Accidents in machine learning systems — reframes away from → Long-term / superintelligence AI-risk framing

The paper does not argue that long-term, superintelligence-focused risk is wrong so much as that it is the wrong level of abstraction for getting research done now. Section 1 states plainly that "one need not invoke these extreme scenarios to productively discuss accidents, and in fact doing so can lead to unnecessarily speculative discussions that lack precision" (concrete-problems, §"1 Introduction", p. 2). Section 8 sharpens the contrast institutionally: it credits the Future of Humanity Institute and the Machine Intelligence Research Institute with studying advanced-AI safety "at a more philosophical level" while positioning its own contribution as "the empirical study of practical safety problems in modern machine learning systems" (concrete-problems, §"8 Related Efforts", p. 20). Accidents-in-ml is therefore not a scaled-down subset of long-term risk; it is a deliberate change of method, precision over speculation, aimed at producing experiments rather than scenarios.