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What You'll Learn

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

Requirements

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

Description

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.

Who this course is for:

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