When AI Goes Bad: What Every Tech Leader Should Learn from Failure

AI can accelerate innovation—or amplify mistakes at scale. In this session, Dr. Davis McAlister, a military-trained interrogator turned leadership expert featured on Ticker News, shares real-world examples of AI gone wrong and what developers can do to prevent similar failures. Through stories and case studies, you’ll learn how bias, poor data, and blind trust in automation can derail even the smartest systems—and how transparency, testing, and communication can keep your projects (and reputation) intact.

1. Identify the most common types of AI failures (hallucinations, bias, misinformation, data leaks) and their real-world consequences. 2. Analyze real case studies of AI misuse in business, healthcare, education, and media. 3. Evaluate the leadership and reputational risks associated with overreliance on generative AI tools. 4. Apply basic ethical frameworks and critical thinking protocols to review AI-generated output before acting on it. 5. Develop a basic set of AI use policies to guide teams toward responsible and transparent use of AI tools in professional settings.