The integration of artificial intelligence (AI) is transforming monitoring and evaluation, offering unprecedented capabilities for data analysis, predictive modeling, and real-time insights. However, this powerful shift also introduces significant ethical challenges, including issues of bias, data privacy, and accountability. This training course is designed to empower M&E professionals to harness the transformative potential of ethical AI while navigating its complex risks. By establishing clear principles and practical safeguards, participants will learn how to design and implement AI-driven systems that are not only effective but also fair, transparent, and respectful of human rights.

This comprehensive program provides a practical, hands-on roadmap for addressing the ethical dimensions of AI in M&E. Participants will gain skills in everything from auditing data for bias and ensuring algorithmic transparency to establishing robust data governance frameworks and engaging with communities on the use of AI. The course is a strategic investment for organizations seeking to demonstrate their commitment to responsible innovation, ensuring that their work is a force for positive and equitable change.

Duration: 10 days

Target Audience:

• M&E Specialists
• Data Scientists
• Program and Project Managers
• Researchers
• Ethical Review Board Members
• AI Developers
• Humanitarian Aid Workers
• Government Officials
• Civil Society Leaders
• Consultants

Objectives:

• Understand the core ethical principles of AI in M&E.
• Master the process of designing a responsible AI framework.
• Learn how to audit data and algorithms for bias.
• Develop a framework for ensuring data privacy and security.
• Integrate ethical considerations into the M&E project cycle.
• Build a more participatory and inclusive approach to AI.
• Communicate the ethical implications of AI to different audiences.
• Ensure ethical considerations and safety protocols in data collection.
• Foster a culture of continuous learning and adaptive management.
• Apply ethical AI principles to a wide range of sectors.

Course Modules:

Module 1: Introduction to AI in M&E

• The rise of AI and its potential in M&E
• Defining AI, machine learning, and their relevance to M&E
• The business case for AI in impact assessment
• An overview of the AI in M&E workflow
• Key challenges and opportunities

Module 2: Foundational Concepts of AI Ethics

• The "do no harm" principle in an AI context
• Defining fairness, accountability, and transparency
• The importance of human oversight
• An overview of major global ethical frameworks
• The difference between a technical and an ethical problem

Module 3: Data and Algorithmic Bias

• Understanding how bias enters a dataset
• The importance of a representative and diverse dataset
• The role of a "fairness audit"
• The use of a simple tool for bias detection
• Case studies on how bias can lead to harm

Module 4: Transparency and Explainability

• The concept of the "black box" problem
• The importance of a clear and understandable algorithm
• The role of a "user-friendly" explanation
• The use of a simple tool for explainability
• Practical labs on a simple explainable AI (XAI) tool

Module 5: Data Privacy and Security

• The importance of a clear data governance plan
• The role of data anonymization and aggregation
• The use of a simple data security tool
• The importance of a clear and consistent reporting style
• Ensuring compliance with data protection regulations

Module 6: Accountability and Oversight

• The importance of a clear accountability framework
• The role of a "human-in-the-loop" model
• The importance of a clear and consistent reporting style
• The importance of a "responsible AI" team
• The long-term implications for the organization

Module 7: M&E for a Diverse Audience

• The importance of knowing your audience
• The role of a "stakeholder analysis"
• Designing reports and dashboards for non-technical audiences
• The importance of accessibility and inclusivity
• Case studies on communicating with different audiences

Module 8: Ethical M&E Design

• How to integrate AI into an existing M&E system
• The importance of a phased implementation strategy
• The role of AI in the project cycle
• Building a culture of adaptive management
• Case studies on successful integration

Module 9: Mainstreaming Ethical AI

• How to integrate ethical AI into existing systems
• The importance of a phased implementation strategy
• The role of ethical AI in the project cycle
• Building a culture of adaptive management
• Case studies on successful integration

Module 10: The Role of the M&E Professional

• Shifting from a technician to a facilitator
• The skills required for an ethical AI M&E professional
• Managing power dynamics and group conflicts
• The importance of a non-judgmental and empathetic approach
• The ethical responsibilities of the M&E professional

Module 11: M&E for a Diverse AI Sector

• M&E for AI in public health
• M&E for AI in humanitarian response
• M&E for AI in governance and social protection
• M&E for AI in climate change and environment
• M&E for AI in education and livelihoods

Module 12: The Future of M&E and AI

• The role of AI and machine learning in automated analysis
• The potential of blockchain for data integrity
• The use of new data sources (e.g., IoT sensors)
• The rise of complexity-aware M&E
• The long-term implications for the sector

Module 13: CBM and Ethical AI

• The potential of community-based monitoring with AI
• Training community members as monitors
• The importance of a participatory approach
• The role of a feedback mechanism for continuous learning
• The long-term benefits of a community-led system

Module 14: Practical Application and Simulation

• A hands-on simulation of a real-world project
• Participants work in teams to design an ethical AI framework
• Troubleshooting common challenges in data collection
• Analyzing and interpreting a set of data
• Peer review and feedback sessions on framework design

Module 15: Building Your Ethical AI Roadmap

• Creating a customized plan for a new project
• Identifying key stakeholders and securing buy-in
• Developing a phased implementation strategy
• Measuring the ROI and business impact of the investment
• Final Q&A and course wrap-up

Module 16: The Role of a Responsible AI Team

• The importance of a cross-functional team
• The role of a data scientist, a data analyst, and an M&E specialist
• The importance of a clear and consistent reporting style
• The long-term implications for the organization
• The importance of a "responsible AI" framework

Training Approach

This course will be delivered by our skilled trainers who have vast knowledge and experience as expert professionals in the fields. The course is taught in English and through a mix of theory, practical activities, group discussion and case studies. Course manuals and additional training materials will be provided to the participants upon completion of the training.

Tailor-Made Course

This course can also be tailor-made to meet organization requirement. For further inquiries, please contact us on: Email: info@skillsforafrica.org, training@skillsforafrica.org  Tel: +254 702 249 449

Training Venue

The training will be held at our Skills for Africa Training Institute Training Centre. We also offer training for a group at requested location all over the world. The course fee covers the course tuition, training materials, two break refreshments, and buffet lunch.

Visa application, travel expenses, airport transfers, dinners, accommodation, insurance, and other personal expenses are catered by the participant

Certification

Participants will be issued with Skills for Africa Training Institute certificate upon completion of this course.

Airport Pickup and Accommodation

Airport pickup and accommodation is arranged upon request. For booking contact our Training Coordinator through Email: info@skillsforafrica.org, training@skillsforafrica.org  Tel: +254 702 249 449

Terms of Payment: Unless otherwise agreed between the two parties’ payment of the course fee should be done 10 working days before commencement of the training.