Anthropic Fellows Program: AI Safety, Economics, and Research Methods Practice Test

Scalable oversight is about how to supervise AI systems when the tasks are so complex that no single human can fully evaluate every decision. It develops processes to turn human judgment into supervision signals that can scale up as the AI’s capabilities grow. This includes aggregating input from many humans, using their preferences or critiques to train reward models that guide the AI, and running iterative cycles where outputs are reviewed, feedback is provided, and the system updates accordingly. The aim is to maintain alignment and safety by distributing evaluation across multiple people or experts and converting their judgments into scalable guidance for the AI. This is distinct from the broader field of AI Safety, which covers many safety goals; red-teaming focuses on stress-testing for vulnerabilities, while Mechanistic Interpretability looks at understanding the model’s internal mechanisms.