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References: Unicorn Spotting — Separating Fact from Fantasy in Tech Claims

  1. Media literacy - Wikipedia - Framework for critically evaluating information sources and claims, the foundational skill this chapter applies to technology press releases and investor pitches.

  2. Hype cycle - Wikipedia - Gartner's model for tracking technology maturity and adoption, providing the classification system this chapter teaches students to apply to real-world claims.

  3. Confirmation bias - Wikipedia - Psychological research on how people selectively interpret information to support existing beliefs, explaining why unicorn claims persist despite contrary evidence.

  4. Calling Bullshit: The Art of Skepticism in a Data-Driven World - Carl T. Bergstrom and Jevin D. West - Random House - University of Washington professors' guide to identifying misleading claims, the intellectual ancestor of this chapter's unicorn-spotting methodology.

  5. AI Snake Oil: What Artificial Intelligence Can Do, What It Can't, and How to Tell the Difference - Arvind Narayanan and Sayash Kapoor - Princeton University Press - Princeton researchers' systematic debunking of exaggerated AI claims, the closest real-world equivalent to this chapter's field guide.

  6. AI Incident Database - Partnership on AI - Catalog of real-world AI failures and harms, providing documented evidence for evaluating claims against actual performance.

  7. Snopes Fact-Checking Methodology - Snopes - Overview of professional fact-checking processes that students can adapt for evaluating technology claims using the chapter's framework.

  8. Critical Thinking and AI Claims - Brookings Institution - Policy researchers' guide to assessing AI capabilities versus marketing, a sober companion to the chapter's satirical approach.

  9. The Turk: The Life and Times of the Famous Automaton - Public Domain Review - History of the 18th-century chess-playing automaton hoax, proving that fake AI demonstrations predate actual AI by two centuries.

  10. CRAAP Test for Evaluating Sources - California State University, Chico - Library science framework for evaluating information credibility that maps directly to the chapter's unicorn verification checklist.