Summary
Artificial intelligence (AI) systems built on machine learning have inherent limitations due to training and data quality, and may appear surprisingly unintelligent when their behavior does not match what people expect from an intelligent system. Despite these limitations, imperfect AI can still be very helpful and has been widely adopted, raising concerns about being left behind as these technologies advance. This project seeks to support non-AI experts in living and working effectively with imperfect artificial intelligence tools by studying the workarounds people generate when encountering
