Responsible Artificial Intelligence

In this course, we explore the concept of responsible artificial intelligence, misconceptions surrounding it, and its recent rapid growth and development. We will discuss the benefits and potential applications of responsible AI and the ethical and social concerns surrounding its use. We will also discuss the importance of ensuring that AI is developed and used in a way that is fair, transparent and accountable.

This seven-week, online, self-paced course complements online content with live weekly office hours and live Q&A sessions with learning facilitators.

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Course Instructors

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Alex John London

Clara L. West Professor of Ethics and Philosophy

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Lei Li

Assistant Professor on Language Technologies Institute

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Pradeep Ravikumar

Professor of Machine Learning

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Norman Sadeh

Professor, Software and Societal Systems Development

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Rayid Ghani

Distinguished Career Professor in Machine Learning and Public Policy

 

 

 

Modules: Introduction to Responsible AI, Robustness, Explainability, Privacy, Fairness and Bias, Responsible Generative AI and Large Language Models

In each module, we define a specific concept in the context of responsible AI systems, and discuss its benefits and limitations. We discuss how to account for this factor as part of responsible AI development, important considerations and common misconceptions for each, and recent developments in this space.

Intended Audience and Prerequisites

This course is designed for leaders, product managers, developers, buyers and users of AI products and services.

It does not require prior technical knowledge.

Course Curriculum

Module 1: Introduction to Responsible AI

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 

Module 2: Explainability

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

Module 3: Robustness

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

Module 4: Privacy

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

Module 5: Fairness and Bias

You will learn how to:

  • Define and explain the importance of fairness and bias in AI
  • 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

Module 6: Connecting the Concepts

You will learn how to:

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

Module 7: Responsible Generative AI

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

                     

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