A thorough understanding of concepts in responsible AI—such as bias, drift, and data constraints—can help us use AI more ethically and with greater accountability. This article explores how we can use AI tools responsibly and understand the implications of unfair or inaccurate outputs. Recognizing Harms and Biases Engaging with AI responsibly requires knowledge of its inherent biases. Data biases occur when systemic errors or prejudices lead to unfair or inaccurate information, resulting in biased outputs. These biases can cause various types of harm to people and society, including: Allocative Harm This occurs when an AI system’s use or behavior withholds opportunities, resources, or information in domains that affect a person’s well-being. Example: If a job recruitment AI tool screens out candidates from certain zip codes due to historical crime data, qualified applicants from those areas might be unfairly denied job opportunities. Quality-of-Service Harm This happens when AI tool...