Fairness and Explainability in AI Systems

CODE: NAI15

DURATION: 3 Days | 5 Days | 10 Days

CERTIFICATIONS: CPD

  • Modern facilities
  • Course materials and certificate
  • Accredited international trainers

3 Days

₦400000

5 Days

₦1000000

10 Days

₦2500000

Course Overview

This course explores the critical concepts of fairness, transparency, and explainability in AI systems. The course includes practical tools and frameworks for measuring fairness and generating model explanations, equipping participants with the skills to build ethical and trustworthy AI applications. Participants will learn how biases are introduced into algorithms, how to detect and reduce them, and how to build models that are interpretable and accountable.

Course Delivery

This course is available in the following formats:

Virtual

Classroom

Request this course in a different delivery format.

Course Outcomes

Delegates will gain the knowledge and skills to:

Know about the sources and impact of bias in AI systems.

Apply fairness metrics to evaluate model behavior.  

Use tools to explain model predictions and decisions.

Design AI systems with transparency and ethical alignment.

Communicate AI decisions clearly to non-technical audiences.

Build trust in AI systems through interpretable design.

Align model development with regulatory and ethical standards.

Key Course Highlights

At the end of this course, you’ll understand:

  • An introduction to fairness in AI: concepts and challenges.
  • Types of bias: data, algorithmic, and societal.
  • Fairness metrics: demographic parity, equal opportunity, etc.
  • Explainable AI (XAI) tools: SHAP, LIME, and interpretable models.
  • Trade-offs between performance, fairness, and interpretability.
  • Regulatory compliance and ethical frameworks.
  • Case studies in healthcare, finance, hiring, and law.
  • Hands-on labs using Python-based fairness and XAI toolkits.
  • Best practices for inclusive and responsible AI design.
  • Communicating AI outcomes and decisions ethically.
Who Should Attend

This course is designed for data scientists, AI/ML engineers, technical leads, compliance officers, product managers, researchers, and policy advisors. All categories of professionals working on AI systems where transparency, accountability, and fairness are required or regulated, such as in finance, healthcare, human resources, or government, will find the course as a useful guide in their operations.

Upcoming Course Dates

Delivery Format: Classroom & Virtual

Date: 25/08/2025

Location: Abuja

Fairness and Explainability in AI Systems

✓ Modern facilities

✓ Course materials and certificate

✓ Accredited international trainers

✓ Training materials and workbook

✓ Access to online resources

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