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This course focuses on social, economic, legal, and ethical issues that arise from the collection, analysis, and use of large data sets, especially when these processes are automated or embedded within artificial intelligence systems. The course explores the design of ethical algorithms by considering questions like the following: what kinds of biases are ethically problematic and how can they be avoided? what are the effects of automation on jobs and inequality? what are the privacy considerations that arise when collecting and using data? what is the ethical significance of transparency in automation? who owns data sets and who has the right to access information? who is responsible for actions that result from artificial intelligence systems? In thinking about these complex questions, students consider specific case studies of controversial uses of data and algorithms in fields such as medicine, biotechnology, military, advertising, social media, finance, transportation, and criminal justice, among others. In addition to relevant ethical theories, students are introduced to philosophical, legal, and scientific theories that play a central role in debates regarding the ethics of data and artificial intelligence. Readings are drawn from a number of classic and contemporary texts in philosophy, science and technology studies, law, public policy, and the emerging fields of "data ethics" and "robot ethics".

This course examines ethical issues related to the implementation of artificially intelligent systems in medicine and healthcare from a philosophical perspective. Topics include (but are not limited to): algorithmic biases in medical diagnoses and healthcare decisions; the impact of AI on the ability to foster trust and respect patient autonomy; disagreements between clinicians and AI decision-support systems; and the implementation of empathic or empathy-simulating AI in healthcare.

This course examines the impact of artificial intelligence and emerging technologies on economic, social, and political structures. It explores how AI integration across sectors such as healthcare, education, media, and the arts affects labor markets, democratic processes, and human relationships. Students investigate the ethical challenges AI presents to democracy, safety, and equality, weighing these against potential benefits. The class considers what practices and governance structures
can ensure ethical and safe AI deployment, evaluating existing legal and scientific frameworks while discussing the need for new regulatory systems and societal norms. To contextualize these issues, the course draws parallels with historical
technological revolutions. It adopts an interdisciplinary approach, incorporating perspectives from philosophy, sociology, history, law, and political science to provide a comprehensive understanding of the AI revolution. By the end of the course, students gain a solid foundation in the legal, policy, and practical aspects of AI, preparing them to navigate an increasingly AI-driven world.

This course delves into the ethical and social implications of emerging biotechnologies and the integration of AI in healthcare. The course examines the transformative role of cutting-edge biotechnological innovations and AI not only in healthcare but also in shaping human interactions and our understanding of human identity such as race. Key ethical topics explored may include AI applications in healthcare and biomedical research, AI caregivers, AI doctors, AI dolls, digital health, genomics and the implications of big data, deep brain stimulation therapy, neurorights and mental privacy concerns, pursuits of life extension and immortality, induced pluripotent stem cells, animal ethics in biotechnological contexts, and green bioethics. Students are encouraged to actively engage in discussions, debates, and role-playing exercises based on assigned readings and materials. These materials include a range of sources, including bioethics literature, legal documents, biomedical and science literature, films, podcasts, and videos.

This course introduces the student to the techniques of artificial intelligence. Students learn strategies for uninformed and informed (heuristic) search, knowledge representation, problem-solving, and machine learning. Additional topics may include motion planning, probabilistic reasoning, natural language understanding, and philosophical implications.

This course explores the use of computer code as a form of creative practice and artmaking. Students discuss the history, practice, and current trends in computational art through a blend of theoretical and project-based learning. Through weekly examples and projects, students learn core concepts of computer science and apply them to the creation of digital artworks. Creative coding, the practice of writing computer programs for creative purposes, is practiced in many different domains of art and design. These include graphic design, generative art, interaction design, digital fabrication, data visualization, and installation art. To explore these different applications of creative coding, this course is oriented around four core topics: generative design, interaction design, data-driven art, and virtual environments. Prior experience with computer programming is not required.