@article{1438150,
      recid = {1438150},
      author = {Giansiracusa, Noah,},
      title = {How algorithms create and prevent fake news : exploring  the impacts of social media, deepfakes, GPT-3, and more /},
      pages = {1 online resource (xii, 235 pages) :},
      abstract = {From deepfakes to GPT-3, deep learning is now powering a  new assault on our ability to tell what's real and what's  not, bringing a whole new algorithmic side to fake news. On  the other hand, remarkable methods are being developed to  help automate fact-checking and the detection of fake news  and doctored media. Success in the modern business world  requires you to understand these algorithmic currents, and  to recognize the strengths, limits, and impacts of deep  learning---especially when it comes to discerning the truth  and differentiating fact from fiction. This book tells the  stories of this algorithmic battle for the truth and how it  impacts individuals and society at large. In doing so, it  weaves together the human stories and what's at stake here,  a simplified technical background on how these algorithms  work, and an accessible survey of the research literature  exploring these various topics. How Algorithms Create and  Prevent Fake News is an accessible, broad account of the  various ways that data-driven algorithms have been  distorting reality and rendering the truth harder to grasp.  From news aggregators to Google searches to YouTube  recommendations to Facebook news feeds, the way we obtain  information today is filtered through the lens of tech  giant algorithms. The way data is collected, labelled, and  stored has a big impact on the machine learning algorithms  that are trained on it, and this is a main source of  algorithmic bias which gets amplified in harmful data  feedback loops. Dont be afraid: with this book youll see  the remedies and technical solutions that are being applied  to oppose these harmful trends. There is hope.},
      url = {http://library.usi.edu/record/1438150},
      doi = {https://doi.org/10.1007/978-1-4842-7155-1},
}