AI ethics, safety and governance courses and concepts
AI ethics, safety and governance is the work of making AI systems fair, reliable and accountable: understanding how they fail, how they can be misused, and which rules and controls apply to them. Some of the problems are technical, such as a model stating false information or being tricked into ignoring its safety rules. Others are not: a system can work exactly as designed and still treat some groups of people unfairly or break a law where it is used, so the subject covers oversight and regulation as well as technical fixes. Learning it means knowing the common ways AI systems go wrong and the practices and laws meant to prevent them.
AI ethics, safety and governance courses
Learn AI — overviews, with no coding
AI Ethics
DataCamp
An introduction to artificial intelligence
The Open University
Databricks AI Security Fundamentals
Databricks
Explainable Artificial Intelligence (XAI) Concepts
DataCamp
Fundamentals of Generative AI
AWS Skill Builder
GitHub responsible AI
Microsoft Learn
Introduction to Generative AI
Google Skills
Responsible Artificial Intelligence Practices
AWS Skill Builder
Understanding the EU AI Act
DataCamp
Use AI — responsible use and governance in practice
AI Ethics and Governance for Accountants
Learnsignal
AI Fluency for Students
Anthropic
AI Fluency: Framework & Foundations
Anthropic
AI literacy eLearn
PwC's Academy
AI Risk Management for Accountants
Learnsignal
AI Security and Risk Management
DataCamp
AIED: Human-Centered Approaches to AI
NEA (National Education Association)
AIED: Promoting AI Equity and Access
NEA (National Education Association)
AIED: Safe and Ethical Use of AI
NEA (National Education Association)
AIED: Student-Centered AI Literacy
NEA (National Education Association)
Artificial Intelligence Governance
DataCamp
Certificate in Ethical Artificial Intelligence (AI)
CISI
Deploying Claude Enterprise with confidence: The five decisions that shape your rollout
Anthropic
Ethical and responsible use of Generative AI
The Open University
Ethical Considerations for Generative AI
IBM SkillsBuild
Ethical use of generative AI microcourse
CA ANZ
Ethics and Critical Thinking in an AI World
AICPA & CIMA
Ethics in the world of AI: A CPA’s guide to managing the risks
CPA Canada
GenAI governance blueprint microcourse
CA ANZ
GenAI in Corporate Governance and Board Strategy
ICAEW
Large Language Models for Business
DataCamp
Navigating risk management
The Open University
Responsible AI Practices
DataCamp
Responsible Implementation and Compliance
HRCI (HR Certification Institute)
Security, Compliance, and Governance for AI Solutions
AWS Skill Builder
Understanding legal regulation and compliance
The Open University
Develop AI — responsibility when building AI systems
AI ethics, safety and governance terms explained
Most of these terms fall into three groups: principles (alignment, fairness, transparency, interpretability, responsible AI), ways AI systems fail or are misused (bias, hallucination, jailbreaks, prompt injection, deepfakes), and safeguards against those failures (guardrails, red teaming, human in the loop, model cards, watermarking). Several of the principle terms are everyday words with a specific technical meaning, which their definitions set out.
Learn AI covers how AI systems work, without building one; Use AI covers using AI tools in practice, including courses for specific professions; Develop AI covers building AI systems.