Law,” in Political Thought and Political Thinkers Jon Stone, “Liz Truss To Give Government Powers to Override Human Rights Court,” in The Independent Thoughtslime, “All Cops Are Bad” Daniel Trilling, “Not Much Like Consent,” in The London Review of Books Trashfuture, “Bad Boys” Wesley Ware, “Rounding Up the Homosexuals,” in Captive Genders #police #law #cops
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Directing large language models to follow the letter or spirit of the law
The distinction between the spirit and letter of the law is a central issue across research and everyday life, and a growing concern for building safe, intelligent machines. What is this distinction based on, and how can we develop machines that follow the intention behind a rule? We used targeted adaptation that made large language models prioritize the spirit or letter of the law. With minimal modifications, our method significantly changed LLM behavior across diverse measures, novel vignettes, real-world scenarios, and influential legal cases. An analysis of model internals revealed a low-dimensional space with three interpretable dimensions matching a formal pre-specified framework for the geometry of legal concepts. These findings show how legal thought in LLMs may be organized and directed.
Predictive Policing and the Politics of Patterns
Abstract Patterns are the epistemological core of predictive policing. With the move towards digital prediction tools, the authority of the pattern is rearticulated and reinforced in police work. Based on empirical research about predictive policing software and practices, this article puts the authority of patterns into perspective. Introducing four ideal-typical styles of pattern identification, we illustrate that patterns are not based on a singular logic, but on varying rationalities that give form to and formalize different understandings about crime. Yet, patterns render such different modes of reasoning about crime, and the way in which they feed back into policing cultures, opaque. Ultimately, this invites a stronger reflection about the political nature of patterns.
Police Abolition | The Paper Pilot
The Paper Pilot's digital garden of thoughts on philosophy, politics, and sociology
Editor's Notes: Can We Defy Courts While Believing In Rule of Law?
Do our tactics throw out democracy and rule of law while purportedly aiming at saving them?
Hallucination-Free? Assessing the Reliability of Leading AI Legal Research Tools
Legal practice has witnessed a sharp rise in products incorporating artificial intelligence (AI). Such tools are designed to assist with a wide range of core legal tasks, from search and summarization of caselaw to document drafting. But the large language models used in these tools are prone to "hallucinate," or make up false information, making their use risky in high-stakes domains. Recently, certain legal research providers have touted methods such as retrieval-augmented generation (RAG) as "eliminating" (Casetext, 2023) or "avoid[ing]" hallucinations (Thomson Reuters, 2023), or guaranteeing "hallucination-free" legal citations (LexisNexis, 2023). Because of the closed nature of these systems, systematically assessing these claims is challenging. In this article, we design and report on the first preregistered empirical evaluation of AI-driven legal research tools. We demonstrate that the providers' claims are overstated. While hallucinations are reduced relative to general-purpose chatbots (GPT-4), we find that the AI research tools made by LexisNexis (Lexis+ AI) and Thomson Reuters (Westlaw AI-Assisted Research and Ask Practical Law AI) each hallucinate between 17% and 33% of the time. We also document substantial differences between systems in responsiveness and accuracy. Our article makes four key contributions. It is the first to assess and report the performance of RAG-based proprietary legal AI tools. Second, it introduces a comprehensive, preregistered dataset for identifying and understanding vulnerabilities in these systems. Third, it proposes a clear typology for differentiating between hallucinations and accurate legal responses. Last, it provides evidence to inform the responsibilities of legal professionals in supervising and verifying AI outputs, which remains a central open question for the responsible integration of AI into law.
12 Commonsense Safeguards - staffny
Officers must list the law being violated, provide a case number, and explain the suspicion tied to a specific vehicle or plate. Limit use only to suspected fel...
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.
Governing Digital Legal Systems: Insights on Artificial Intelligence and Rules as Code · MIT Computational Law Report
This article explores how AI and 'rules as code' are turning law into automated systems. It highlights the need for governance focused on transparency, explainability, and risk management to ensure these digital legal frameworks stay reliable and fair.
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.
Promises and pitfalls of artificial intelligence for legal applications
Is AI set to redefine the legal profession? We argue that this claim is not supported by the current evidence. We dive into AI's increasingly prevalent roles in three types of legal tasks: information processing; tasks involving creativity, reasoning, or judgment; and predictions about the future. We find that the ease of evaluating legal applications varies greatly across legal tasks, based on the ease of identifying correct answers and the observability of information relevant to the task at hand. Tasks that would lead to the most significant changes to the legal profession are also the ones most prone to overoptimism about AI capabilities, as they are harder to evaluate. We make recommendations for better evaluation and deployment of AI in legal contexts.
AI Hallucination Cases Database – Damien Charlotin
The most comprehensive database of AI hallucination cases in law: legal decisions from courts worldwide, searchable by country, party, AI tool, and outcome. Updated daily.