Expected Value Analysis in AI Product Management

Decision-making for AI product teams is challenging due to the probabilistic nature of algorithms, data quality issues, and unrealistic expectations. Expected value analysis offers a framework to guide these decisions by quantifying potential outcomes under uncertainty. The concept is explained using simple examples like dice games and roulette before being formally defined as the sum of outcome values weighted by their probabilities. A case study on an e-commerce fraud detection system demonstrates how to calculate the net monetary impact of an AI model by considering the costs and benefits of true positives, false positives, true negatives, and false negatives.