Ethics, Bias & Trust in AI
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This course contains the use of artificial intelligence.
Duration: 5 Months · 21 Weeks · 105 Teaching Days
Audience: Non-technical Product Owners & Business Leaders
Philosophy: Ethics as a business capability, not a philosophy class
Ethics, 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.
No prior AI or machine learning experience is required
Basic understanding of technology, business, or digital products is helpful but not mandatory
Suitable for product owners, managers, business leaders, designers, analysts, and curious beginners
No coding or programming knowledge is needed for this course
A willingness to think critically about AI systems, fairness, trust, and decision-making
Interest in responsible AI, AI governance, product strategy, or ethical technology leadership
Access to a computer and internet connection for viewing lessons and course materials
An open mindset for discussion, reflection, and real-world case analysis
Understand the core principles of AI ethics, fairness, transparency, and accountability in modern AI systems
Identify different forms of bias in AI, including historical bias, systemic bias, proxy bias, and post-deployment bias
Analyze how AI decisions impact users, businesses, trust, reputation, and society
Evaluate ethical tradeoffs such as accuracy vs fairness, speed vs safety, and personalization vs privacy
Design AI products with stronger trust, transparency, human oversight, and responsible decision-making
Detect and respond to ethical risks during the AI product lifecycle, from problem framing to deployment and monitoring
Build frameworks for AI governance, accountability, incident response, and ethical product leadership
Develop the mindset and judgment needed to become a trustworthy AI Product Owner or AI leader
Product Owners and Product Managers working on AI-powered products and digital platforms
AI Product Leaders who want to build more trustworthy, responsible, and transparent AI systems
Business Leaders and Executives involved in AI strategy, governance, and decision-making
UX Designers and Researchers interested in fairness, trust, explainability, and human-centered AI experiences
Data, Risk, and Governance Professionals looking to understand ethical AI challenges and operational risks
Developers and Technical Professionals who want a stronger understanding of AI ethics beyond coding and models
Startup Founders building AI products and looking to avoid trust, bias, and governance failures early
Students, researchers, and AI enthusiasts who want practical knowledge of ethics, bias, accountability, and responsible AI leadership
