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LinkedIn Learning

Foundations of Responsible AI

via LinkedIn Learning

Overview

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Learn about the practices needed to perform fairness testing and implement responsible AI systems.

Syllabus

Introduction
  • Understanding responsible AI
1. Philosophy of AI
  • What is AI and how does data enable it?
  • Modern AI development
  • Problems in ML that differ from software engineering
2. Data Awareness and Literacy
  • Big data and where it comes from
  • Seeing trends in data
  • Building data understanding
  • Visualization and comparing data
  • Storytelling with data
3. Ethical Theories
  • Introduction to ethical AI
  • Ethical frameworks
  • Beneficence vs. maleficence
  • Calculating consequences
  • Consequence scanning
  • Common good and equity
4. Responsible AI Principles
  • Fairness
  • Transparency
  • Accountability
  • Explanations
  • Interpretability
  • Inclusivity
5. Algorithmic Harm
  • Why fairness related harms?
  • Critical AI incidents and learnings
  • Bias in the design and development lifecycle
  • Causal reasoning and fairness
  • Risk mitigation in AI
  • Technical aspects of sociotechnical solutions
6. Human Rights and AI
  • Anonymity and data privacy
  • Unintended uses and misuses
  • Unethical business cases
  • Autonomous systems and society
  • Who AI is developed for?
Conclusion
  • AI regulation and applying responsible AI frameworks

Taught by

Ayodele Odubela

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4.6 rating at LinkedIn Learning based on 516 ratings

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