Impossibility Results in AI: A Survey

This survey systematically catalogs impossibility results across AI
research, organizing them by domain (deductive, inductive, intractability,
unprovability, unfairness, ethical) and analyzing their implications for
AI safety, alignment, and the design of trustworthy AI systems. We argue
that understanding these formal limits is foundational to setting realistic
expectations and identifying genuinely hard problems in the field.