Ensure Ethical AI & Debiasing

This course is part of AI Techniques, Causal Inference & Business Optimization Specialization

Instructor: Hurix Digital

Access provided by InZone - Université de Genève

4 modules

Gain insight into a topic and learn the fundamentals.

Intermediate level

Recommended experience

3 hours to complete

Flexible schedule

Learn at your own pace

4 modules

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.

Skills you'll gain

Details to know

Shareable certificate

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Assessments

6 assignments¹

AI Graded see disclaimer

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

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.

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

1 video Total 5 minutes

Implementing SMOTE Re-sampling for Bias Reduction - Apply SMOTE and compare original vs re-sampled model performance using classification report. 5 minutes

1 reading Total 6 minutes

Podcast: Dataset Representation Bias: Detection and Impact Analysis - Focus on representation bias patterns and their impact. Use freely available datasets. 6 minutes

1 assignment Total 8 minutes

Dataset Bias Analysis and Re-sampling Assessment 8 minutes

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.