Book contents
- Equalizing Justice
- Equalizing Justice
- Copyright page
- Dedication
- Epigraph
- Contents
- Figures
- Tables
- In Memoriam
- Contributors
- Foreword
- Preface
- Acknowledgments
- Author’s Note
- Introduction
- Part I Addressing the Crisis in Access to Justice with Legal AI
- Part II Automating Legal Tasks with AI and Other Technologies
- Part III Promising Applications
- Part IV Today’s Access to Justice Systems
- Part V Barriers and Risks
- 14 The UPL Barrier
- 15 AI Assurance
- Part VI Recommendations
- Index
15 - AI Assurance
from Part V - Barriers and Risks
Published online by Cambridge University Press: 29 July 2026
Book contents
- Equalizing Justice
- Equalizing Justice
- Copyright page
- Dedication
- Epigraph
- Contents
- Figures
- Tables
- In Memoriam
- Contributors
- Foreword
- Preface
- Acknowledgments
- Author’s Note
- Introduction
- Part I Addressing the Crisis in Access to Justice with Legal AI
- Part II Automating Legal Tasks with AI and Other Technologies
- Part III Promising Applications
- Part IV Today’s Access to Justice Systems
- Part V Barriers and Risks
- 14 The UPL Barrier
- 15 AI Assurance
- Part VI Recommendations
- Index
Summary
This chapter examines two dimensions of AI assurance for systems designed to assist unrepresented litigants: (1) reliability and (2) privacy and accountability. Reliability encompasses accuracy, calibration, robustness, and bias. Privacy and accountability involve balancing data needs with the protection of sensitive information and ensuring the responsible deployment of AI. These considerations take on heightened importance given unrepresented litigants’ unique vulnerabilities and legal contexts.
Reliability standards must be higher for unrepresented litigants, who cannot verify AI outputs, than for legal professionals, who can exercise oversight. Privacy and accountability challenges arise from AI systems’ need for vast training datasets containing sensitive legal information, creating tensions between the effectiveness of these systems and data protection rights under frameworks such as the General Data Protection Regulation (“GDPR”) and the EU AI Act.
Effective AI assurance requires interdisciplinary collaboration between technologists and legal stakeholders to develop appropriate methodologies for defining, measuring, and verifying both reliability and privacy protections in legal AI systems.
Keywords
Information
- Type
- Chapter
- Information
- Equalizing JusticeHarnessing AI for Litigants Without Lawyers, pp. 299 - 310Publisher: Cambridge University PressPrint publication year: 2026
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