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

• Author: Grant, Thomas D.
• TeilnehmendeR: Wischik, Damon J.
• Place / Publishing House: Cham : : Springer International Publishing AG,, 2020.; ©2020.
• Year of Publication: 2020
• Edition: 1st ed.
• Language: English
• Physical Description: 1 online resource (163 pages)

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.