In the race for progress, we must tread carefully, for haste in engineering and data science can shatter more than just code.
Imagine a world where Artificial Intelligence (AI) powered systems could do no wrong, where they flawlessly executed their tasks without a glitch. Sounds like a sci-fi dream, doesn’t it? Welcome to the real world of AI, where things don’t always go as planned. An integral part of responsible AI practice involves preventing and addressing what we term ‘AI incidents.’ This article discusses cultural competencies that can prevent and mitigate AI incidents, focusing on the concept of promoting responsible AI practices. Subsequently, we will explore related business processes in future articles to provide a comprehensive perspective on this crucial topic.
A Note on the Series
As we embark on this series, it’s important to provide context. I am one of the co-authors of ‘Machine Learning for High-Risk Applications,’ along with Patrick Hall 和 James Curtis. This series is designed to offer a concise, reader-friendly companion to the book’s extensive content. In each article, we aim to distill the critical insights, concepts, and practical strategies presented in the book into easily digestible portions, making this knowledge accessible to a broader audience.
Addressing AI incidents is crucial before delving into ML safety because we can’t effectively mitigate what we don’t comprehend. AI incidents encompass any outcomes stemming from AI systems that could potentially cause harm. The severity of these incidents naturally varies depending on the extent of damage they result in. These incidents could range from relatively minor inconveniences, such as mall security robots tumbling downstairs, to more catastrophic events, like self-driving cars causing pedestrian fatalities 和 large-scale diversion of healthcare resources away from those in dire need.
AI incidents encompass any outcomes stemming…