Quantitative Responsible AI: Principles, Governance, and Methods

Author

Fei Huang, UNSW Sydney

Overview

Quantitative Responsible AI is the systematic use of quantitative methods to measure, evaluate, and manage the fairness, explainability, privacy, and risks of AI systems.

This open learning resource covers responsible AI for consequential automated decision systems: fairness, explainability, privacy, and how to integrate these concerns when systems are built and deployed. The principles are general — insurance recurs as a worked case study throughout, alongside examples from hiring, lending, and healthcare. This material is used to teach ACTL4306 and ACTL6106 at UNSW Sydney.

Lectures

The Responsible AI Song

A song about building AI with care, created with AI assistance.

Fei Huang · Build AI with Care (The Responsible AI Song)

About the Author

Dr. Fei Huang

School of Risk and Actuarial Studies, UNSW Business School

Email: feihuang@unsw.edu.au · Website: feihuang.org

Dr. Fei Huang is an Associate Professor in Risk and Actuarial Studies at UNSW Business School. Her research and teaching focus on responsible AI, insurance, and data-driven decision-making that is accurate, interpretable, and equitable.

License

Materials created for this resource are licensed under the Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0) license unless otherwise noted.

NoteFor educators

This license doesn’t permit public redistribution of adapted versions. If you’d like to adapt this content for your own non-commercial course, please get in touch at feihuang@unsw.edu.au to discuss.

How to cite

Huang, F. (2026). Quantitative Responsible AI: Principles, Governance, and Methods. UNSW Sydney. https://responsible-ai.feihuang.org

To cite this resource, please use this BibTeX entry:

@misc{huang2026quantresponsibleai,
  author    = {Huang, Fei},
  title     = {Quantitative Responsible AI: Principles, Governance, and Methods},
  publisher = {UNSW Sydney},
  year      = {2026},
  url       = {https://responsible-ai.feihuang.org}
}

Acknowledgement

I would like to thank Xi Xin for his help in preparing some of the course materials, and acknowledge Dr. Patrick Laub, whose Quarto website templates supported the development of this site.

Development note

This project was developed with support from AI coding assistants. All analysis, content decisions, and conclusions are the author’s own and her responsibility.