Ethics and Governance of Data Sharing Training Course

Ethics and Governance of Data Sharing Training Course.

Introduction

The global economy is fundamentally reliant on data often termed the new oil making effective and ethical data sharing critical for innovation, public good, and competitive advantage. However, this unprecedented velocity of data necessitates robust frameworks for Data Governance to mitigate mounting risks related to Data Privacy, algorithmic bias, and compliance failures. This course introduces the principles, practices, and policies required for Responsible Data Stewardship in a complex, multi-jurisdictional environment. We will move beyond mere compliance to foster a culture of Data Ethics by Design, enabling professionals to navigate the intricate balance between data utility and individual rights. Cross-Border Data Flows and the rise of Generative AI have amplified the need for Transparency and Accountability in every stage of the Data Lifecycle, from collection to secure disposal, ensuring data sharing is both strategic and ethically sound.

Ethics and Governance of Data Sharing Training Course is specifically designed to equip participants with the practical tools to operationalize ethical governance, transforming regulatory challenges into opportunities for building Digital Trust. Through immersive Case Studies and practical framework development, attendees will master the creation of an effective Data Sharing Agreement, the implementation of strong Data Security measures, and the deployment of Privacy-Enhancing Technologies (PETs). Ultimately, success in the modern data landscape demands that organizations establish a resilient Ethical AI policy, manage Data Sovereignty concerns, and uphold the core principles of Fairness and non-discrimination. This training is your guide to becoming a leader in the next generation of Digital Diplomacy and Data Culture ensuring that data assets are shared legally, securely, and ethically for maximum societal and business value.

Course Duration

5 days

Course Objectives

Upon completion of this course, participants will be able to:

1. Formulate a comprehensive Data Governance Framework aligned with organizational strategy.
2. Analyze the legal landscape of Cross-Border Data Flows, including GDPR and global regulations.
3. Implement protocols for Data Minimization and Purpose Limitation throughout the data lifecycle.
4. Develop and enforce robust Data Sharing Agreements and Interoperability standards.
5. Evaluate and mitigate risks associated with Algorithmic Bias and discrimination in shared datasets.
6. Master the application of Privacy-Enhancing Technologies (PETs) for secure data utility.
7. Establish clear roles for Data Stewardship and Data Ownership across business units.
8. Design a framework for ensuring Accountability and Transparency in data processing.
9. Assess the ethical implications of using shared data for Generative AI and Machine Learning.
10. Operationalize processes for managing Data Subject Rights
11. Identify and respond effectively to emerging threats in Cybersecurity and Data Breaches.
12. Integrate principles of Ethical AI into data collection and sharing pipelines.
13. Cultivate an organizational Data Culture that prioritizes Digital Trust and responsible innovation.

Target Audience

1. Data Governance Officers & Managers
2. Compliance & Legal Professionals (Focus on Data Protection)
3. Data Scientists & Analysts (Data use and bias mitigation)
4. IT & Cybersecurity Directors (Data security and access control)
5. Chief Data Officers (CDOs) & Business Executives (Strategic oversight)
6. Privacy Officers & Specialists
7. Data Architects & Engineers (Data lineage and infrastructure design)
8. Information Risk Managers & Auditors

Course Modules

1. Fundamentals of Data Ethics and Responsible Stewardship

• Data as an asset, Data Utility and Individual Rights.
• Fairness, Transparency, Accountability, and Trust.
• The Data Lifecycle
• Fostering a Data Culture of responsibility.
• Case Study: The Facebook/Cambridge Analytica Scandal 

2. Global Data Governance and Regulatory Compliance

• Introduction to Frameworks
• Major Regulations
• Jurisdictional Challenges
• Regulatory Mechanism
• Case Study: Project Nightingale

3. Operationalizing Data Sharing Agreements and Interoperability

• Components of a comprehensive Data Sharing Agreement
• The role of Metadata in ensuring data understanding and trust.
• Achieving Data Interoperability for seamless and secure exchange.
• Defining Data Owners, Custodians, and Data Stewards.
• Case Study: COVID-19 Contact Tracing Apps

4. Data Privacy, Security, and Risk Mitigation

• Privacy by Design (PbD)
• Encryption, access controls, and Zero Trust Architecture.
• Privacy-Enhancing Technologies.
• Data Breach Protocol.
• Case Study: Tesla Internal Data Leaks

5. Algorithmic Ethics and Mitigation of Bias

• The Problem of Bias
• Quantifying and measuring Algorithmic Fairness.
• Explainable AI (XAI).
• Testing and Validation
• Case Study: Facial Recognition Technology Bias.

6. The Ethics of Data Sharing in the Age of AI

• Data Use for Generative AI
• Synthetic Data
• Intellectual Property (IP)
• Ethical AI Policy
• Case Study: Microsoft Tay/Google Bard Incidents

7. Accountability and Audit Trail

• Data Lineage
• Audit and Monitoring
• Conducting Data Protection Impact Assessments and Privacy Impact Assessments for high-risk sharing.
• Mechanisms for correcting errors and addressing ethical complaints.
• Case Study: GDPR Fines and Enforcement Actions

8. Leadership and Building a Sustainable Data Ethics Program

• Strategies for gaining organizational support for governance initiatives.
• Effective methods for communicating data policies to diverse audiences.
• Creating a continuous learning environment for Responsible Data Stewardship.
• Defining KPIs for ethical data sharing and governance maturity.
• Developing a personalized roadmap for immediate implementation.
• Case Study: Apple's Privacy-First Positioning

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.