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Responsible AI – Fundamentals, Policy, and Implementation

94-885

Units: 6

Description

As the world rapidly embraces Artificial Intelligence, the potential for both benefit and harm escalates. This course, "Responsible AI: Principles, Policies, and Practices," navigates the complexities of responsible AI use. Our focus is on providing a detailed and practical understanding of the key risks and harms traditional and generative AI can pose, the principles guiding ethical use of AI, and the intricacies of how these harms manifest themselves in the AI lifecycle. This course places a strong emphasis on bias, fairness, transparency, explainability, safety, security, privacy, and accountability, demystifying these foundational concepts and highlighting their relevance in the end-to-end AI life cycle.

Delve into the regulatory landscape of AI as we dissect policymaking worldwide and scrutinize responsible AI frameworks adopted by leading organizations. You'll gain valuable insight into the emerging standards, certifications, and accreditation programs that are guiding the responsible use of AI, Generative AI, and Large Language Models. Building on this knowledge, the course will help you understand the integral role of governance in AI and the pivotal role that various stakeholders play in this landscape.

Our unique approach combines theory with practical strategy, enabling you to develop a comprehensive operational plan for implementing responsible AI within an organization. The course culminates with the creation of a strategy and handbook tailored to the needs of an organization. Furthermore, we will equip you with the skills to communicate effectively, making a compelling case for implementing a responsible AI program. Several guest lectures from practitioners and policy makers, coupled with synthetic case scenarios will give you a window into how organizations and policy making bodies are advancing the responsible use of AI.

Whether you're a technology enthusiast or policy student, if you possess a basic understanding of data science and artificial intelligence, this course is a golden opportunity to immerse yourself in the riveting world of responsible AI. Join us as we explore, analyze, and operationalize Responsible AI from a vantage point that fuses ethical considerations with technical prowess.

Learning Outcomes

  1. Evaluate and categorize the key risks and harms associated wtih traditional and generative AI.
  2. Critically assess and apply ethical principles and trade-offs in the use of AI technologies.  
  3. Identify and map the stages of the AI system lifecycle, pinpointing where risks and harms are most likely to manifest.
  4. Develop and implement strategies to manage and mitigate issues of bias, fairness, transparency, explainability, safety, security, privacy, and accountability in AI systems.
  5. Evaluate and compare global regulatory and policy frameworks related to AI, Generative AI, and Large Language Models.
  6. Analyze and critique how various companies adopt and operationalizing Responsible AI frameworks.
  7. Evaluate and differentiate between emerging standards, certifications, and accreditation programs in the field of responsible AI.
  8. Create end-to-end and top-down AI governance models for AI, identifying the roles and responsibilities of different stakeholders.
  9. Formulate and document a comprehensive strategy for operationalizing responsible AI within an organization.
  10. Effectively articulate and present the rationale and mechanics of a responsible AI program to both technical and non-technical stakeholders within an organization.

Prerequisites Description

No pre-requisite course.