Xiaochang Li

Jan 16, 2024 | issues.org | Xiaochang Li |Kelsey Schoenberg

Current technical approaches to preventing harm from artificial intelligence and machine learning largely focus on bias in training data and careless (even malicious) misuse. To be sure, these are crucial steps, but they are not sufficient solutions. Many risks from AI are not simply due to flawed executions of an otherwise sound strategy: AI’s penchant for enabling bias and misinformation is built into its “data-driven” modeling paradigm.