AI Ethics
Coursera
Kennesaw State University's two-module introduction to generative AI ethics, built around prompt-engineering choices and case studies such as AI-generated images. Finishers can identify ethical risks in AI output and apply a structured framework when writing prompts.
More resources on AI Ethics
Partnership on AI
Nonprofit consortium of AI companies, civil society groups, and academic institutions publishing research, case studies, and practical guidance on issues such as synthetic media, algorithmic fairness, and worker wellbeing in AI deployment.
Artificial Intelligence: Ethics & Societal Challenges
Four-week Lund University course covering algorithmic bias, AI-enabled surveillance, AI's effects on democracy, machine consciousness, responsibility for autonomous systems and the control problem, with peer-reviewed reflections so learners can reason about the ethical and societal consequences of AI.
aiethicsinitiative.org
An AI ethics resource hub offering research analyses, policy briefs, and practical guidelines to support responsible AI development, governance, and fairness.
AI Ethics
Coeckelbergh's short MIT Press survey of the philosophical questions raised by machine intelligence: responsibility, bias, privacy, moral status and automation. Readers finish able to frame AI policy debates in established ethical theory rather than intuition.
AI for Everyone
AI is not only for engineers. If you want your organization to become better at using AI, this is the course to tell everyone--especially your non-technical colleagues--to take. In this course, you will learn: - The meaning behind common AI terminology, including neural networks, machine learning, deep learning, and data science - What AI realistically can--and cannot--do - How to spot opportunities to apply AI to problems in your own organization - What it feels like to build machine learning and data science projects - How to work with an AI team and build an AI strategy in your company - How to navigate ethical and societal discussions surrounding AI Though this course is largely non-technical, engineers can also take this course to learn the business aspects of AI.
Weapons of Math Destruction
A mathematician and former quant examines opaque algorithms used in credit scoring, hiring, policing, insurance and targeted advertising. Readers learn how data collected about people feeds models that reinforce inequality, and what makes a scoring model scalable, unaccountable and harmful.