AI and actor-specific decisions

Engineering notes

Key topics: autonomous driving. See the paper for implementation details and experimental results.

Chinese explanation / 中文解读

中文解读待补充：本站会优先为端到端自动驾驶、BEV感知、3D目标检测、轨迹预测、路径规划、LiDAR感知等高价值论文补充中文说明。

Original abstract

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.

Links and sources

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