Global Governance of AI and Emerging Technologies Training Course

Global Governance of AI and Emerging Technologies Training Course

Introduction

AI and emerging technologies, from machine learning to quantum computing, are rapidly reshaping our world, presenting unprecedented opportunities and complex challenges. As these innovations permeate every sector, the need for robust, ethical AI governance and responsible technology frameworks has become a critical global priority. This training course is designed to equip professionals with the essential knowledge and practical skills to navigate this complex landscape, ensuring that technological advancements are aligned with societal values, human rights, and sustainable development goals.

Global Governance of AI and Emerging Technologies Training Course goes beyond a theoretical overview, providing a deep dive into the regulatory landscapes, data governance, and policy implications of cutting-edge technologies. Participants will learn how to identify and mitigate risks, such as algorithmic bias and data privacy concerns, while fostering a culture of responsible innovation within their organizations. Through a blend of expert-led sessions and hands-on case studies, you will be empowered to lead the development of trustworthy AI systems and contribute to shaping a safer, more equitable digital future.

Course duration

5 days

Course Objectives

• Establish a foundational understanding of AI and emerging technologies, their societal impact, and their governance challenges.
• Analyze key ethical principles for AI, including fairness, transparency, and accountability.
• Evaluate and apply global AI regulatory frameworks, such as the EU AI Act and national policies.
• Develop strategies for data governance and privacy protection in the age of big data and AI.
• Identify and mitigate risks associated with AI systems, including algorithmic bias and security vulnerabilities.
• Implement AI risk management frameworks and conduct AI impact assessments.
• Formulate organizational policies and internal controls for responsible AI adoption.
• Analyze the geopolitical implications of AI, including international cooperation and competition.
• Explore the role of multi-stakeholder governance involving governments, corporations, and civil society.
• Understand the legal and liability issues surrounding autonomous systems and AI-generated content.
• Discuss the intersection of AI with human rights and its impact on labor, democracy, and misinformation.
• Apply ethical and legal frameworks to real-world case studies in various sectors.
• Build a roadmap for continuous monitoring and improvement of AI governance practices.

Target Audience

• AI Ethicists and AI Policy Analysts.
• Data Governance Managers and Chief Data Officers.
• Risk Managers and Compliance Officers.
• Legal Professionals specializing in technology law.
• Government Officials and Policymakers.
• IT and Technology Executives.
• Corporate Social Responsibility (CSR) and ESG Managers.
• Researchers and Academics in technology and public policy.

Course Outline

Module 1: Foundational Concepts in AI and Emerging Technology Governance

• Understanding the technological landscape: AI, machine learning, deep learning, quantum computing.
• Key ethical principles for AI: fairness, transparency, accountability, and explainability.
• The evolution of AI governance and why it's a global imperative.
• Introduction to major AI governance frameworks
• Case Study: The use of AI in a hiring tool that resulted in algorithmic bias and discriminatory outcomes.

Module 2: Legal and Regulatory Landscape

• Deep dive into global AI regulations: EU AI Act, US regulatory approaches, and China's policies.
• Navigating cross-border data flows and compliance challenges.
• Intellectual property and liability for AI-generated content and autonomous systems.
• The role of voluntary standards and industry self-regulation.
• Case Study: The legal and ethical implications of a self-driving car accident, analyzing liability and regulatory gaps.

Module 3: Data Governance and Privacy

• Core principles of data governance and its intersection with AI.
• Practical application of data protection regulations (GDPR, CCPA).
• Implementing privacy-by-design and ethical data usage frameworks.
• Best practices for data security and managing data breaches in AI systems.
• Case Study: A multinational corporation's challenges in complying with different regional data privacy laws when training a global AI model.

Module 4: Risk Management and Security

• Identifying and assessing risks in the AI lifecycle.
• Developing and implementing an AI risk management framework.
• Cybersecurity governance for AI and emerging technologies.
• Strategies for mitigating and responding to AI-related threats and vulnerabilities.
• Case Study: A financial institution's use of AI for fraud detection and the risks of adversarial attacks that could compromise the system.

Module 5: Responsible AI Development and Deployment

• Practical methods for ensuring algorithmic fairness and mitigating bias.
• Techniques for achieving explainable AI (XAI) and interpretability.
• Developing a responsible innovation culture within an organization.
• Establishing internal ethical review boards and governance structures.
• Case Study: A healthcare startup building a diagnostic AI tool and the process of a Trustworthiness Auditing for AI to ensure fairness and accuracy across diverse patient demographics.

Module 6: Geopolitics and International Collaboration

• The role of AI in global power dynamics and competition.
• International cooperation and the challenges of harmonizing AI governance.
• The use of AI for surveillance, disinformation, and national security.
• Building a framework for international dialogue and collaboration on AI.
• Case Study: The global debate over lethal autonomous weapons systems and the push for a new international treaty.

Module 7: AI and Human Rights

• Assessing the impact of AI on fundamental human rights.
• The future of work: AI, automation, and workforce transformation.
• Addressing misinformation and deepfakes: policy and technical solutions.
• Ensuring an inclusive and human-centric approach to AI development.
• Case Study: A social media company's use of an AI-powered content moderation system and its impact on freedom of expression and censorship.

Module 8: Building a Strategic AI Governance Roadmap

• Creating a long-term strategy for AI governance that is both agile and robust.
• Integrating AI governance into existing corporate governance and ESG frameworks.
• Developing communication strategies to build public trust.
• The future of AI and emerging governance challenges (e.g., Artificial General Intelligence (AGI), quantum computing).
• Case Study: A major tech company's journey in creating and implementing a comprehensive AI governance council and its public-facing principles.

Training Methodology

This course uses a blended learning approach that combines theoretical knowledge with practical application to ensure a deep and lasting understanding. The methodology includes:

• Interactive Lectures and Expert-led Discussions.
• Real-World Case Studies and Scenario Analysis.
• Group Workshops and Collaborative Exercises.
• Practical Frameworks and Toolkits for immediate application.
• Q&A sessions with industry leaders and policymakers.

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.