@article{1433455,
      recid = {1433455},
      author = {Roberge, Jonathan, and Castelle, Michael,},
      title = {The cultural life of machine learning : an incursion into  critical AI studies /},
      pages = {1 online resource :},
      abstract = {This 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. Jonathan Roberge is an  Associate Professor at the Institut National de la  Recherche Scientifique in Montreal, Canada. He funded the  Nenic Lab as part of the Canada Research Chair in Digital  Culture he has held since 2012. His most recent edited  volume is Algorithmic Cultures (2016). Michael Castelle is  an Assistant Professor at the University of Warwicks Centre  for Interdisciplinary Methodologies, UK and a Turing Fellow  at the Alan Turing Institute, UK. He has a Ph. D. in  Sociology from the University of Chicago and a Sc. B. in  Computer Science from Brown University. Chapter 2 is  available open access under a Creative Commons Attribution  4.0 International License via link.springer.com},
      url = {http://library.usi.edu/record/1433455},
      doi = {https://doi.org/10.1007/978-3-030-56286-1},
}