AI Fairness and Bias Management Training Course

AI Fairness and Bias Management Training Course

Introduction

Artificial Intelligence is transforming decision-making across financial services, healthcare, education, recruitment, public services, retail, technology, and government. However, AI systems can unintentionally reproduce or amplify algorithmic bias, data bias, representation bias, measurement bias, historical bias, and model bias, creating unequal outcomes for different demographic and social groups. AI Fairness and Bias Management Training Course provides a practical and strategic framework for identifying, measuring, mitigating, monitoring, and governing bias throughout the AI lifecycle. Participants explore responsible AI, trustworthy AI, fairness metrics, inclusive datasets, explainable AI (XAI), model governance, algorithmic accountability, ethical AI, and AI risk management while learning how to build AI systems that support equitable and transparent outcomes.

The course connects technical AI practices with organizational governance, regulatory expectations, human rights, data protection, and responsible innovation. Through practical exercises, real-world case studies, bias audits, fairness assessments, model evaluation scenarios, and governance simulations, participants learn how to establish effective AI fairness frameworks and bias management controls. The training emphasizes fairness-by-design, human oversight, continuous monitoring, stakeholder impact assessment, model transparency, documentation, and accountable AI deployment, enabling organizations to identify potential discrimination risks before and after AI systems enter production.

Course Duration

5 days

Course Objectives

By the end of the course, participants will be able to:

1. Understand AI fairness, algorithmic bias, responsible AI, and trustworthy AI principles. 
2. Identify data bias, sampling bias, historical bias, representation bias, and measurement bias. 
3. Conduct AI bias and fairness risk assessments across the AI lifecycle. 
4. Apply quantitative fairness metrics and statistical techniques to evaluate model outcomes. 
5. Detect disparate impact, disparate treatment, and unequal model performance. 
6. Design bias-aware data collection, preparation, labeling, and feature-engineering processes. 
7. Apply fairness-aware machine learning and bias mitigation techniques. 
8. Implement Fairness-by-Design and Responsible AI-by-Design practices. 
9. Use explainability and transparency techniques to investigate potentially biased AI decisions. 
10. Establish AI governance, accountability, human oversight, and escalation mechanisms. 
11. Develop effective AI fairness testing, validation, auditing, and continuous monitoring programs. 
12. Strengthen organizational readiness for AI regulation, ethical risk management, and algorithmic accountability. 
13. Develop practical AI Fairness and Bias Management frameworks, policies, controls, and action plans. 

Target Audience

1. AI and Machine Learning Engineers 
2. Data Scientists and Data Analysts 
3. AI Governance and Responsible AI Professionals 
4. Risk, Compliance, and Internal Audit Teams 
5. Data Protection and Privacy Professionals 
6. Technology, Product, and Innovation Managers 
7. HR, Recruitment, and People Analytics Professionals 
8. Government, Public Sector, and Policy Professionals 

Course Modules

Module 1: Foundations of AI Fairness and Algorithmic Bias

• Principles of AI fairness and responsible AI
• Understanding algorithmic bias and discrimination risks
• Types and sources of AI bias 
• Fairness across the AI lifecycle
• Case Study: Bias risks in an automated recruitment screening system 

Module 2: Data Bias and Dataset Governance

• Identifying data quality and representation issues
• Sampling, selection, labeling, and historical bias 
• Dataset imbalance and underrepresented populations 
• Inclusive data collection and data governance 
• Case Study: Facial recognition performance across demographic groups 

Module 3: Fairness Metrics and AI Model Evaluation

• Demographic parity and statistical parity 
• Equal opportunity and equalized odds 
• False-positive and false-negative rate analysis 
• Model performance across demographic subgroups 
• Case Study: AI-powered credit scoring and disparate outcomes 

Module 4: Bias Detection and Fairness Assessment

• Designing AI bias audits and fairness assessments
• Intersectional and subgroup analysis 
• Bias testing throughout model development 
• Risk-based AI evaluation frameworks 
• Case Study: Healthcare AI producing unequal diagnostic recommendations 

Module 5: Bias Mitigation and Fairness Engineering

• Pre-processing, in-processing, and post-processing approaches 
• Rebalancing and data transformation techniques 
• Fairness-aware model development 
• Trade-offs between fairness, accuracy, and other performance objectives 
• Case Study: Reducing bias in an automated loan approval model 

Module 6: Explainable, Transparent, and Accountable AI

• Explainable AI (XAI) and interpretable models 
• Feature importance and model explanation techniques 
• AI documentation and transparency requirements 
• Human oversight and meaningful review 
• Case Study: Explaining an AI-assisted insurance risk decision 

Module 7: AI Fairness Governance, Compliance, and Monitoring

• Building an AI fairness governance framework
• Roles, responsibilities, accountability, and escalation 
• AI risk registers and algorithmic impact assessments 
• Continuous bias monitoring and model drift detection
• Case Study: Enterprise AI governance for a multinational organization 

Module 8: Fairness-by-Design and Responsible AI Implementation

• Embedding Fairness-by-Design into AI development 
• Developing organizational AI fairness policies 
• AI impact assessments and stakeholder engagement 
• Creating fairness KPIs, controls, and assurance mechanisms 
• Case Study: Developing a Responsible AI framework for a public-sector AI platform 

Training Methodology

• Interactive lectures and presentations.
• Group discussions and brainstorming sessions.
• Hands-on exercises using real-world datasets.
• Role-playing and scenario-based simulations.
• Analysis of case studies to bridge theory and practice.
• Peer-to-peer learning and networking.
• Expert-led Q&A sessions.
• Continuous feedback and personalized guidance.

Register as a group from 3 participants for a Discount

Send us an email: info@datastatresearch.org or call +254724527104 

Certification

Upon successful completion of this training, participants will be issued with a globally- recognized certificate.

Tailor-Made Course

 We also offer tailor-made courses based on your needs.

Key Notes

a. The participant must be conversant with English.

b. Upon completion of training the participant will be issued with an Authorized Training Certificate

c. Course duration is flexible and the contents can be modified to fit any number of days.

d. The course fee includes facilitation training materials, 2 coffee breaks, buffet lunch and A Certificate upon successful completion of Training.

e. One-year post-training support Consultation and Coaching provided after the course.

f. Payment should be done at least a week before commence of the training, to DATASTAT CONSULTANCY LTD account, as indicated in the invoice so as to enable us prepare better for you.