Avoid Bias Problems Before They Become Business Problems

AI bias in business: how to avoid AI bias before it becomes a business problem A practical, non-technical ethical AI guide with an AI bias checklist, representation bias, allocation bias, and AI risk management tips Learn a simple 3-step review process to reduce bias in machine learning, protect your brand, and support AI compliance

What You'll Learn:

• Use a simple 3-step checklist—Represent, Impact, Sense-check—to quickly review AI use cases for bias before launch.
• Spot the two most common problem patterns in business AI: representation bias and allocation bias, with concrete real-world examples.
• Translate abstract “ethical AI” goals into specific business risks: legal exposure, financial loss, and reputational damage.
• Run a non-technical AI review with cross-functional teams so that product, legal, compliance, and ops can all flag potential harms early.
• Turn research insights into action by documenting your key takeaways from the episode so you actually remember and use them.
• Identify one real area in your business (hiring, marketing, customer support, lending, etc.) where AI bias could show up right now.
• Take one small, low-friction step this week—like adding a bias check to a meeting agenda—to build an AI risk management habit.
• Differentiate between verified facts and unverified claims in AI discussions so your team can make higher-confidence decisions.