AI Failure Modes
Diverse scenarios where AI systems do not perform as expected or generate unintended consequences.
AI failure modes refer to the varied situations in which an AI-based system does not perform according to its intended use, produces unexpected results, or causes undesirable impacts. These can range from minor breakdowns, such as a recommendation engine suggesting irrelevant products, to significant problems including biased decision-making or accidents caused by autonomous systems. Understanding AI failure modes is critical, as it helps to improve system robustness, to design better fallback plans, and to alleviate potential negative effects. These failure modes can be a result of multiple factors including faulty data, incorrect model training, bugs in the code, improper handling of edge cases, or unforeseen real-world complexities that the system is not capable of managing.
The concept of AI failure modes is as old as AI research itself, with the first discussions on AI safety and its potential breakdowns beginning in the 1960s. However, the term gained significant popularity over recent years with AI's increased mainstream integration and the subsequent focus on its ethical implications.
Key contributors to the understanding of AI failure modes include safety-focused organizations like OpenAI, DeepMind's Safety Team, and the Future of Life Institute. Prominent individuals, like Elon Musk and Nick Bostrom, have also greatly influenced this field through their work and advocacy for AI safety.