Confused about AI and worried about what it means for your future and the future of the world? You’re not alone. AI is everywhere―and few things are surrounded by so much hype, misinformation, and misunderstanding. In AI Snake Oil, computer scientists Arvind Narayanan and Sayash Kapoor cut through the confusion to give you an essential understanding of how AI works and why it often doesn’t, where it might be useful or harmful, and when you should suspect that companies are using AI hype to sell AI snake oil―products that don’t work, and probably never will.
While acknowledging the potential of some AI, such as ChatGPT, AI Snake Oil uncovers rampant misleading claims about the capabilities of AI and describes the serious harms AI is already causing in how it’s being built, marketed, and used in areas such as education, medicine, hiring, banking, insurance, and criminal justice. The book explains the crucial differences between types of AI, why organizations are falling for AI snake oil, why AI can’t fix social media, why AI isn’t an existential risk, and why we should be far more worried about what people will do with AI than about anything AI will do on its own. The book also warns of the dangers of a world where AI continues to be controlled by largely unaccountable big tech companies.
By revealing AI’s limits and real risks, AI Snake Oil will help you make better decisions about whether and how to use AI at work and home.
About Authors
Arvind Narayanan is an American computer scientist and a professor at Princeton University. Narayanan is recognized for his research in the de-anonymization of data.[2][3] He is currently the director of Princeton's Center for Information Technology Policy.
Sayash Kapoor is a PhD candidate at Princeton University's Center for Information Technology Policy, where he studies the impact of AI on society. He previously worked in both commercial and academic AI at Facebook, Columbia University, and the École Polytechnique Fédérale de Lausanne. He is a recipient of the ACM FAccT Best Paper Award and a special commendation at the ACM CSCW conference.