Data: The Real Source of AI’s Power—and Its Problems

Ethical AI data power: AI data bias, garbage in garbage out, and how data really drives AI fairness and safety Unique deep dive into ethical AI systems, AI training data, representation bias, historical bias in AI, and algorithmic bias Learn how to spot AI discrimination risks and take practical steps to make AI systems safer, fairer, and more trustworthy

What You'll Learn:

• Why data—not algorithms—is the true source of AI’s power, limitations, and risks
• How “garbage in, garbage out” explains both AI breakthroughs and AI failures in the real world
• The difference between representation bias and historical bias—and how each leads to unfair AI outcomes
• Real examples of AI discrimination, from facial recognition errors to predictive policing and hiring tools
• Simple ways non-technical professionals can question AI training data and spot red flags in AI outputs
• How to connect AI fairness and safety to your own work, team, or organization—even if you don’t build models yourself
• A practical 3-step reflection: write down key insights, identify one relevant area in your life, and take one small action this week
• Why continuous monitoring and updating of data is essential for ethical AI systems over time