Responsible AI and Ethics
Navigate AI ethics by tackling bias, ensuring transparency, and implementing responsible practices across diverse applications.
Navigate AI ethics by tackling bias, ensuring transparency, and implementing responsible practices across diverse applications.
This comprehensive course explores the ethical, social, and technical dimensions of artificial intelligence, focusing on developing responsible AI systems. You'll examine the sources and impacts of bias in both human and machine systems, learning effective strategies for risk mitigation. The course covers key ethical frameworks including transparency, fairness, and accountability, while introducing you to the evolving regulatory landscape surrounding AI implementation. Through detailed case studies across industries, you'll analyze real-world AI applications to identify critical success factors and potential pitfalls. Special attention is given to comparing human and machine biases, privacy considerations, and methods for creating explainable AI. By balancing theoretical concepts with practical applications, this course equips you with the knowledge to lead AI projects that are not only innovative but also ethically sound, fair, and sustainable.
Instructors:
English
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What you'll learn
Identify and analyze sources of bias in both human and AI systems
Implement effective strategies to mitigate bias in machine learning algorithms
Apply ethical frameworks for responsible AI development and deployment
Evaluate AI systems for transparency, fairness, and accountability
Navigate privacy considerations and international regulations in AI implementation
Assess real-world AI case studies to identify success factors and potential pitfalls
Skills you'll gain
This course includes:
7 Hours PreRecorded video
9 assignments
Access on Mobile, Tablet, Desktop
FullTime access
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There are 4 modules in this course
This course provides a comprehensive exploration of ethical considerations in artificial intelligence development and implementation. The curriculum begins with an in-depth examination of bias in both human and machine systems, comparing their similarities and differences to enable more balanced risk assessment. Students learn to identify various types of bias including machine learning bias, algorithmic bias, human bias, and measurement bias. The second module focuses on responsible AI frameworks, contrasting risk-based and human baseline approaches while covering essential aspects of privacy, transparency, and explainability in AI systems. International regulations and legal considerations are also addressed. The final section presents real-world case studies across multiple domains including computer vision, healthcare, service automation, and security. These practical examples illustrate successful AI implementations and lessons learned from challenges, providing students with contextual understanding of ethical decision-making in AI development. Throughout the course, students engage with reflective readings and assessments that reinforce the practical application of ethical principles.
Course Introduction
Module 1 · 9 Minutes to complete
Bias (Human and Machine)
Module 2 · 5 Hours to complete
Responsible AI
Module 3 · 5 Hours to complete
Case Studies
Module 4 · 5 Hours to complete
Fee Structure
Instructor
Professor or Instructor in Artificial Intelligence and Statistical Methods
Ian McCulloh is associated with Johns Hopkins University and is involved in courses related to artificial intelligence, probability, and statistical methods. His expertise likely spans AI project management, social media analytics, and foundational concepts in AI. He may also be involved in teaching or research related to neuroscience and social computing. If Ian McCulloh is a specific instructor, more detailed information about his background or specific courses taught would be needed to provide a more accurate description.
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