AI Ethics and Policy

University of Colorado Boulder via Coursera

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Provider

Coursera
Pricing

Paid Course
Languages

English
Certificate

Certificate Available
Effort

5 weeks, 4 hours a week
Sessions

Self-Paced
Level

Beginner
Subtitles

English

Found in

Overview

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As artificial intelligence becomes more integrated into society, questions about how it should be governed have become both more important and more challenging. This course examines how societies translate ethical concerns about AI into policies, laws, standards, and other forms of governance. Learners will explore the complex governance ecosystem surrounding AI, from legal frameworks to organizational policies to social norms. This course covers broad approaches to AI governance around the world, comparing major frameworks such as those emerging in the EU and the US, and including topics such as data privacy and intellectual property, as well as emerging efforts to address societal concerns including discrimination, labor, and environmental impacts. Learners will also consider the role of governance beyond law, including how market forces, architecture, and norms can steer the direction of technology. Through analyzing research and policy documents as well as current events, students will learn to identify emerging governance issues, critique proposed solutions, and design policy strategies for responsible AI development. This course can be taken for academic credit as part of CU Boulder’s Masters of Science in Computer Science (MS-CS) and the Master of Science in Artificial Intelligence (MS-AI) degrees offered on the Coursera platform. This fully accredited graduate degree offer targeted courses, short 8-week sessions, and pay-as-you-go tuition. Admission is based on performance in three preliminary courses, not academic history. CU degrees on Coursera are ideal for recent graduates or working professionals. Learn more: MS in Computer Science: https://coursera.org/degrees/ms-computer-science-boulder MS in Artificial Intelligence: https://www.coursera.org/degrees/ms-artificial-intelligence-boulder

Syllabus

Ethics, Policy, and Governance

This module introduces students to the foundational concepts that shape AI governance and policy. Students will examine how ethical concerns about AI are translated into policy recommendations and regulatory responses. The module also explores why societies choose to govern emerging technologies, why AI's characteristics make it especially difficult to define and to govern, and how governance extends beyond formal law to include mechanisms like social norms, market forces, and technical architecture.

Approaches to AI Governance

This module explores major approaches to AI governance and the different ways institutions attempt to shape the development and use of AI systems. Students will map the AI governance ecosystem, identify key actors and policy tools, and compare approaches such as risk-based and rights-based policy frameworks. The module also examines how governance frameworks distribute responsibility across actors, and how non-legislative mechanisms like technical standards can influence AI practice.

Data Ownership, Privacy, and Authenticity

This module examines how AI challenges existing ideas about data ownership, privacy, and authenticity. Students will explore how generative AI complicates copyright law, how AI intensifies longstanding privacy problems through data collection, inference, surveillance, and automated decision-making, and why provenance and consent are difficult to establish in AI training data. The module also considers the rise of deepfakes and synthetic media as challenges for dignity, democracy, and security. Throughout, students will consider what legal, technical, and governance responses might help address data harms.

Governing AI Impacts

This module examines how AI governance responds to the concrete impacts of AI systems on individuals and communities. Students will explore how automated decision-making can produce bias and discrimination, and what makes such decisions legitimate or contestable when they affect people's lives. The module then turns to AI's consequences for labor and the environment, and the governance tools that might address them. Finally, students will consider questions of responsibility and liability: when an AI system causes harm, who is accountable, and how responsibility can be distributed and misplaced. Throughout, students will weigh legal, organizational, and technical responses to these impacts.

Designing AI Governance

This module turns from analyzing AI governance to designing it. Students will examine how organizations translate high-level AI principles into everyday practice, and why responsible AI efforts can fall short when they meet the realities of institutions, incentives, and resources. Building on the rest of the course, the module introduces a layered model for thinking about governance and walks through how to design a governance strategy, including framing the problem, choosing interventions, and stress testing approaches. Throughout, students will consider not only what governance should achieve in principle but how it actually works in practice, as well as the roles they might play in shaping it.