Ensure Ethical AI & Debiasing

Bias in AI systems can undermine trust and create serious ethical and legal risks for organizations. This Short Course was created to help data analysis professionals accomplish comprehensive bias detection and mitigation in AI-driven decision systems. By completing this course, you'll be able to apply formal fairness metrics, implement proven mitigation techniques, and confidently communicate ethical trade-offs to stakeholders. By the end of this course, you will be able to: Apply fairness metrics to HR selection models and document disparities Evaluate and implement bias mitigation approaches with measurable improvements Analyze datasets for representation bias and apply re-sampling techniques Evaluate accuracy-fairness trade-offs and communicate findings to stakeholders This course is unique because it combines hands-on technical implementation with strategic stakeholder communication skills. To be successful in this course, you should have a background in Python programming and basic machine learning concepts

• Measure AI fairness using objective metrics like demographic parity
• Apply evidence-based bias mitigation techniques effectively
• Correct data-level bias through resampling for stable models
• Communicate ethical trade-offs transparently to stakeholders

• Basic familiarity with the topic and its common terminology
• Readiness to practice through applied exercises or case-based work