Introduction

In today’s rapidly evolving technological landscape, AI-driven evaluation is transforming how organizations collect, analyze, and interpret data. While artificial intelligence offers unparalleled efficiency, precision, and predictive insights, it also introduces critical ethical and privacy considerations that cannot be ignored. Privacy and Ethics in AI-Driven Evaluation Training Course equips professionals with the knowledge and skills to navigate the complex intersection of AI, privacy regulations, and ethical evaluation practices, ensuring responsible and transparent use of AI in organizational decision-making. Participants will explore frameworks for ethical AI deployment, understand privacy compliance requirements, and learn strategies to mitigate bias, protect sensitive data, and foster accountability in AI-powered evaluation processes.

As AI systems become increasingly embedded in monitoring and evaluation (M&E), safeguarding ethical standards and privacy rights has become paramount. This course emphasizes real-world applications, case studies, and scenario-based learning to prepare participants to identify risks, apply ethical principles, and implement privacy-preserving AI evaluation methods. By combining theoretical insights with hands-on exercises, participants will gain actionable competencies that enhance credibility, ensure compliance with global data protection regulations, and promote equitable outcomes in AI-driven evaluations.

Programme Curriculum

Privacy and Ethics in AI-Driven Evaluation Training Course

Introduction

Course Duration

10 days

Course Objectives

Understand the fundamentals of AI and its applications in evaluation.
Examine global data privacy regulations, including GDPR, CCPA, and Kenya Data Protection Act.
Identify ethical challenges in AI-driven evaluation processes.
Analyze potential biases in AI algorithms and models.
Learn techniques for privacy-preserving data collection and analysis.
Develop frameworks for responsible AI governance in M&E.
Apply AI ethics principles in designing evaluation projects.
Evaluate transparency, accountability, and fairness in AI systems.
Implement data minimization and anonymization strategies.
Integrate stakeholder perspectives in AI ethics decision-making.
Assess risks associated with automated decision-making.
Design monitoring tools to track AI ethical compliance.
Apply case studies to reinforce practical understanding of AI privacy and ethics.

Target Audience

Monitoring & Evaluation (M&E) professionals
Data analysts and data scientists
AI and machine learning practitioners
Ethics and compliance officers
Policy makers in technology and innovation sectors
Program managers in NGOs and government agencies
Academic researchers in AI and evaluation
Technology consultants advising on AI implementation

Course Modules

Module 1: Introduction to AI in Evaluation

Overview of AI technologies in M&E
Role of AI in data-driven decision-making
Emerging trends in AI-powered evaluation
Benefits and limitations of AI applications
Case study: AI for program performance assessment

Module 2: Ethics in AI: Principles and Frameworks

Defining AI ethics in evaluation contexts
Core ethical principles: fairness, accountability, transparency
Ethical decision-making frameworks
Alignment with organizational values
Case study: Ethical dilemmas in AI evaluation

Module 3: Global Data Privacy Regulations

GDPR, CCPA, and Kenya Data Protection Act overview
Rights of data subjects and consent management
Data handling and cross-border regulations
Compliance strategies for organizations
Case study: Privacy breaches in AI data collection

Module 4: Bias and Fairness in AI Algorithms

Understanding algorithmic bias
Sources of bias in training data
Detection and mitigation techniques
Fairness evaluation metrics
Case study: Reducing bias in predictive analytics

Module 5: Privacy-Preserving Data Collection

Data minimization strategies
Anonymization and pseudonymization
Secure data storage and transmission
Role of encryption in protecting privacy
Case study: Privacy-safe survey collection

Module 6: Responsible AI Governance

Governance structures for AI projects
Policy and procedure development
Roles and responsibilities in AI oversight
Accountability frameworks
Case study: Establishing AI ethics committees

Module 7: AI Ethics in Evaluation Design

Designing evaluation projects with ethics in mind
Incorporating stakeholder values
Balancing efficiency and fairness
Scenario planning for ethical risk
Case study: Ethical project design for health programs

Module 8: Transparency in AI Systems

Importance of transparency for trust
Explainable AI concepts
Communicating AI decisions to stakeholders
Transparency reporting practices
Case study: Transparent AI reporting in NGOs

Module 9: Risk Assessment in AI Evaluation

Identifying ethical and privacy risks
Risk scoring and prioritization
Mitigation planning
Continuous monitoring strategies
Case study: AI risk assessment in government programs

Module 10: Automated Decision-Making Impacts

Understanding the consequences of automation
Legal and ethical implications
Human-in-the-loop approaches
Auditing automated decisions
Case study: Automated decision systems in social services

Module 11: Stakeholder Engagement and Ethical Input

Incorporating feedback from affected communities
Participatory approaches in AI evaluation
Addressing stakeholder concerns ethically
Transparent communication strategies
Case study: Community consultation in AI program evaluation

Module 12: Data Minimization and Anonymization Techniques

Principles of data minimization
Practical anonymization methods
Tools for de-identifying datasets
Reducing re-identification risks
Case study: Anonymization in education program datasets

Module 13: Monitoring AI Ethical Compliance

Establishing compliance indicators
Continuous monitoring mechanisms
Auditing AI processes for ethics adherence
Reporting non-compliance issues
Case study: Ethics monitoring dashboard in healthcare AI

Module 14: Practical AI Ethics Tools and Frameworks

Checklists for ethical AI evaluation
Ethical impact assessment frameworks
Toolkits for bias detection
Integrating AI ethics in project cycles
Case study: Implementing ethical AI tools in corporate evaluations

Module 15: Capstone Case Study and Integration

Comprehensive AI evaluation case study
Ethical risk assessment simulation
Privacy-preserving data handling exercise
Case study: Stakeholder consultation role-play
Presentation of findings with ethical recommendations

Training Methodology

This course employs a participatory and hands-on approach to ensure practical learning, including:

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@fineskilltrainingcenter.org or call +254769199797

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 Fineskill Training Center account, as indicated in the invoice so as to enable us prepare better for you.

Skills & Software Covered

AI ethics Data privacy Bias mitigation Responsible AI use

Programme Fee

Duration: 10 Days

Method: Online or Physical

Online Price: $2,000

Physical Price: $3,000

Certification: Accredited Certificate

View Available Dates 📄 Download Course Outline (PDF)

Available Sessions

Location

Month

Year

Oct 19 2026

19 Oct — 30 Oct 2026

💻 Online / Virtual session

Online $2,000