What to Evaluate When Everything Works

This article explores the nuanced evaluation of AI systems when they function well, emphasizing the importance of continuity, emphasis, and scope in creating supportive and coherent user experiences. It argues that traditional metrics like accuracy and speed do not fully capture the essence of a good AI experience, which lies in how the system manages these qualities across different moments and tasks.