This paper focuses on the failures of AI, and how we can learn from them. “AI Fails” proposes a shift in perspective: we should measure an AI’s success by its impact on human beings, rather than prioritizing its mathematical properties (like accuracy).
Five AI Fails and How We Can Learn from Them
Download Resources
These lessons derive from a more holistic view of automated technologies. Such technologies are more than independent widgets; they are part of a complex ecosystem that interacts with and influences human behavior and decision making.
"AI Fails" proposes a shift in perspective: we should measure the success of an AI system by its impact on human beings, rather than prioritizing its mathematical or economic properties (e.g., accuracy, false alarm rate, or efficiency). Such a shift has the potential to empower the development and deployment of amazing as well as responsible AI.