The Fair Pricing Playbook
A practical framework for developing, evaluating, and auditing fair algorithmic pricing systems (2026)
The Fair Pricing Playbook
An open guide for building algorithmic pricing systems that are accurate, defensible, and fair. The playbook has four steps (one page each), with references at the bottom of every step.
Open the site at fair.feihuang.org (source: github.com/feihuangFH/fair-pricing-playbook).
Four steps
| Step | Topic | Question | Page |
|---|---|---|---|
| 1 | Fairness metrics | What is fair? | Define fairness |
| 2 | Fair models | How to achieve fairness? | Design fair pricing |
| 3 | Welfare implications | Who gains and loses? | Assess impact |
| 4 | Fairness testing | Does it work? | Audit the system |
End-to-end audit flow
Step 4 follows an integrated Plan → Audit → Decide → Improve protocol (Huang and Hooker 2026). All design choices are fixed before examining data.
Plan · Audit · Decide · Improve
| Phase | Actions |
|---|---|
| Plan | Select criterion (PD or CDP), legitimate factors, tolerance bands, representative sample |
| Audit | Collect quotes; run statistical fairness tests with corrected inference |
| Decide | Pass, insufficient information, or fail (equivalence testing) |
| Improve | Remediate and re-test; collect more data if unclear |
Full detail: Step 4: Audit the system.
Research map
| Step | Key references |
|---|---|
| 1 | Frees and Huang (2023); Xin and Huang (2024); Krafcheck, Balnozan, and Huang (2026) |
| 2 | Xin and Huang (2024) |
| 3 | Huang, Shimao, and Khern-am-nuai (2026); Huang and Shimao (2026) |
| 4 | Huang and Hooker (2026) |
About the Author
Dr. Fei Huang
Dr. Fei Huang is an Associate Professor (with tenure) in Risk and Actuarial Studies at UNSW Business School. She holds degrees from Xiamen University (BSc), the University of Hong Kong (MPhil), and the Australian National University (PhD). Her research sits at the intersection of responsible AI, insurance, and data-driven decision-making, with emphasis on insurance and retirement systems that stay fair, sustainable, and resilient amid technological and climate change. She draws on statistics, machine learning, economics, and actuarial science to develop approaches that are accurate, interpretable, and equitable.
Her work has been recognised with awards including the Australian Business Deans Council Award for Innovation and Excellence in Research, the Dean’s Award for Distinction, the North American Actuarial Journal Best Paper Award, and the Actuaries Institute Volunteer of the Year Award, and is supported by competitive funding such as Australian Research Council Discovery Projects and the National Industry PhD Program. She is a columnist for Actuaries Digital, works with industry and government on topics from fair pricing to longevity and climate resilience, and has advised regulators internationally, including as an invited expert at the New York State Assembly public hearing on AI in insurance. At UNSW she teaches actuarial data science and responsible AI and has received multiple teaching excellence awards.
License
Materials are licensed under CC BY 4.0. See LICENSE.
How to cite
If you are using this playbook in your academic work, please find below an example for referencing it using the APA citation style.
Huang, F. (2026). The Fair Pricing Playbook: A practical framework for fair algorithmic pricing. fair.feihuang.org (source: github.com/feihuangFH/fair-pricing-playbook).