This open access book explores machine learning and its impact on how we make sense of the world. It does so by bringing together two 'revolutions' in a surprising analogy: the revolution of machine learning, which has placed computing on the path to artificial intelligence, and the revolution in thinking about the law that was spurred by Oliver Wendell Holmes Jr in the last two decades of the 19th century. Holmes reconceived law as prophecy based on experience, prefiguring the buzzwords of the machine learning age-prediction based on datasets. On the path to AI introduces readers to the key concepts of machine learning, discusses the potential applications and limitations of predictions generated by machines using data, and informs current debates amongst scholars, lawyers and policy makers on how it should be used and regulated wisely. Technologists will also find useful lessons learned from the last 120 years of legal grappling with accountability, explainability, and biased data. .
Formatted Contents Note
Prologue: Starting with logic CHAPTER 1: Two Revolutions CHAPTER 2: Getting past logic CHAPTER 3: Experience and data as input CHAPTER 4: Finding patterns as the path from input to output CHAPTER 5: Output as prophecy CHAPTER 6: Explanations of machine learning CHAPTER 7: Juries and other reliable predictors CHAPTER 8: Poisonous datasets, poisonous trees CHAPTER 9: From Holmes to AlphaGo CHAPTER 10:Conclusion EPILOGUE: Lessons in two directions.
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