Name: Ethics, Bias & Trust in AI
Availability: InStock

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

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

Professional Educator

An experienced instructor with expertise in this field and a passion for teaching.

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Certificate of Completion

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