Responsible AI - Principles and Ethical Considerations
This course is part of Leadership Strategies for AI and Generative AI.
This course cannot be purchased separately - to access the complete learning experience, graded assignments, and earn certificates, you'll need to enroll in the full Leadership Strategies for AI and Generative AI Specialization program. You can audit this specific course for free to explore the content, which includes access to course materials and lectures. This allows you to learn at your own pace without any financial commitment.
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What you'll learn
Understand and apply responsible AI principles in technology development
Identify and mitigate bias in AI algorithms and data systems
Implement transparency and explainability in AI decision-making
Establish accountability and governance frameworks for AI systems
Apply privacy and security measures in AI development
Skills you'll gain
This course includes:
2 Hours PreRecorded video
12 assignments
Access on Mobile, Tablet, Desktop
FullTime access
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There are 5 modules in this course
This comprehensive course explores the fundamental principles and ethical considerations of Responsible AI. Students learn about key concepts including fairness, transparency, accountability, and bias mitigation in AI systems. The curriculum covers practical techniques for identifying and addressing algorithmic bias, ensuring AI explainability, implementing privacy-by-design principles, and establishing effective AI governance frameworks. Through real-world examples and case studies, learners develop skills in building ethical and responsible AI systems that balance accuracy with interpretability.
Introduction to Responsible AI
Module 1 · 3 Hours to complete
Ensuring Fairness and Bias Mitigation
Module 2 · 1 Hours to complete
Transparency and Explainability in AI
Module 3 · 1 Hours to complete
Ensuring Accountability and Governance
Module 4 · 1 Hours to complete
Privacy and Security in AI
Module 5 · 1 Hours to complete
Fee Structure
Individual course purchase is not available - to enroll in this course with a certificate, you need to purchase the complete Professional Certificate Course. For enrollment and detailed fee structure, visit the following: Leadership Strategies for AI and Generative AI
Instructors
Diverse, Industry-Focused AI and Data Science Courses
Fractal Analytics, as a leading Coursera partner, provides a comprehensive suite of online courses and professional certificates in English, covering topics such as Generative AI, data science, human behavior, coding, data storytelling, responsible AI, and quantum computing. Their offerings include foundational and advanced programs like "GenAI for Everyone," "Coding with Generative AI," "Data Frameworks for Generative AI," and "Python for Data Science," catering to learners from various backgrounds and industries. These courses are designed for continuous learning, blending instructor-led, eLearning, and blended formats, and aim to equip professionals and organizations with in-demand skills for the evolving digital economy, while also supporting career advancement through recognized certifications and practical, hands-on projects
Pioneering Responsible AI Leadership and Innovation
Sray Agarwal has established himself as a distinguished leader in Data and Artificial Intelligence, particularly championing Responsible AI (RAI) frameworks across international platforms. His multifaceted role at Fractal Analytics encompasses providing pre-sales support globally, mentoring teams in emerging technologies, and serving as a Coursera instructor for Responsible AI. His credentials include authoring a book on Responsible AI, earning a Microsoft MVP award, and serving as an expert advisor to the United Nations. His educational background combines a PGP in Business Analytics from ISB, a Master's in Economics from Mahatma Gandhi University, and a Global Management Programme from Darden Business School. As a Principal Consultant with over 13 years of industry experience, he has pioneered RAI frameworks for major banks in the UK and US, while regularly contributing to international forums including Davos 2024. His expertise spans credit risk modeling, financial crime detection, and graph-based crime modeling, utilizing technologies like Python, R, Spark, AWS, and Azure. Through his practical, hands-on teaching approach, he focuses on equipping learners with skills for effective digital transformation while ensuring AI projects prioritize human, societal, and environmental values
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