Beginner AI Ethics Learning Path

This course explores the ethics and responsible use of generative AI tools. Learners will engage with these tools with a focus on intentionality, sustainability, and responsibility, and learn to evaluate them using the SIFT process.

This certification course provides policymakers, analysts, and public sector professionals with the knowledge to use AI responsibly. It covers governance frameworks, ethics, and real-world applications from predictive analytics to public safety.

An advanced course by IBM that covers feature engineering, data ethics, unsupervised learning, and dimensionality reduction. Students will learn about responsible AI, text mining, and data wrangling.

This course from Columbia University provides an introduction to causal inference. You will learn about the key concepts in causal inference, such as confounding, selection bias, and instrumental variables. The course includes lectures, quizzes, and a final project.

This course provides an introduction to generative AI, covering topics like machine learning, virtual environments, and responsible AI.

This course covers the best practices for testing machine learning systems. You'll learn how to design and implement tests for data, models, and infrastructure. The course also covers topics such as fairness, privacy, and security in the context of ML testing.

A professional certificate program from IBM that covers the fundamentals of AI engineering, including machine learning, deep learning, and AI ethics. It has a strong focus on practical, job-ready skills.

An 8-course professional certificate series by IBM that teaches you to build next-generation AI systems using Retrieval-Augmented Generation and Agentic AI. You'll gain skills in LangChain, OpenAI, and responsible AI.

A Harvard University course that teaches the use of causal diagrams (DA Gs) to represent assumptions, understand biases, and guide data analysis for causal inference.

This course provides guidance on balancing AI innovation with risk mitigation, covering frameworks for AI governance, Safety by Design strategies, risk assessment methods, red teaming approaches, and regulatory compliance essentials. It is designed for professionals in Trust & Safety, AI engineering, policy, and brand management.

ML Ops with Weights & Biases

Learn Ethics in AI