Evidence, Respect and Truth: Knowledge and Justice in Legal Trials

Evidence, Respect and Truth: Knowledge and Justice in Legal Trials

by Liat Levanon
Evidence, Respect and Truth: Knowledge and Justice in Legal Trials

Evidence, Respect and Truth: Knowledge and Justice in Legal Trials

by Liat Levanon

eBook

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Overview

Can we rely solely on statistics when we judge what is true and just? This book takes a holistic approach to addressing this question. It considers the legal trial as its paradigmatic case study before analysing a wide range of different cases, including profiling, the use of algorithms to predict students' grades, and the authorisation of automated cars.

The book suggests that when we make judgements about the truth or about justice, approximations are not good enough. Truth and justice are uncompromising. They must be so, because the value that underlies them both is respect; and respect takes no compromise. Thus, in the search for truth as in the search for justice, a body of evidence that imposes a statistical compromise will not do. Only evidence that in principle allows reaching the truth and doing justice is good evidence. Once such evidence has been traced, the burden is on us to make good use of the evidence and reach truth and justice. We might or might not succeed, but once we have done our best on evidence that allows success, our judgements are justified; and as such, they can resolve conflicts over the truth and over justice.

Product Details

ISBN-13: 9781509942664
Publisher: Bloomsbury Publishing
Publication date: 11/03/2022
Sold by: Barnes & Noble
Format: eBook
Pages: 272
File size: 682 KB

About the Author

Liat Levanon is Senior Lecturer in Law at King's College London, UK
Liat Levanon is Senior Lecturer in Law at King's College, London, UK.

Table of Contents

Introduction
PART I
THE EPISTEMOLOGY OF LEGAL FACT FINDING
1. The Rationality of Belief and Error Eliminability
I. Epistemology and Proof Paradoxes: A Very Brief Introduction
II. Legal Practice: Logically Ineliminable Errors
III. Rational Legal Belief
IV. Error Eliminability and Truth Tracking
V. Error Eliminability and Eliminative Induction
2. The Challenge from Error and Error Eliminability
I. Sceptical Challenges
II. Error Eliminability and the Argument about Error
III. Error Eliminability and Other Sceptical Arguments
3. Between the Epistemic and the Practical: Pushing against a Persisting Difficulty
I. Does Knowledge Have Practical Value?
II. The Practical Value of Epistemic Reasons: Stability of Belief and Successful Action
III. Pragmatism: The Epistemic Value of Practical Reasons
IV. A Shared Method of Reasoning for the Epistemic and the Practical
V. An Overarching Value: Introducing Respect

PART II
THE PRACTICALITY OF LEGAL FACT FINDING
4. Respecting, Asserting and Error Eliminability
I. Legal Assertions
II. Informing of Wrongdoing
III. Reasons to Inform as Reasons to Assert: Respect for Persons
IV. Respect and the Norm of (Legal) Assertion
V. Conclusions
5. Respecting, Doing Justice and Error Eliminability
I. Justice as Fairness and Justified Belief: The Convergence of Justifications
II. The Context of a Legal Trial
III. Accounting for the Convergence and Taking it Forward: Respect, Evidential Conditions and 'Disaster Prevention'
IV. A More Rigid Account: Epistemic Value as a Source of Moral Value
V. Conclusions
6. Resolution
I. Conflict Resolution Outside and Inside Legal Discourse
II. Error Eliminability and Legal Resolution
III. Some Procedural Implications
7. From Respect to Cost Analysis in Criminal Judgments
I. Evidence of Past Misconduct
II. Statistical Evidence that Indicates Propensity
III. Statistical Evidence that Does Not Indicate Propensity
IV. A Mutual Tragedy: The Error of the Legal System
V. Practical Implications: Aesthetics, Ethics and the Value of Choice
VI. Error Eliminability and Cost Analysis

PART III
RESPONSIBILITY
8. Epistemic and Moral Responsibility
I. Legal Assertions and Epistemic Responsibility
II. Legal Assertions and Practical Responsibility
III. Conclusions

PART IV
BEYOND LEGAL FACT FINDING
9. Applications
I. Artificial Intelligence
II. Algorithmic Sentencing: Predicting how a Human Would Make Retributive Judgements
III. Algorithmic Prediction of Students' Grades
IV. Allocation of Resources in the Private and Public Domains
V. Automated Cars and Other Dangerous Machines
VI. Profiling and Individual Risk Prediction Based on Group Affiliation
VII. Personal Attitudes
VIII. Beliefs about Groups and the Problem of Prejudice
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