Data Ethics, AI and Responsible Innovation
Explore ethical challenges in data-driven innovation. Learn to navigate social, legal, and moral issues in AI and data science.
Explore ethical challenges in data-driven innovation. Learn to navigate social, legal, and moral issues in AI and data science.
This intermediate-level course examines the ethical, social, and legal implications of data-driven innovation and AI. Led by experts in data science, AI, law, and responsible research, the course uses real-world case studies to explore key issues in data ethics. Topics include bias in AI, fairness in algorithmic decision-making, privacy concerns in smart technologies, and ethical considerations in health data use. Through a story-driven approach, learners engage with complex ethical dilemmas across various domains including crime and justice, smart homes and cities, finance, and healthcare. The course emphasizes critical thinking about the societal impacts of data science and AI, preparing participants to make ethical decisions in the rapidly evolving landscape of data-driven technologies.
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What you'll learn
Understand the ethical, social, and legal implications of data-driven innovation and AI
Analyze real-world case studies of ethical challenges in data science and AI applications
Explore concepts of fairness, bias, and accountability in algorithmic decision-making
Evaluate privacy and security concerns in smart home and city technologies
Examine ethical considerations in the use of health and genetic data
Develop critical thinking skills for addressing ethical dilemmas in data-driven contexts
Skills you'll gain
This course includes:
2 Hours PreRecorded video
5 assignments
Access on Mobile, Tablet, Desktop
FullTime access
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There are 5 modules in this course
This course offers a comprehensive exploration of data ethics, AI, and responsible innovation. Through five modules, learners examine the complex ethical challenges arising from data-driven technologies across various domains. The curriculum begins with foundational concepts in law and ethics, progressing to specific applications in crime and justice, smart homes and cities, finance, and healthcare. Key topics include algorithmic bias, fairness in AI systems, privacy concerns in IoT devices, ethical considerations in medical data use, and the social implications of AI in urban environments. The course employs a mix of expert lectures, case studies, and interactive discussions to engage learners in critical thinking about the societal impacts of data science and AI. By the end of the course, participants will be equipped to navigate the ethical landscape of data-driven innovation and contribute to responsible technology development.
Week 1: Law and Ethics
Module 1 · 2 Hours to complete
Week 2: Crime and Justice
Module 2 · 3 Hours to complete
Week 3: Home and City
Module 3 · 2 Hours to complete
Week 4: Money and Markets
Module 4 · 2 Hours to complete
Week 5: Life and Health
Module 5 · 4 Hours to complete
Fee Structure
Payment options
Financial Aid
Instructors
Expert in Data Ethics and Responsible Innovation at The University of Edinburgh
Dr. Ewa Luger is a prominent academic at The University of Edinburgh, where she focuses on the intersection of data ethics, artificial intelligence (AI), and responsible innovation. She teaches the course Data Ethics, AI and Responsible Innovation, which explores the ethical implications of AI technologies and their impact on society. This course addresses critical issues such as fairness, accountability, and transparency in AI systems, emphasizing the importance of ethical considerations in the development and deployment of data-driven technologies. Dr. Luger's work aims to equip students with the knowledge and tools necessary to navigate the complex landscape of AI ethics and promote responsible innovation in their respective fields.
Expert in Data Ethics and Responsible Innovation at The University of Edinburgh
Professor Michael Rovatsos is an esteemed faculty member at The University of Edinburgh, where he teaches the course Data Ethics, AI and Responsible Innovation. This course delves into the social implications of data-driven technologies, such as medical robots, smart cities, and predictive policing, while addressing critical questions about equity and justice in technological advancements. Participants explore how these innovations can benefit society as a whole and who may be marginalized in the process. The course aims to equip learners with the tools to critically analyze ethical challenges and develop responsible AI systems that promote fairness and enhance human well-being.
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