INF 385T Special Topics in Information Science : Human-AI Interaction
|Unique ID: 27204||Min Kyung Lee|
|3:00 pm - 6:00 pm||Syllabus|
Introduction to combining human and machine intelligence to benefit people and society. Students will learn cutting-edge research on a number of topics related to human-AI interaction, including the psychological and societal impacts of AI, AI biases and fairness, transparency and explainability, mixed-initiative interaction, human-in-the-loop decision-making, embodied and natural language based AI, and design guidelines and methods for AI user experiences. These topics will be explored in the context of real-world applications, including online social media and labor platforms, algorithmic management tools for worker hiring and evaluation, and decision-support tools for public administrative decisions on risk assessment and resource distribution. Students will form interdisciplinary teams and learn through projects how to critically analyze existing AI systems, study their human impact, and design new systems to be
This course is about human-centric theories and methods for envisioning AI systems and will provide no technical insight on machine learning, data-mining, or statistical pattern recognition. Prior experience with programming, AI/machine learning, human-computer interaction or interaction design, or user research is not required but will be helpful. The methodological skills required for projects will be covered in the class.
Synchronous class meetings conducted online.
Some seats reserved for graduate students in the School of Information