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

Autor Liat Levanon
en Limba Engleză Paperback – 10 apr 2024
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.
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Specificații

ISBN-13: 9781509942695
ISBN-10: 1509942696
Pagini: 272
Dimensiuni: 156 x 234 x 25 mm
Greutate: 0.31 kg
Editura: Bloomsbury Publishing
Colecția Hart Publishing
Locul publicării:London, United Kingdom

Caracteristici

Traces the implications of the analysis across the field of rational judgement in personal life as well as evidence-based reasoning in political judgments

Notă biografică

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

Cuprins

Introduction PART ITHE EPISTEMOLOGY OF LEGAL FACT FINDING1. 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 Induction2. 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 DifficultyI. 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 IITHE PRACTICALITY OF LEGAL FACT FINDING4. 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 TrialIII. 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 IIIRESPONSIBILITY8. Epistemic and Moral Responsibility I. Legal Assertions and Epistemic Responsibility II. Legal Assertions and Practical Responsibility III. Conclusions PART IVBEYOND LEGAL FACT FINDING9. 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