AI Governance Content

Week 1

Introduction [AI Governance]

During the first week of our fellowship on Introduction to AI and Machine Learning, we will focus on providing a high-level understanding of the technical basics of machine learning, which is the dominant approach to AI. We will explore the basics of neural networks, their training process, and how they perform inference. Additionally, we will delve into the developments in AI capabilities over the past decade, highlighting key advancements such as foundation models and tools like ChatGPT. By the end of the week, participants will be able to describe the significance of algorithms, computing power, and data in AI development, and make initial predictions about future developments in the field. This introductory week aims to lay a solid foundation for further exploration of the risks and governance solutions associated with AI in the subsequent parts of the course.
Speaker: Benjamin Sturgeon

Week 2

Introduction [Data Governance]

The second week focuses on data governance frameworks, policies and initiatives, especially in the African context. We examine proposals for comprehensive data governance and arguments for including Global South voices in AI governance. Case studies will analyze data governance efforts by organizations like the African Union. Participants will gain critical knowledge on equitable data governance as a key enabler of responsible AI.
Speaker: Looking For Speakers

Week 3

AI standards and regulations*

In week three, we explore various standards and regulatory approaches emerging around AI governance. We review distributed regulation models suited for AI oversight and analyze Africa-based regulatory initiatives and coalitions. By analyzing case studies of AI governance regulation worldwide, participants will develop insights on managing risks of AI through policy.
Speaker: Looking For Speakers

Week 4

AI Governance through Data Governance

Here we focus on the linkages between AI governance outcomes and data governance policies. We learn how issues of compute access, digital divides, and representation in data shape AI systems and their impacts. Participants will connect data governance concepts from week 2 to the broader context of operationalizing responsible AI.
Speaker: Leonard Vibbi

Week 5

Explainable AI

The fifth week dives into issues around explainable AI, interpreting model behavior, and evaluation of language models - especially in the context of the Global South. We assess technical literature on making AI explainable and build understanding on intercultural dimensions of explainability.
Speaker: Chinasa T. Okolo

Week 6

Economic and Democratic Impacts

In week six, we explore risks and challenges AI pose in economic and democratic domains while also covering misinformation. Participants analyze research forecasts on transformative AI's economic potential as well as destabilization risks from dynamics like labor automation. We also examine the democratic corrosion of misinformation proliferation through social media and language models.
Speaker: Charlotte Siegmann

Week 7

Law, Labour and Policy

The final week ties together legal, labor and policy perspectives on AI governance, including through case studies on applications in law. We synthesize learnings from all weeks into frameworks for holistically assessing and improving responsibility and equity in AI through a coordinated governance approach including participation of affected groups.
Speaker: Susan Otieno