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On the Path to AI : Law's Prophecies and the Conceptual Foundations of the Machine Learning Age.

Bibliographic Details
Main Author: Grant, Thomas D.
Other Authors: Wischik, Damon J.
Format: eBook
Language:English
Published: Cham : Springer International Publishing AG, 2020.
Edition:1st ed.
Subjects:
Electronic books.
Online Access:Click to View
Table of Contents:
  • Intro
  • Prologue-Starting with Logic
  • Holmes and His Legacy
  • A Note on Terminology: Machine Learning, Artificial Intelligence, and Neural Networks
  • Notes
  • Contents
  • About the Authors
  • Abbreviations
  • 1 Two Revolutions
  • 1.1 An Analogy and Why We're Making It
  • 1.2 What the Analogy Between a Nineteenth Century Jurist and Machine Learning Can Tell Us
  • 1.3 Applications of Machine Learning in Law-And Everywhere Else
  • 1.4 Two Revolutions with a Common Ancestor
  • 2 Getting Past Logic
  • 2.1 Formalism in Law and Algorithms in Computing
  • 2.2 Getting Past Algorithms
  • 2.3 The Persistence of Algorithmic Logic
  • 3 Experience and Data as Input
  • 3.1 Experience Is Input for Law
  • 3.2 Data Is Input for Machine Learning
  • 3.3 The Breadth of Experience and the Limits of Data
  • 4 Finding Patterns as the Path from Input to Output
  • 4.1 Pattern Finding in Law
  • 4.2 So Many Problems Can Be Solved by Pure Curve Fitting
  • 4.3 Noisy Data, Contested Patterns
  • 5 Output as Prophecy
  • 5.1 Prophecies Are What Law Is
  • 5.2 Prediction Is What Machine Learning Output Is
  • 5.3 Limits of the Analogy
  • 5.4 Probabilistic Reasoning and Prediction
  • 6 Explanations of Machine Learning
  • 6.1 Holmes's "Inarticulate Major Premise"
  • 6.2 Machine Learning's Inarticulate Major Premise
  • 6.3 The Two Cultures: Scientific Explanation Versus Machine Learning Prediction
  • 6.4 Why We Still Want Explanations
  • 7 Juries and Other Reliable Predictors
  • 7.1 Problems with Juries, Problems with Machines
  • 7.2 What to Do About the Predictors?
  • 8 Poisonous Datasets, Poisonous Trees
  • 8.1 The Problem of Bad Evidence
  • 8.2 Data Pruning
  • 8.3 Inferential Restraint
  • 8.4 Executional Restraint
  • 8.5 Poisonous Pasts and Future Growth
  • 9 From Holmes to AlphaGo
  • 9.1 Accumulating Experience
  • 9.2 Legal Explanations, Decisions, and Predictions.
  • 9.3 Gödel, Turing, and Holmes
  • 9.4 What Machine Learning Can Learn from Holmes and Turing
  • 10 Conclusion
  • 10.1 Holmes as Futurist
  • 10.2 Where Did Holmes Think Law Was Going, and Might Computer Science Follow?
  • 10.3 Lessons for Lawyers and Other Laypeople
  • Epilogue: Lessons in Two Directions
  • A Data Scientist's View
  • A Lawyer's View
  • Selected Bibliography
  • Index.

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