TECH 75 — AI and Accountability: How to Evaluate and Use Artificial Intelligence
Quarter: Summer
Instructor(s): Remi Ounadjela
Date(s): Jul 2—Jul 30
Class Recording Available: Yes
Class Meeting Day: Thursdays
Grade Restriction: NGR only; no credit/letter grade
Class Meeting Time: 6:00—7:30 pm (PT)
Tuition: $355
Refund Deadline: Jul 4
Unit(s): 0
Enrollment Limit: 45
Status: Registration opens May 18, 8:30 am (PT)
Quarter: Summer
Day: Thursdays
Duration: 5 weeks
Time: 6:00—7:30 pm (PT)
Date(s): Jul 2—Jul 30
Unit(s): 0
Tuition: $355
Refund Deadline: Jul 4
Instructor(s): Remi Ounadjela
Grade Restriction: NGR only; no credit/letter grade
Enrollment Limit: 45
Recording Available: Yes
Status: Registration opens May 18, 8:30 am (PT)
AI tools now shape everyday decisions, from drafting emails and generating code to moderating content and delivering customer support. As these systems become embedded in professional and public life, questions of accountability, trust, and responsible use move to the forefront. This course examines AI through the lens of real-world adoption, governance, and oversight.
Students explore where AI deployments succeed and where they fail, analyzing examples that range from productivity-enhancing copilots to automation efforts undermined by weak accountability. The course then turns to transparency, regulation, and emerging standards, including the European Union’s AI Act and ongoing US policy debates. Case studies from technology, healthcare, and media ground discussions of bias, misinformation, and data governance.
Through guided discussion and applied analysis, students develop practical frameworks for evaluating AI systems and making informed decisions about their use, leaving with a deeper understanding of how AI is reshaping trust in society and the judgment to engage with these systems thoughtfully in professional and civic contexts.
Students explore where AI deployments succeed and where they fail, analyzing examples that range from productivity-enhancing copilots to automation efforts undermined by weak accountability. The course then turns to transparency, regulation, and emerging standards, including the European Union’s AI Act and ongoing US policy debates. Case studies from technology, healthcare, and media ground discussions of bias, misinformation, and data governance.
Through guided discussion and applied analysis, students develop practical frameworks for evaluating AI systems and making informed decisions about their use, leaving with a deeper understanding of how AI is reshaping trust in society and the judgment to engage with these systems thoughtfully in professional and civic contexts.