TY  - BOOK
AU  - Grant, Thomas D.
AU  - Wischik, Damon J.
AB  - 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.
DO  - 10.1007/978-3-030-43582-0
ID  - OAPEN ID: ONIX_20200615_9783030435820_16
ID  - OAPEN ID: http://library.oapen.org/handle/20.500.12657/39543
ID  - OAPEN ID: 46239
KW  - Science and Technology Studies
KW  - Human Geography
KW  - IT Law, Media Law, Intellectual Property
KW  - Artificial Intelligence
KW  - AI
KW  - Machine learning
KW  - artificial intelligence
KW  - 'big data'
KW  - probability theory
KW  - history of ideas
KW  - legal interpretation
KW  - Transhumanism
KW  - Futurism
KW  - Oliver Wendell Holmes Jr
KW  - Wendell Holmes Jr.
KW  - legal philosophy
KW  - machine bias
KW  - algorithmic bias
KW  - Open Access
KW  - Sociology
KW  - Human geography
KW  - Entertainment & media law
KW  - Artificial intelligence
L1  - https://library.oapen.org/bitstream/20.500.12657/39543/1/2020_Book_OnThePathToAI.pdf
LA  - English
LK  - https://directory.doabooks.org/handle/20.500.12854/36340
PB  - Springer Nature
PP  - Singapore
PY  - 2020
TI  - On the path to AI : Law’s prophecies and the conceptual foundations of the machine learning age
ER  - 
