Dowdeswell Data Governance for Justice and Human Rights: Forensics, Flow, and Frontiers

1. Auflage 2026
ISBN: 979-8-89881-225-6
Verlag: De Gruyter
Format: EPUB
Kopierschutz: 0 - No protection

E-Book, Englisch, 270 Seiten

Reihe: Current and Future Developments in Law

Data Governance for Justice and Human Rights: Forensics, Flow and Frontiers examines the historical development and current applications of data in legal decision-making, including AI-driven fact-finding and evidence-based argumentation. It also addresses the governance of legal data, tackling challenges such as AI-generated misinformation, forensic bioinformatics, and cognitive biases in forensic science. Finally, it highlights novel forensic applications, particularly in bioinformatics for human identification.
Key Features:
· Comprehensive coverage of data-driven approaches in law and justice.
· Focus on AI, machine learning, and statistical methods in forensic applications.
· Explores governance, ethics, and strategies for reliable legal data use.
· Case studies and real-world examples linking theory to practice.

Autoren/Hrsg.

Dowdeswell, Tracey Leigh

Fachgebiete

• Rechtswissenschaften Strafrecht Rechtsmedizin, Forensik

Leseproben

Introduction: Current and Future Developments in the use of Data Science in Law

Tracey Leigh Dowdeswell1, *

1 Department of Humanities & Social Sciences, Douglas College, 700 Royal Avenue, New Westminster, BC V3M 5Z5, Canada

Abstract

This chapter introduces the volume as a whole, outlining the current and future directions in the legal use of data, and exploring how data can be utilized in the legal system to promote justice and human rights, as well as enhance the legal decision-making process itself.

Part I examines some of the ways in which Artificial Intelligence (AI) and Machine Learning (ML) technologies are marshalling data to improve legal decision-making. The included chapters examine the ways in which data science has been used to improve legal decision-making, from its inception in the mid-twentieth century to Groningen’s school's more recent work on using AI to model the fact-finding process itself. Data, when governed judiciously, can be used to improve legal decision-making and thus improve access to justice, better justify decisions, reduce biases in decision-making, and evaluate evidence to improve accuracy and reduce miscarriages of justice.

Part II examines new strategies to govern the use of data in the legal system: identifying and addressing cognitive biases among forensic scientists, regulating the forensic use of bioinformatics in the criminal justice system, and eliminating the use of false legal information hallucinated by AI systems.

Part III of this book describes some novel forensic applications of genetic data, particularly in the field of bioinformatics and its advances in human identification. Some of the most significant advances have been made in the field of bioinformatics and its application to human identification. The chapters in this section focus on these developments, examining how advances in SNP sequencing, combined with computational methods for kinship identification, are leading to the clearance of previously unsolvable cases.

Keywords: Artificial Intelligence, AI and law, Argumentation theory, Bioinformatics, Data science, Ethics of AI, Forensic genetics, Forensic genomics, Forensic science, Genetic genealogy, Human identification, Kinship, Legal ethics, Machine learning, Philosophy of evidence.

* Corresponding author Tracey Leigh Dowdeswell: Department of Humanities & Social Sciences, Douglas College, 700 Royal Avenue, New Westminster, BC V3M 5Z5, Canada; E-mail: [e-mail]

INTRODUCTION

This book examines current and future directions in the application of data in the legal system. The authors discuss various ways in which data can be utilized to promote justice and human rights, as well as to enhance the legal decision-making process itself.

Part I examines the ways in which data science has been utilized to enhance legal decision-making, from its inception in the mid-twentieth century to Groningen’s more recent work on using AI to model the fact-finding process itself, as described by McGregor Richmond. Dowdeswell provided one such case study, applying argumentation theory to the evaluation of evidence in a notorious miscarriage of justice. Part II examines new strategies to govern the legal use of data: eliminating the use of false legal information hallucinated by AI systems, regulating the forensic use of bioinformatics in the criminal justice system, and identifying and addressing cognitive biases among forensic scientists. Part III describes several novel applications of the forensic use of data, particularly in the field of bioinformatics and its advances in human identification.

