Curriculum

This course provides a comprehensive exploration of artificial intelligence. Students will learn fundamental concepts—such as symbolic learning, machine learning, deep learning, algorithms, explainability, and alignment—through the story of AI, from Leibniz and Lovelace’s ambition to mechanize problem-solving through Turing's work on computability and the Dartmouth Conference. The course culminates in the deep learning revolution of the last decades that led to our contemporary large language models and image generators. Beyond a traditional historical survey, this course highlights the symbiotic relationship between AI and natural science. By the end, students will gain a deep appreciation for AI's transformative potential and the critical role of leadership in shaping its ethical and responsible development.

This course equips students with a broad, conceptual understanding of AI principles and terminology needed to engage effectively in AI-related conversations and decision-making. Topics will include the fundamentals of AI, machine learning, and data-driven decision-making without the need for technical skills or programming. Students will gain confidence in interpreting AI project proposals, performance metrics, and risk assessments. Emphasis will be on the role of AI in transforming organizational strategy, understanding AI-driven workflows, and discerning credible AI metrics and performance claims, enabling students to drive AI initiatives effectively within their organizations.

This course provides a focused exploration of current and emerging AI tools, platforms, and ecosystems, equipping students to identify and evaluate their potential applications. Students will gain a practical understanding of diverse AI technologies. Potential examples include machine learning frameworks, natural language processing systems, computer vision applications, and various LLM wrappers. The course will also examine the evolving landscape of AI hardware and software, with a focus on emerging trends and their potential impact on various industries. By the end of the course, students will be able to critically assess AI technologies and make informed decisions about their adoption and implementation.

This course considers the ethics of AI development and deployment. Possible topics include the environmental footprint of AI, data ethics and privacy, intellectual property and training data, algorithmic bias, and AI as a tool for the common good. Further topics may include the global AI divide, autonomous weapons and the militarization of AI, accountability for AI-related harms, impact on vulnerable populations, AI and the transformation of work and society, artificial consciousness and machine rights, and potential catastrophic risks. The course is a roadmap of ethical issues surrounding artificial intelligence.

This course addresses the fundamental challenge of aligning AI with human values. We will explore classic and contemporary theories of value and determine why it is difficult to convey these values to a machine. The course covers the three main types of alignment failure, corresponding to the three main machine learning training methods: bias amplification (in supervised learning), hallucination (in unsupervised learning), and reward hacking (in reinforcement learning). Students will learn to identify, mitigate, and communicate about AI bias, examining real-world scenarios where the tolerance for bias differs, from medical diagnostics to image generation. The course will explore various fairness metrics, their limitations and trade-offs, and how to apply them effectively. By the end, students will be prepared to lead interdisciplinary teams responsible for assessing, explaining, and mitigating misalignment.

This course empowers future AI leaders to strategically identify and evaluate potential AI applications within their organizations. Students will learn to map business processes, contrast job descriptions with actual workflows, and pinpoint areas where AI can drive strategic advantage and enhance efficiency, productivity, or innovation. Additionally, the course will address strategies for integrating existing, potentially unsanctioned AI solutions into a centralized framework, ensuring compliance while harnessing the creativity and productivity gains of grassroots AI adoption. Throughout the course, we will emphasize aligning AI initiatives with overarching organizational goals and values, regardless of the sector (business, government, non-profit, etc.). As part of the coursework, students will conduct a strategic assessment of AI opportunities within a chosen organization or context, identifying potential use cases, evaluating their feasibility, and aligning them with the organization's strategic vision.

This course is designed to equip future AI leaders with the practical skills and strategic insights needed to successfully integrate and scale AI solutions. The course will explore change management principles and techniques to lead organizations through the AI adoption process, fostering a culture of innovation and collaboration. For the preparation stage, students will learn to assemble and manage high-performing AI project teams, ensure data readiness for AI applications (including the collection, cleaning, and management of internal data), and prepare AI infrastructure and customize appropriate tools. For the implementation stage, students will learn to effectively test, deploy, evaluate, and scale AI solutions. As a capstone project, students will develop a comprehensive roadmap for AI integration at their chosen organizations. The plan should align the steps covered in the course with the chosen organization’s strategic and ethical goals. Students will have the opportunity to integrate this plan with prior work, creating a comprehensive AI Opportunity Assessment & Implementation Roadmap, showcasing their ability to lead strategic AI initiatives from conception to execution.

This course introduces the rapidly changing legal and regulatory environment for AI. We will explore existing law, emerging AI regulation, and best practices to minimize liability. Existing law topics include data and informational privacy, bias and non-discrimination, intellectual property, and product liability. Emerging regulatory systems include the EU AI Act, potential federal regulations in the US, new state-level laws, and the advocacy efforts of various communities lobbying for specific regulations. Entrepreneurs, developers, product managers, legal professionals, and policymakers interested in the intersection of AI and law will have the opportunity to develop their own stance on AI governance.

Clear and strategic communication is essential in the rapidly changing landscape of artificial intelligence. This course equips you to spot misinformation, identify reliable sources, understand cycles of hype and disillusionment, and anticipate AI's impact on marketing, advertising, public relations, and media. You will learn to craft compelling narratives around AI initiatives, addressing potential benefits and ethical considerations in a way that fosters transparency and trust. By the end of the course, you will be able to communicate confidently about AI, whether you are launching a new product, engaging with stakeholders, or simply making informed personal decisions.

This course provides an interactive exploration of AI leadership through articles, case studies, current practices, and emerging trends. By examining a range of AI initiatives across various industries, we will analyze the leadership decisions and strategies employed at each stage of the AI lifecycle: from identifying opportunities and preparing for integration to implementation, scaling, evaluation, communication, and compliance. Case studies may include implementing AI-powered diagnostics in healthcare while addressing ethical concerns and patient privacy; developing AI-driven investment strategies and navigating regulatory complexities in finance; integrating AI tools for personalized learning in education while mitigating bias and ensuring equitable access; deploying AI-powered automation and managing workforce transitions in manufacturing; and utilizing AI for public services and policymaking while upholding transparency and accountability in government. Through in-depth analysis and discussion, students will apply what they have learned so far to gain practical insight into AI leadership.