The concept of spillover in AI encompasses a variety of unintended impacts that artificial intelligence systems may have when they interact with real-world environments or influence areas beyond their original scope. This includes how the deployment of AI in one domain might inadvertently affect other sectors, influence social dynamics, or alter economic patterns. For example, AI-driven automation in manufacturing might increase efficiency but could also lead to job displacement in other sectors not directly connected to automation. Ethically, addressing spillover involves careful assessment of AI systems' broader social, economic, and political impacts, necessitating robust multidisciplinary approaches to mitigate negative consequences while enhancing positive outcomes.
Historical Overview: The term 'spillover' has been used in various disciplines like economics and ecology to describe unintended consequences across borders or systems. In the context of AI, it began to gain prominence in the early 21st century as AI applications proliferated and their broader impacts became more evident.
Key Contributors: No specific individuals are universally credited with identifying or theorizing the concept of spillover in AI, as it is a broad and interdisciplinary issue. However, numerous scholars and ethicists contribute to ongoing discussions and research on managing and understanding AI's unintended effects across different domains.