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PHILOS 544

ADVANCED ETHICS OF DATA AND ARTIFICIAL INTELLIGENCE

4 credits.

In-depth examination of contemporary moral and political issues in artificial intelligence (AI) and Data Ethics, integrating urgent problems, controversies, and continuously updated case studies. Covers topics such as the use of facial recognition technology in law and immigration enforcement, algorithmically-supported hiring processes, and the complex dynamics of human-computer interaction. Includes critical engagement with cutting-edge research in applied moral/political philosophy, such as on competing theories of justice and political legitimacy as well as on key normative concepts like trust, autonomy, privacy, explainability; and case studies drawing from work in computer sciences and applied statistics.

Prerequisites & course information

Requisites: Junior standing Course Designation: Core GenEd - Social & Behavioral Science Breadth - Humanities or Social Science Level - Advanced L&S Credit - L&S Liberal Arts and Science Repeatable for Credit: No Learning Outcomes: 1. Critically evaluate ethical arguments in relation to data and AI. Audience: Undergraduate 2. Interpret complex texts accurately and analyze them logically by identifying explicit and implicit background assumptions, and by understanding which conclusions necessarily follow from them. Audience: Undergraduate 3. Communicate precisely and concisely in both writing and speech, in a way that adequately captures ethical nuances and ambiguities arising in AI design and deployment while not sacrificing technical accuracy. Audience: Undergraduate 4. Discuss controversial ethical and social questions respectfully and with the aim of uncovering the truth. Audience: Undergraduate 5. Deploy the conceptual tools and language necessary to converse with scholars and practitioners in the interdisciplinary AI and Data Ethics Community. Audience: Undergraduate 6. Identify and evaluate the ethical, legal, and political implications of AI, as well as their historical and societal context. Audience: Undergraduate 7. Communicate the distinctive value of humanistic inquiry into AI and data ethics as compared to, and in addition to, technical efforts to better understand and improve AI and other emerging technologies. Audience: Undergraduate Learning Outcomes: 1. Critically evaluate ethical arguments in relation to data and AI. Audience: Undergraduate 2. Interpret complex texts accurately and analyze them logically by identifying explicit and implicit background assumptions, and by understanding which conclusions necessarily follow from them. Audience: Undergraduate 3. Communicate precisely and concisely in both writing and speech, in a way that adequately captures ethical nuances and ambiguities arising in AI design and deployment while not sacrificing technical accuracy. Audience: Undergraduate 4. Discuss controversial ethical and social questions respectfully and with the aim of uncovering the truth. Audience: Undergraduate 5. Deploy the conceptual tools and language necessary to converse with scholars and practitioners in the interdisciplinary AI and Data Ethics Community. Audience: Undergraduate 6. Identify and evaluate the ethical, legal, and political implications of AI, as well as their historical and societal context. Audience: Undergraduate 7. Communicate the distinctive value of humanistic inquiry into AI and data ethics as compared to, and in addition to, technical efforts to better understand and improve AI and other emerging technologies. Audience: Undergraduate

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