Data Ethics and Responsible AI Innovation
Explore ethical challenges in AI and data innovation. Learn to address critical issues in privacy, fairness, and responsible technology development.
Explore ethical challenges in AI and data innovation. Learn to address critical issues in privacy, fairness, and responsible technology development.
This comprehensive course examines the ethical dimensions of data-driven technologies and AI systems. Through real-world case studies and expert insights, students explore crucial topics including data privacy, algorithmic bias, fairness, and responsible innovation. The course addresses pressing questions about the societal impact of technologies like predictive policing, facial recognition, and smart systems, emphasizing the importance of ethical decision-making in developing and implementing these solutions.
4.4
(16 ratings)
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What you'll learn
Analyze critical social, legal, and ethical issues throughout the data lifecycle
Apply professional judgment to complex moral problems in data science and AI
Evaluate and address ethical challenges in current professional practice
Develop ethically-driven solutions for responsible innovation in AI and data science
Skills you'll gain
This course includes:
PreRecorded video
Graded assignments, exams
Access on Mobile, Tablet, Desktop
Limited Access access
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Module Description
This course explores the critical intersection of data ethics, AI, and responsible innovation. Students examine real-world controversies and ethical challenges in data-driven technologies, including AI systems, machine learning, and big data applications. The curriculum covers key topics such as bias, fairness, digital rights, data privacy, discrimination, transparency, and accountability. Through case studies involving facial recognition, predictive policing, smart cities, and other applications, participants learn to evaluate ethical implications and develop solutions that promote responsible innovation.
Fee Structure
Instructors
1 Course
Distinguished Science and Technology Studies Scholar and Innovation Expert
Robin Williams serves as Professor of Social Research on Technology at the University of Edinburgh, where he established and directs the Institute for the Study of Science, Technology and Innovation (ISSTI). After studying Natural Sciences and Social and Political Science at Cambridge and earning his PhD from Aston University, he has built an extraordinary career spanning over three decades at Edinburgh since 1986. His research focuses on the social shaping of technology, particularly examining how various actors influence the design, implementation, and use of information and communication technologies. As a pioneer in the field of Science and Technology Studies, he has secured over 50 external research awards and developed the influential "Biography of Artefacts" perspective with colleagues to understand how technologies and practices co-evolve. His impact is evidenced through numerous influential publications, including "How Industry Analysts Shape the Digital Future" and "Software and Organizations," with his 1996 paper on social shaping of technology garnering over 2,400 citations. Through ISSTI, he continues to promote interdisciplinary research across areas including ICT, life science innovation, and environmental sustainability, while fostering collaborations between social scientists and specialists from Science, Engineering, and Medicine.
1 Course
Distinguished Data Society Scholar and Digital Democracy Expert
Morgan Currie serves as Lecturer in Data and Society at the University of Edinburgh's Science, Technology and Innovation Studies department, where she examines the intersection of data infrastructure and democratic governance. After earning her PhD in Information Studies from UCLA in 2017 and completing a postdoctoral fellowship at Stanford University's Digital Civil Society Lab, she has built an impressive career investigating open government data, algorithmic welfare systems, and civic data practices. As principal investigator of The Culture & Communities Mapping Project and co-lead of the Digital Social Science Research Cluster at the Centre for Data, Culture & Society, she explores how data infrastructures shape democratic participation and political engagement. Her research spans multiple areas including automated social services, administrative data practices, and urban datafication, with particular focus on how city governments deploy data-driven tools for public services. Through projects examining Universal Credit automation and cultural mapping initiatives, she continues to advance understanding of how data practices influence democratic decision-making while maintaining active engagement with communities through participatory research methods.
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4.4 course rating
16 ratings
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