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On the path to AI

- Law's prophecies and the conceptual foundations of the machine learning age

Om On the path to AI

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 ΓÇÿrevolutionsΓÇÖ 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 ageΓÇöprediction 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.

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  • Språk:
  • Engelsk
  • ISBN:
  • 9783030435813
  • Bindende:
  • Hardback
  • Sider:
  • 147
  • Utgitt:
  • 3. juni 2020
  • Utgave:
  • 12020
  • Dimensjoner:
  • 148x210x0 mm.
  • Vekt:
  • 454 g.
  • BLACK NOVEMBER
  Gratis frakt
Leveringstid: 2-4 uker
Forventet levering: 18. desember 2024

Beskrivelse av On the path to AI

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 ΓÇÿrevolutionsΓÇÖ 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 ageΓÇöprediction 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.

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