PART I: CURRENT AND FUTURE DIRECTIONS IN USING DATA IN LEGAL DECISION-MAKING

As discussed above, part I of this book examines some of the ways in which Artificial Intelligence (AI) and Machine Learning (ML) technologies are marshalling data to improve the legal system itself. Each of the chapters in this part examines the ways in which data can be used to improve legal decision-making, thereby enhancing access to justice, justifying decisions more effectively, reducing biases in decision-making, and evaluating evidence to enhance accuracy and prevent miscarriages of justice.

Karen McGregor Richmond, in her chapter, “The Integration of Data Science and Evidential Analysis,” reviews the use of data science to understand the analysis of evidence and the processes of juridical proof [1]. Innovations in data science and ML have led to advancements in the modelling of legal reasoning and legal prediction science, where argumentation modelling has become a major research focus [1]. This, in turn, has enabled data science to play a leading role in understanding and modelling the fact-finding process itself [1].

This chapter reviews the work of the Groningen School, which has played an important role in shaping this field. A significant contribution of the Groningen School has been to adopt an integrated, hybrid perspective of the fact-finding process [1]. This has led to the proliferation of research that integrates various strengths of the Wigmorean, Narrative, and Bayesian accounts of fact-finding – the three main normative approaches used in the field [1]. Hybrid approaches provide a better account of the adversarial setting of arguments, both pro and con, the globally coherent perspective offered by scenarios, and the dynamic uncertainty of probabilities than do previous approaches, such as those relying on legal positivism [1]. This also enables explainability in ML systems that model the fact-finding process [1]. Explainability, in turn, is crucial for these systems to be accepted by courts and legal professionals [1].

Tracey Dowdeswell, in her chapter “Argumentation Schemes, AI, and Criminal Law: Evaluating Evidence in a Miscarriage of Justice,” uses argumentation schemes to analyze evidence in a criminal case [2]. She reviews the application of artificially intelligent systems to the assessment of evidence in criminal cases and proposes a system for formalizing argumentation schemes for future computational applications [2]. She uses argumentation schemes to examine the evidence in a notorious miscarriage of justice – that of Robert Earl Hayes [2].

She draws upon the themes raised in McGregor Richmond’s chapter. Her work combines abductive reasoning with narrative or scenario-based reasoning and integrates them within argumentation schemes [2]. She identifies six different scenarios, or narrative explanations, as having been put forward by the parties over the years to explain the evidence in Hayes’ criminal prosecutions [2]. Each scenario is evaluated separately; in the final step, their relative plausibilities are compared using an abductive argumentation scheme [2].

The final result is a plausibility value that indicates how justified we are in believing that a given scenario is the most plausible explanation for the available evidence at the time of evaluation [2]. The plausibilities proposed here are qualitative, relative and are correlated with legal standards of proof [2]. This system assists in identifying and removing prejudices and cognitive biases that impede reliable evaluations of evidence [2]. It also assists in better formalizing and defining argumentation schemes to evaluate evidence in criminal cases, and to prepare the way for future computational applications [2].

Dowdeswell argues that miscarriages of justice are best prevented by using methods that are objective, accurate, reproducible, and sufficiently formalized that any agent, including a computational decision support system, would obtain a similar result [2]. Systematization and formalization are key to accurately evaluating the plausibility of evidence, and they form the basis for further application of ML technologies in this domain of legal decision-making [2].

PART II: CURRENT AND FUTURE DIRECTIONS IN GOVERNING THE LEGAL USE OF DATA

Data poses numerous – and highly complex – challenges throughout the legal system. Sean Goltz and Zhao Yuxin, in their chapter, “AI Hallucinations as an Existential Risk to Human Society: Mitigating the Risks,” discuss the regulation of large language models, such as Gemini and ChatGPT, and the significant risks they pose through “hallucinating” false data, which is particularly harmful in the legal field [3].

They argue that AI hallucinations, if left unchecked, pose a serious risk to our existing laws and social order [3]. The authenticity of documents in general and legal documents in particular, such as statutes, regulations, bills, and caselaw, is seriously jeopardized if Large Language Models (LLMs) produce – and we accept – false legal information [3]. This chapter explores the risks...