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.
Ensure Ethical AI & Debiasing
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Ensure Ethical AI & Debiasing
This course is part of AI Techniques, Causal Inference & Business Optimization Specialization
Instructor: Hurix Digital
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Gain insight into a topic and learn the fundamentals.
Intermediate level
Recommended experience
3 hours to complete
Flexible schedule
Learn at your own pace
What you'll 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.
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Recently updated!
March 2026
Taught in English
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This course is part of the AI Techniques, Causal Inference & Business Optimization Specialization
When you enroll in this course, you'll also be enrolled in this Specialization.
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There are 4 modules in this course
Apply fairness metrics to HR selection models and document observed disparities.
What's included
1 video1 reading1 assignment1 ungraded lab
Evaluate mitigation approaches and implement bias reduction strategies with measurable improvements.
What's included
2 videos2 assignments
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.
What's included
1 video1 reading1 assignment
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.
What's included
2 videos1 reading2 assignments1 ungraded lab
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