An extensive literature details how businesses and governments use the information they collect. We focus on the relatively recent use of artificial intelligence (machine learning, predictive analytics) to analyze the information. We focus on two features of AI-driven surveillance. Broad-based predictions: What happens in virtually any area of one’s life (what stores you shop in, what you post on social media, and so on) may serve as input to predictions affecting virtually any other area (what you pay for insurance, whether you get hired, what schools you get in to, and so on). Data feedback without error correction: AI-driven decisions affect what happens to people in the future, and that information feeds back into the systems as input for subsequent decisions. Current AI systems often lack mechanisms for effective detection and correction of errors. These two features create fairness problems.