Ethics, Bias & Trust in AI

This course contains the use of artificial intelligence.Duration: 5 Months · 21 Weeks · 105 Teaching DaysAudience: Non-technical Product Owners Business LeadersPhilosophy: Ethics as a business capability, not a philosophy classEthics, Bias Trust in AI (Foundations) is a comprehensive 5-month course for product owners, AI product managers, and business leaders who want to build AI systems people can trust. Across 21 weeks and 105 teaching days, learners explore how poor AI decisions create business risk, reputational damage, legal exposure, user distrust, and long-term ethical debt.This course goes beyond theory and focuses on practical AI ethics, bias detection, trust design, responsible product decisions, and AI governance readiness. Students learn what “bad AI” really means in business, why AI failures are often invisible, and how ethical mistakes can scale quickly when systems are automated.The course covers the foundations of AI ethics, including the difference between ethics and compliance, fairness, accountability, transparency, human autonomy, consent, and risk prevention. Learners examine where bias comes from, including historical bias, systemic bias, proxy variables, biased data collection, labeling issues, model objectives, and post-deployment drift.A major focus is helping product leaders understand how users experience AI. Students explore customer trust, perceived fairness, automation bias, over-trust, under-trust, emotional reactions to AI decisions, and how transparency can either build or damage confidence.The course also teaches how ethics lives inside real product work: problem definition, MVP design, launch decisions, staged rollouts, support readiness, incident response, monitoring, and decision logs. Learners will understand how to balance growth vs responsibility, accuracy vs fairness, automation vs human judgment, personalization vs privacy, and speed vs safety.By the end, students will be able to think like trustworthy AI product leaders. They will know how to identify ethical risks early, ask better product questions, design for accountability, prepare for governance, and build AI products that are safer, fairer, more transparent, and more trusted over time.