Responsible AI and Social Computing

Course Description

Artificial Intelligence (AI) and data science hold the promise of accelerating scientific discovery, revolutionizing healthcare, and driving positive societal change. Yet, AI-enabled systems can expand opportunities for some while constraining the life chances of others. If not designed and deployed responsibly, these technologies risk exacerbating pressing issues of fairness, accountability, and transparency, causing unprecedented harm at both local and global levels. This graduate course adopts a socio-technical life-cycle approach to Responsible AI, examining the production, deployment, and governance of intelligent systems. Drawing on interdisciplinary traditions, including Human-Centered AI, Data Science, Human-Computer Interaction (HCI), and Science and Technology Studies (STS), we will engage with scholarship on safe, responsible, and trustworthy AI. Core topics include privacy, bias, explainability, trust, human-AI decision-making, accessibility, and the invisible labor powering data-driven systems. Through case studies and examples, the course facilitates critical thinking, debate, and problem-solving focused on harm reduction and governance. 3 credits Grading Letter graded (A, A-, B+, etc.)

Code

EMP 532

Credits

3

Subject

EMP

Programs That Include This Course

• Master's Master of Science in Technological Systems Management