000			02770nam a22003617a 4500
003			IIITD
005			20250619020004.0
008			250520b |||||||| |||| 00| 0 eng d
020			_a9783030562885
040			_aIIITD
082			_a303.483 _bROB-C
245			_aThe cultural life of machine learning : _ban incursion into critical AI studies _cedited by Jonathan Roberge and Michael Castelle
260			_aSwitzerland : _bPalgrave Macmillan, _c©2021
300			_axv, 289 p. : _bcol. ill. ; _c21 cm.
500			_aIncludes index
505			_t1. Toward an End-to-End Sociology of 21st-Century Machine Learning
505			_t2. Mechanized Significance and Machine Learning: Why it Became Thinkable and Preferable to Teach Machines to Judge the World
505			_t3. What Kind of Learning Is Machine Learning?
505			_t4. The Other Cambridge Analytics: Early "Artificial Intelligence" in American Political Science
505			_t5. Machinic Encounters: A Relational Approach to the Sociology of AI
505			_t6. AlphaGo's Deep Play: Technological Breakthrough as Social Drama
505			_t7. Adversariality in Machine Learning Systems: On Neural Networks and the Limits of Knowledge
505			_t8. Planetary Intelligence
505			_t9. Critical Perspectives on Governance Mechanisms for AI/ML Systems
520			_aThis book brings together the work of sociologists and historians along with perspectives from media studies, communication studies, cultural studies, and information studies to address the origins, practices, and possible futures of contemporary machine learning. From its foundations in 1950s and 1960s pattern recognition and neural network research to the modern-day social and technological dramas of DeepMinds AlphaGo, predictive political forecasting, and the governmentality of extractive logistics, machine learning has become controversial precisely because of its increased embeddedness and agency in our everyday lives. How can we disentangle the history of machine learning from conventional histories of artificial intelligence? How can machinic agents capacity for novelty be theorized? Can reform initiatives for fairness and equity in AI and machine learning be realized, or are they doomed to cooptation and failure? And just what kind of "learning" does machine learning truly represent? Contributors empirically address these questions and more to provide a baseline for future research.
650			_aScience and Technology Studies
650			_aArtificial intelligence -- Social aspects
650			_aCulture -- Study and teaching
650			_aMachine learning -- Social aspects
700			_aRoberge, Jonathan _eeditor
700			_aCastelle, Michael _eeditor
942			_cBK _2ddc _01
999			_c190043 _d190043