Working Paper: NBER ID: w30177
Authors: Ajay K. Agrawal; Joshua S. Gans; Avi Goldfarb
Abstract: Recent advances in AI represent improvements in prediction. We examine how decision-making and risk management strategies change when prediction improves. The adoption of AI may cause substitution away from risk management activities used when rules are applied (rules require always taking the same action), instead allowing for decision-making (choosing actions based on the predicted state). We provide a formal model evaluating the impact of AI and how risk management, stakes, and inter-related tasks affect AI adoption. The broad conclusion is that AI adoption can be stymied by existing processes designed to address uncertainty. In particular, many processes are designed to enable coordinated decision-making among different actors in an organization. AI can make coordination even more challenging. However, when the cost of changing such processes falls, then the returns from AI adoption increase.
Keywords: AI; prediction; risk management; insurance; protection
JEL Codes: D81; O32
Edges that are evidenced by causal inference methods are in orange, and the rest are in light blue.
| Cause | Effect |
|---|---|
| Improvements in AI prediction (C45) | Shift from rigid rules to flexible decision-making (D91) |
| Improvements in AI prediction (C45) | Decreased reliance on insurance mechanisms (G52) |
| Decreased reliance on insurance mechanisms (G52) | Reduced need for traditional risk management activities (G22) |
| Cost of changing organizational processes decreases (D23) | Returns from AI adoption increase (O31) |
| Decision stakes are high (D81) | Rules maintain their appeal (Z28) |