Course Includes:
- Price: FREE
- Enrolled: 917 students
- Language: English
- Certificate: Yes
- Difficulty: Beginner
Coupon verified 12:58 PM (updated every 10 min)
Course Folder
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
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