AI and actor-specific decisions

Artificial intelligence (AI) is increasingly seen as potentially replacing humans in decision-making and problem-solving across many domains. AI is effective for many well-specified decisions. But we argue that AI cannot deal with what we call “actor-specificity.” Actor-specific decisions and problems are (a) forward-looking, (b) individual and idiosyncratic, (c) reasoning-intensive, and (d) experimental—requiring intervention in the world to facilitate “counter-to-data” reasoning. These four criteria, captured by the “FIRE” acronym, function as exclusion criteria: they identify when decisions should not be delegated to AI. These criteria are unified by an overarching concept we call “actor-specificity”: the recognition that such decisions cannot be separated from the particular agent—their beliefs, goals, and theories. The “actor” in actor-specificity refers to the focal decision maker, highlighting the need for a first-person point of view in decision-making —an approach that cannot be modeled from the third-person, population-level perspective that is the basis of AI. As such, the FIRE criteria offer an epistemic and normative basis for determining which decisions should remain with human actors, distinct from the current and future technical capabilities of AI.