Ensure Ethical AI & Debiasing

Overview

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.

Syllabus

Module 1: Fairness Metrics Application - Foundation

Apply fairness metrics to HR selection models and document observed disparities.

Module 2: Bias Mitigation Evaluation - Core Application

Evaluate mitigation approaches and implement bias reduction strategies with measurable improvements.

Module 3: Dataset Bias Analysis - Integration

This module teaches how to detect representation bias in datasets, apply re-sampling strategies such as SMOTE, and assess their impact on model performance across demographic groups.

Trade-off Communication - Assessment

Learners will evaluate the impact of bias mitigation techniques on AI system performance and fairness, then communicate results clearly to stakeholders for informed decision making.