the admissibility of AI- generated evidence

Durante as duas últimas décadas, a Inteligência Artificial tornou-se uma presença constante nas nossas vidas. Ao impactar setores relevantes da sociedade, tem relevando o seu caráter disruptivo, sendo um dos motores impulsionadores da Quarta Revolução Industrial. A Inteligência Artificial além dos seus presentes benefícios para a humanidade, promete soluções inovadoras para os problemas que afligem a sociedade contemporânea, porém a mesma comporta uma duplicidade de efeitos. Os sistemas de Inteligência Artificial pela sua capacidade de monitorizar o seu ambiente circundante, e autonomamente recolher, processar dados, aprender e agir, podem concretizar riscos para os direitos fundamentais, principalmente no contexto da justiça criminal. Esta análise irá focar-se nas especificidades dos sistemas dotados de Inteligência Artificial, aprofundando a temática da admissibilidade da prova gerada por Inteligência Artificial no quadro probatório do Direito Processual Penal Português à luz dos direitos de defesa do arguido e dos seus princípios que norteadores.During the last two decades Artificial Intelligence became ubiquitous in our lives. Revealing itself as a disruptive technology, it is already impacting important sectors of society, being a driver of the Fourth Industrial Revolution. Artificial Intelligence is benefiting humanity, and promises innovative solutions to modern-life problems, nevertheless it has a twofold effect. Artificial Intelligence as systems that are capable to monitor their surrounding environment, autonomously collect and process data, learn and act, may constitute harm to fundamental rights, mainly when deployed to criminal justice. This analysis will focus on the specificities of Artificial Intelligence systems, delving into the admissibility of AI-generated evidence in the Portuguese criminal evidentiary framework in light of the defence rights and structuring principles of Portuguese criminal procedure

The GPTJudge: Justice in a Generative AI World

Generative AI (“GenAI”) systems such as ChatGPT recently have developed to the point where they can produce computer-generated text and images that are difficult to differentiate from human-generated text and images. Similarly, evidentiary materials such as documents, videos, and audio recordings that are AI-generated are becoming increasingly difficult to differentiate from those that are not AI-generated. These technological advancements present significant challenges to parties, their counsel, and the courts in determining whether evidence is authentic or fake. Moreover, the explosive proliferation and use of GenAI applications raises concerns about whether litigation costs will dramatically increase as parties are forced to hire forensic experts to address AI-generated evidence, the ability of juries to discern authentic from fake evidence, and whether GenAI will overwhelm the courts with AI-generated lawsuits, whether vexatious or otherwise. GenAI systems have the potential to challenge existing substantive intellectual property (“IP”) law by producing content that is machine, not human, generated, but that also relies on human-generated content in potentially infringing ways. Finally, GenAI threatens to alter the way in which lawyers litigate and judges decide cases. This article discusses these issues, and offers a comprehensive, yet understandable, explanation of what GenAI is and how it functions. It explores evidentiary issues that must be addressed by the bench and bar to determine whether actual or asserted (i.e., deepfake) GenAI output should be admitted as evidence in civil and criminal trials. Importantly, it offers practical, step-by-step recommendations for courts and attorneys to follow in meeting the evidentiary challenges posed by GenAI. Finally, it highlights additional impacts that GenAI evidence may have on the development of substantive IP law, and its potential impact on what the future may hold for litigating cases in a GenAI world

Artificial Intelligence as Evidence in Criminal Trial

This paper touches upon the intertwining of AI technology and criminal justice systems and assesses especially the issue of using AI as an evidence-generating mechanism in criminal trials. The paper revolves, in particular, around three focal points. Firstly, it sets the context for the following analysis and gives a short definition of AI. Secondly, it examines some thorny parameters of the evidentiary proceedings and focuses on the most important AI weaknesses that could jeopardise the smooth incorporation of AI in the criminal justice systems. Thirdly, it presents the ways in which AI could affect basic procedural rights of the defendant and concludes with some safety requirements and suggestions that could facilitate the transition to an AI-criminal-justice-era

Critically Envisioning Biometric Artificial Intelligence in Law Enforcement

This report presents an overview of the Critically Exploring Biometric AI Futures project led by the University of Edinburgh in partnership with the University of Stirling. This short 3-month project explored the use of new Biometric Artificial Intelligence (AI) in law enforcement, the challenges of fostering trust around deployment and debates surrounding social, ethical and legal concerns

Critically Envisioning Biometric Artificial Intelligence in Law Enforcement

This report presents an overview of the Critically Exploring Biometric AI Futures project led by the University of Edinburgh in partnership with the University of Stirling. This short 3-month project explored the use of new Biometric Artificial Intelligence (AI) in law enforcement, the challenges of fostering trust around deployment and debates surrounding social, ethical and legal concerns

Artificial Intelligence for Rapid Meta-Analysis: Case Study on Ocular Toxicity of Hydroxychloroquine.

BACKGROUND: Rapid access to evidence is crucial in times of an evolving clinical crisis. To that end, we propose a novel approach to answer clinical queries, termed rapid meta-analysis (RMA). Unlike traditional meta-analysis, RMA balances a quick time to production with reasonable data quality assurances, leveraging artificial intelligence (AI) to strike this balance. OBJECTIVE: We aimed to evaluate whether RMA can generate meaningful clinical insights, but crucially, in a much faster processing time than traditional meta-analysis, using a relevant, real-world example. METHODS: The development of our RMA approach was motivated by a currently relevant clinical question: is ocular toxicity and vision compromise a side effect of hydroxychloroquine therapy? At the time of designing this study, hydroxychloroquine was a leading candidate in the treatment of coronavirus disease (COVID-19). We then leveraged AI to pull and screen articles, automatically extract their results, review the studies, and analyze the data with standard statistical methods. RESULTS: By combining AI with human analysis in our RMA, we generated a meaningful, clinical result in less than 30 minutes. The RMA identified 11 studies considering ocular toxicity as a side effect of hydroxychloroquine and estimated the incidence to be 3.4% (95% CI 1.11%-9.96%). The heterogeneity across individual study findings was high, which should be taken into account in interpretation of the result. CONCLUSIONS: We demonstrate that a novel approach to meta-analysis using AI can generate meaningful clinical insights in a much shorter time period than traditional meta-analysis

A Mixed Bag: Critical Reflections On The Revised Ethical Principles For Judges

In 2021 the Canadian Judicial Council completed a multi-year review and update of Ethical Principles for Judges (EPJ), the ethical and professional guidance for all federally-appointed judges in Canada. The revisions address issues such as case management and settlement conferences, technological competence and the use of social media, interactions with self-represented litigants, professional development for judges, confidentiality, and the return of former judges to the practice of law. In this article, five directors of the Canadian Association for Legal Ethics/Association canadienne pour l’éthique juridique analyze the revised EPJ and offer their observations. The article covers five important topics. On impartiality, it explains the ways in which the revised EPJ represents a significant evolution in the understanding of this important concept. The article then critically examines the absence of any reference to Reconciliation. On judicial involvement with the community, it argues that the revised EPJ may lead judges to disengage from community activities to an unwarranted degree and critiques the scope of new provisions requiring judges to avoid visible signals of support for causes or views. On judicial technological competence, the article endorses new obligations but cautions that these developments will have to be supported by significant resources to provide appropriate training and guidance on best practices. On confidentiality and return to practice, the article welcomes the new provisions while highlighting some additional issues including avenues for enforcement