The risk with that premise is that it treats AI as an existential mystery rather than an engineering discipline. A system’s failure modes—such as reward hacking, data drift, or misaligned objective functions—are concrete engineering and statistical challenges, not philosophical dilemmas about the human condition.
While human values dictate deployment and intent, conflating the math of optimization with human self-awareness distracts from the tangible operational mechanics we actually need to govern: objective measurement, deterministic constraints, and verifiable pipelines.
I’m curious whether the session will treat the algorithm as a metaphor for human psychology, or examine how rigid computational constraints differ fundamentally from human judgment under uncertainty.