• Course Duration
    7-weeks
  • Self-paced
    Online Learning
  • Live online Q&A
    sessions with CMU faculty
  • Certificate from
    CMU-School of Computer Science
  • "Over 81% of businesses use AI, but only 15% report strong governance, emphasizing the critical need for Responsible AI growth"
  • "Responsible AI roles are growing 3.5 times faster than the overall job market, highlighting the increasing demand for AI governance and ethical AI expertise​"
  • Course Fee $2500
  • Cohort Starts Jan 10, 2025

Applications Open

For any assistance in submission of the application form, please contact: +1-412-991-1733

Course at a Glance

This module introduces artificial intelligence and responsible AI, addressing misconceptions, highlighting the importance of stakeholder involvement, and ensuring accountability throughout the development and procurement stages.

You will learn how to:

  • Define the concepts of AI and Responsible AI
  • Debunk common AI myths
  • Outline RAI characteristics
  • Identify stakeholder responsibilities in RAI
  • Consider RAI at each development stage
  • Build accountability in RAI by assigning tasks

This module explores the concept of making AI systems understandable and interpretable, focusing on building trust, ensuring transparency, and evaluating both internal and external systems for clarity and accountability.

You will learn how to:

  • Emphasize explainable and interpretable AI
  • Guide development of explainable systems
  • Evaluate and critique AI systems on explainability and interpretability
  • Develop a testing plan to measure explainability
  • Avoid common misconceptions about explainable AI systems

This module covers the principles of ensuring reliability in AI systems, addressing their resilience, evaluating their performance under different conditions, and identifying critical flaws while proposing effective solutions.

You will learn how to:

  • Define robustness in the context of AI
  • Guide development of robust AI systems
  • Evaluate and critique AI systems on robustness
  • Create organization-level policies for robustness

This module explores privacy concepts, their application in AI/ML systems, and design modifications to ensure compliance with privacy regulations, emphasizing privacy by design and privacy-enhancing technologies.

You will learn how to:

  • Define privacy in the context of AI systems
  • Evaluate AI/ML system designs through a privacy lens
  • Consider Fair Information Practice Principles and regulatory frameworks like GDPR
  • Apply privacy by design methodologies
  • Implement data minimization principles

This module addresses the challenges of bias in AI, examining its origins, impacts, and methodologies to ensure fairness in AI systems, fostering equity and inclusivity for all users.

You will learn how to:

  • Define and explain the importance of fairness and bias in A
  • Discuss different fairness levels and identify potential sources of bias in AI
  • Guide creation of fair AI systems
  • Evaluate and critique AI systems for fairness and bias
  • Discuss policies for fairness training
  • Identify common misconceptions and red flags related to fairness and bias in AI

This module consolidates the previous concepts, offering practical guidance on implementing AI system development principles, with a focus on real-life applications and actionable insights for participants' use cases.

You will learn how to:

  • Apply the concepts of robustness, explainability, privacy, fairness and bias at the same time

This module delves into the world of generative AI, focusing on its capabilities, applications, and ethical challenges, helping participants navigate the fine line between human and machine-generated creativity.

You will learn how to:

  • Define Generative AI and LLMs
  • Explain uses and capabilities of Generative AI
  • Assess risks and ethical concerns associated with the use of LLMs
  • Identify AI-generated text with different techniques
  • Interpret copyright implications of using LLMs
  • Design guidelines for ethical LLM use

Course Outcomes

As AI continues to transform industries, understanding how to design and deploy systems that are ethical, transparent, fair, and inclusive is essential. This course will equip you with the knowledge and tools to ensure your ability to implement AI that benefits society responsibly.

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Who is this course ideal for?

The course is thoughtfully curated for

  • Leaders
  • AI Product Managers
  • Data Scientists
  • AI Developers & Engineers
  • Buyers of AI Products and Services
  • Policy Makers & Regulators
  • Compliance Officers
  • AI Users

*This non-technical course requires no prior knowledge of Artificial Intelligence
or tech expertise

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