CONCEPT
The Affect Heuristic
Paul Slovic's formalization of the tendency to judge risks and benefits based on emotional reactions — the mechanism that explains why the AI discourse polarizes into positions of irreconcilable feeling masquerading as analysis.
The affect heuristic is the cognitive shortcut by which people judge the risks and benefits of a prospect based on their emotional reaction to it. When people feel positively about a technology, they judge it as low-risk and high-benefit; when they feel negatively, they judge it as high-risk and low-benefit. The correlation between perceived risk and perceived benefit is typically negative in affective judgment but positive or independent in reality — many technologies are simultaneously high-risk and high-benefit. The affect heuristic produces judgments that are internally coherent (low-risk AND high-benefit, or high-risk AND low-benefit) but factually inaccurate, because the actual distribution of technologies includes high-risk AND high-benefit options that affective judgment cannot represent.
In The You On AI Field Guide
The affect heuristic was formalized by Slovic, Finucane, Peters, and MacGregor in the early 2000s, drawing on earlier risk perception research showing that dread and familiarity were better predictors of risk judgments than statistical harm estimates. The heuristic operates