# Fairness Score and Process Standardization: Framework for Fairness Certification in Artificial Intelligence Systems

arXiv: https://arxiv.org/abs/2201.06952 · HTML: https://mlsanity.com/paper/2201.06952/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- Builds on 21 of its 61 references that are in the map.
- First posted on arXiv 10 Jan 2022.

## Builds on (most cited first, 21 of 21)

- [Equality of Opportunity in Supervised Learning](https://mlsanity.com/paper/1610.02413/) (2016)
- [A Survey on Bias and Fairness in Machine Learning](https://mlsanity.com/paper/1908.09635/) (2019)
- [Counterfactual Fairness](https://mlsanity.com/paper/1703.06856/) (2017)
- [Inherent Trade-Offs in the Fair Determination of Risk Scores](https://mlsanity.com/paper/1609.05807/) (2016)
- [Algorithmic decision making and the cost of fairness](https://mlsanity.com/paper/1701.08230/) (2017)
- [AI Fairness 360: An Extensible Toolkit for Detecting, Understanding, and Mitigating Unwanted Algorithmic Bias](https://mlsanity.com/paper/1810.01943/) (2018)
- [The Frontiers of Fairness in Machine Learning](https://mlsanity.com/paper/1810.08810/) (2018)
- [50 Years of Test (Un)fairness: Lessons for Machine Learning](https://mlsanity.com/paper/1811.10104/) (2018)
- [Discrimination in Online Ad Delivery](https://mlsanity.com/paper/1301.6822/) (2013)
- [Achieving Fairness through Adversarial Learning: an Application to Recidivism Prediction](https://mlsanity.com/paper/1807.00199/) (2018)
- [Bias Mitigation Post-processing for Individual and Group Fairness](https://mlsanity.com/paper/1812.06135/) (2018)
- [CERTIFAI: Counterfactual Explanations for Robustness, Transparency, Interpretability, and Fairness of Artificial Intelligence models](https://mlsanity.com/paper/1905.07857/) (2019)
- [On the Moral Justification of Statistical Parity](https://mlsanity.com/paper/2011.02079/) (2020)
- [This Thing Called Fairness: Disciplinary Confusion Realizing a Value in Technology](https://mlsanity.com/paper/1909.11869/) (2019)
- [Bias and Discrimination in AI: a cross-disciplinary perspective](https://mlsanity.com/paper/2008.07309/) (2020)
- [Fairness in the Eyes of the Data: Certifying Machine-Learning Models](https://mlsanity.com/paper/2009.01534/) (2020)
- [Evaluating Fairness Metrics in the Presence of Dataset Bias](https://mlsanity.com/paper/1809.09245/) (2018)
- [Data, Power and Bias in Artificial Intelligence](https://mlsanity.com/paper/2008.07341/) (2020)
- [FairTest: Discovering Unwarranted Associations in Data-Driven Applications](https://mlsanity.com/paper/1510.02377/) (2015)
- [Fairness-aware machine learning: a perspective](https://mlsanity.com/paper/1708.00754/) (2017)
- [Towards Fairness Certification in Artificial Intelligence](https://mlsanity.com/paper/2106.02498/) (2021)

## Built on by (most cited first, 14 of 14)

- [(Local) Differential Privacy has NO Disparate Impact on Fairness](https://mlsanity.com/paper/2304.12845/) (2023)
- [A Framework for Responsible AI Systems: Building Societal Trust through Domain Definition, Trustworthy AI Design, Auditability, Accountability, and Governance](https://mlsanity.com/paper/2503.04739/) (2025)
- [On the Relationship Between Interpretability and Explainability in Machine Learning](https://mlsanity.com/paper/2311.11491/) (2023)
- [Enhancements for Developing a Comprehensive AI Fairness Assessment Standard](https://mlsanity.com/paper/2504.07516/) (2025)
- [Ethical AI Governance: Methods for Evaluating Trustworthy AI](https://mlsanity.com/paper/2409.07473/) (2024)
- [A five-layer framework for AI governance: integrating regulation, standards, and certification](https://mlsanity.com/paper/2509.11332/) (2025)
- [A Seven-Layer Model for Standardising AI Fairness Assessment](https://mlsanity.com/paper/2212.11207/) (2022)
- [Size-adaptive Hypothesis Testing for Fairness](https://mlsanity.com/paper/2506.10586/) (2025)
- [An Analysis of the New EU AI Act and A Proposed Standardization Framework for Machine Learning Fairness](https://mlsanity.com/paper/2510.01281/) (2025)
- [A federated architecture for sector-led AI governance: lessons from India](https://mlsanity.com/paper/2603.26865/) (2026)
- [What Work is AI Actually Doing? Uncovering the Drivers of Generative AI Adoption](https://mlsanity.com/paper/2510.23669/) (2025)
- [Evaluation of AI Ethics Tools in Language Models: A Developers' Perspective Case Study](https://mlsanity.com/paper/2512.15791/) (2025)
- [Nishpaksh: TEC Standard-Compliant Framework for Fairness Auditing and Certification of AI Models](https://mlsanity.com/paper/2601.16926/) (2026)
- [A Blended Likelihood Approach for Achieving Fairness Using Naive Bayes](https://mlsanity.com/paper/2605.25228/) (2026)

## Abstract (arXiv)

Decisions made by various Artificial Intelligence (AI) systems greatly influence our day-to-day lives. With the increasing use of AI systems, it becomes crucial to know that they are fair, identify the underlying biases in their decision-making, and create a standardized framework to ascertain their fairness. In this paper, we propose a novel Fairness Score to measure the fairness of a data-driven AI system and a Standard Operating Procedure (SOP) for issuing Fairness Certification for such systems. Fairness Score and audit process standardization will ensure quality, reduce ambiguity, enable comparison and improve the trustworthiness of the AI systems. It will also provide a framework to operationalise the concept of fairness and facilitate the commercial deployment of such systems. Furthermore, a Fairness Certificate issued by a designated third-party auditing agency following the standardized process would boost the conviction of the organizations in the AI systems that they intend to deploy. The Bias Index proposed in this paper also reveals comparative bias amongst the various protected attributes within the dataset. To substantiate the proposed framework, we iteratively train a model on biased and unbiased data using multiple datasets and check that the Fairness Score and the proposed process correctly identify the biases and judge the fairness.