Michael Flood

Cross-posted from Substack Feeling intellectually understimulated, I've begun working my way through Max Lamparth's CS120 - Introduction to AI Safety. I'm going to use this Substack as a kind of open journaling practice to record my observations on the ideas presented, both in the lectures and in the readings. The reading for the first week is Dobbe, Gilbert, and Mintz's 2021 paper "Hard Choices in AI Safety." The paper makes the argument that AI Safety and Governance need to be situated in a 'sociotechnical' milieu - that there is a lot to be learned from sociological studies of complex technology and systems, and that this should flesh out and expand the narrow (from the authors' POV) focus on technical solutions that they attribute to much contemporary AI Safety research. Essentially, they advocate for thinking about AI Safety and Governance as political, social issues as opposed to narrowly technical, mathematical ones. The authors do not at any point engage with ideas about Artificial General Intelligence or the idea of creating generally capable AI agents and how transformative this could be. Rather, their examples are couched in terms of autonomous vehicles, content curation systems, and algorithms perpetuating bias and inequalities - call it Everyday AI. Essentially, they treat AI as equivalent to other transformative technologies like electricity or the Internet, rather than something unprecedented. They imagine AI deployment as the selection and adaptation of a system for a given purpose - a police department adopts a predictive algorithm for patrol routes, a school system purchases a personalized learning curriculum - and then think about how to engage with and gather feedback from the communities affected while mitigating power imbalances between developers, users, and other stakeholders affected by the system's use. They're not thinking of ubiquitous, powerful, autonomous AI agents available anytime and anywhere there is an internet connection, pursu