Ensure Ethical AI & Debiasing

About this course

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.

What you will learn

• Measurable AI Fairness: Fairness can be measured using metrics like demographic parity to objectively assess bias across protected groups.
• Evidence-Based Bias Mitigation:Comparing mitigation methods with quantitative metrics ensures bias intervention are chosen by impact,not assumptions.
• Data-Level Bias Correction: Fixing representation issues through resampling builds more stable, fair, and reliable AI models.
• Transparent Ethical Trade-offs: Ethical AI requires clear communication of fairness–performance trade-offs to support informed stakeholder decisions.