# An End-To-End Machine Learning Pipeline That Ensures Fairness Policies

arXiv: https://arxiv.org/abs/1710.06876 · HTML: https://mlsanity.com/paper/1710.06876/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Oct 2017.

## Built on by (most cited first, 3 of 3)

- [Demographic Fairness in Biometric Systems: What do the Experts say?](https://mlsanity.com/paper/2105.14844/) (2021)
- [A Comprehensive Survey and Classification of Evaluation Criteria for Trustworthy Artificial Intelligence](https://mlsanity.com/paper/2410.17281/) (2024)
- [Ethical AI Governance: Methods for Evaluating Trustworthy AI](https://mlsanity.com/paper/2409.07473/) (2024)

## Abstract (arXiv)

In consequential real-world applications, machine learning (ML) based systems are expected to provide fair and non-discriminatory decisions on candidates from groups defined by protected attributes such as gender and race. These expectations are set via policies or regulations governing data usage and decision criteria (sometimes explicitly calling out decisions by automated systems). Often, the data creator, the feature engineer, the author of the algorithm and the user of the results are different entities, making the task of ensuring fairness in an end-to-end ML pipeline challenging. Manually understanding the policies and ensuring fairness in opaque ML systems is time-consuming and error-prone, thus necessitating an end-to-end system that can: 1) understand policies written in natural language, 2) alert users to policy violations during data usage, and 3) log each activity performed using the data in an immutable storage so that policy compliance or violation can be proven later. We propose such a system to ensure that data owners and users are always in compliance with fairness policies.