Privacy and Ethics in AI-Driven Evaluation Training Course

Privacy and Ethics in AI-Driven Evaluation Training Course

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.

Course Duration

10 days

Course Objectives

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

Target Audience

1. Monitoring & Evaluation (M&E) professionals
2. Data analysts and data scientists
3. AI and machine learning practitioners
4. Ethics and compliance officers
5. Policy makers in technology and innovation sectors
6. Program managers in NGOs and government agencies
7. Academic researchers in AI and evaluation
8. 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@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.