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.