Learn to identify and mitigate ethical concerns in AI development and usage, focusing on responsible implementation strategies.
Learn to identify and mitigate ethical concerns in AI development and usage, focusing on responsible implementation strategies.
This course provides decision-makers with essential knowledge about ethical considerations in artificial intelligence implementation. Students explore real-world examples of AI ethical challenges and learn practical strategies for responsible AI development and use. The curriculum covers key topics including algorithmic bias, consent issues, social justice in AI, and frameworks for ethical decision-making. Designed for professionals and decision-makers, the course emphasizes practical approaches to avoiding AI-related harm.
Instructors:
English
Deutsch, हिन्दी, Русский, 17 more
What you'll learn
Identify key ethical concerns in AI implementation
Develop strategies for responsible AI development
Understand consent and transparency requirements
Recognize and mitigate algorithmic bias
Implement frameworks for ethical AI decision-making
Skills you'll gain
This course includes:
11 Minutes PreRecorded video
7 assignments
Access on Mobile, Tablet, Desktop
FullTime access
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There is 1 module in this course
This course addresses critical ethical considerations in AI development and implementation. The curriculum explores major ethical challenges in AI usage, including algorithmic bias, consent issues, and social justice considerations. Students learn practical frameworks for responsible AI development and implementation, with emphasis on real-world examples and mitigation strategies. The course provides decision-makers with tools to evaluate and address ethical concerns in AI projects.
Introduction to Avoiding AI Harm
Module 1 · 3 Hours to complete
Fee Structure
Payment options
Financial Aid
Instructor
Pioneering Data Science Education and Computational Biology Innovation
Carrie Wright, PhD, serves as a Senior Staff Scientist at Fred Hutchinson Cancer Center and holds an affiliated faculty position at Johns Hopkins Bloomberg School of Public Health, where she focuses on making data science and computational biology more accessible to diverse audiences. Her expertise spans multiple domains, teaching courses including "AI for Decision Makers," "AI for Efficient Programming," "Avoiding AI Harm," "Best Practices for Ethical Data Handling," "Data Management and Sharing for NIH Proposals," and "Write Smarter with Overleaf and LaTeX." Her distinguished career includes significant contributions as a former Assistant Scientist in Biostatistics at Johns Hopkins and postdoctoral research at the Lieber Institute for Brain Development, where she studied genetic mechanisms in psychiatric disease. As a member of the Open Case Studies team, the Genomic Data Science Community Network, and chair of the ITCR OPEN Group, she demonstrates her commitment to advancing science, medicine, and social justice through accessible data science education. Her innovative work includes co-founding the LIBD rstats club and teaching at various institutions, including the Baltimore Underground Science Space and Johns Hopkins Center for Talented Youth.
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