# Fairness On The Ground: Applying Algorithmic Fairness Approaches to Production Systems

arXiv: https://arxiv.org/abs/2103.06172 · HTML: https://mlsanity.com/paper/2103.06172/

## Key facts

- Cited by 21 papers in the map (data through 14 Oct 2026).
- Builds on 32 of its 64 references that are in the map.
- First posted on arXiv 10 Mar 2021.

## Builds on (most cited first, 30 of 32)

- [Equality of Opportunity in Supervised Learning](https://mlsanity.com/paper/1610.02413/) (2016)
- [Model Cards for Model Reporting](https://mlsanity.com/paper/1810.03993/) (2018)
- [Counterfactual Fairness](https://mlsanity.com/paper/1703.06856/) (2017)
- [Certifying and removing disparate impact](https://mlsanity.com/paper/1412.3756/) (2014)
- [A Reductions Approach to Fair Classification](https://mlsanity.com/paper/1803.02453/) (2018)
- [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)
- [Closing the AI Accountability Gap: Defining an End-to-End Framework for Internal Algorithmic Auditing](https://mlsanity.com/paper/2001.00973/) (2020)
- [Fairness Beyond Disparate Treatment & Disparate Impact: Learning Classification without Disparate Mistreatment](https://mlsanity.com/paper/1610.08452/) (2016)
- [AI Fairness 360: An Extensible Toolkit for Detecting, Understanding, and Mitigating Unwanted Algorithmic Bias](https://mlsanity.com/paper/1810.01943/) (2018)
- [Fairness in Criminal Justice Risk Assessments: The State of the Art](https://mlsanity.com/paper/1703.09207/) (2017)
- [Measurement and Fairness](https://mlsanity.com/paper/1912.05511/) (2019)
- [Improving fairness in machine learning systems: What do industry practitioners need?](https://mlsanity.com/paper/1812.05239/) (2018)
- [The What-If Tool: Interactive Probing of Machine Learning Models](https://mlsanity.com/paper/1907.04135/) (2019)
- [Avoiding Discrimination through Causal Reasoning](https://mlsanity.com/paper/1706.02744/) (2017)
- [Delayed Impact of Fair Machine Learning](https://mlsanity.com/paper/1803.04383/) (2018)
- [Mitigating Bias in Algorithmic Hiring: Evaluating Claims and Practices](https://mlsanity.com/paper/1906.09208/) (2019)
- [Fair Inference On Outcomes](https://mlsanity.com/paper/1705.10378/) (2017)
- [Algorithmic Fairness](https://mlsanity.com/paper/2001.09784/) (2020)
- [Fairness-Aware Ranking in Search & Recommendation Systems with Application to LinkedIn Talent Search](https://mlsanity.com/paper/1905.01989/) (2019)
- [Saving Face: Investigating the Ethical Concerns of Facial Recognition Auditing](https://mlsanity.com/paper/2001.00964/) (2020)
- [Problem Formulation and Fairness](https://mlsanity.com/paper/1901.02547/) (2019)
- [Putting Fairness Principles into Practice: Challenges, Metrics, and Improvements](https://mlsanity.com/paper/1901.04562/) (2019)
- [Causal Reasoning for Algorithmic Fairness](https://mlsanity.com/paper/1805.05859/) (2018)
- [A Short-term Intervention for Long-term Fairness in the Labor Market](https://mlsanity.com/paper/1712.00064/) (2017)
- [Fair Classification and Social Welfare](https://mlsanity.com/paper/1905.00147/) (2019)
- [What does it mean to solve the problem of discrimination in hiring? Social, technical and legal perspectives from the UK on automated hiring systems](https://mlsanity.com/paper/1910.06144/) (2019)
- [Fairness Evaluation in Presence of Biased Noisy Labels](https://mlsanity.com/paper/2003.13808/) (2020)
- [Creating Fair Models of Atherosclerotic Cardiovascular Disease Risk](https://mlsanity.com/paper/1809.04663/) (2018)
- [Awareness in Practice: Tensions in Access to Sensitive Attribute Data for Antidiscrimination](https://mlsanity.com/paper/1912.06171/) (2019)

## Built on by (most cited first, 21 of 21)

- [Sociotechnical Safety Evaluation of Generative AI Systems](https://mlsanity.com/paper/2310.11986/) (2023)
- [A Systematic Study of Bias Amplification](https://mlsanity.com/paper/2201.11706/) (2022)
- [Assessing the Fairness of AI Systems: AI Practitioners' Processes, Challenges, and Needs for Support](https://mlsanity.com/paper/2112.05675/) (2021)
- [Improving the Fairness of Chest X-ray Classifiers](https://mlsanity.com/paper/2203.12609/) (2022)
- [Vision-Language Models Performing Zero-Shot Tasks Exhibit Gender-based Disparities](https://mlsanity.com/paper/2301.11100/) (2023)
- [De-biasing "bias" measurement](https://mlsanity.com/paper/2205.05770/) (2022)
- [Net benefit, calibration, threshold selection, and training objectives for algorithmic fairness in healthcare](https://mlsanity.com/paper/2202.01906/) (2022)
- [Towards Responsible Natural Language Annotation for the Varieties of Arabic](https://mlsanity.com/paper/2203.09597/) (2022)
- [Demographic-Reliant Algorithmic Fairness: Characterizing the Risks of Demographic Data Collection in the Pursuit of Fairness](https://mlsanity.com/paper/2205.01038/) (2022)
- [A comparison of approaches to improve worst-case predictive model performance over patient subpopulations](https://mlsanity.com/paper/2108.12250/) (2021)
- [Towards a multi-stakeholder value-based assessment framework for algorithmic systems](https://mlsanity.com/paper/2205.04525/) (2022)
- [OxonFair: A Flexible Toolkit for Algorithmic Fairness](https://mlsanity.com/paper/2407.13710/) (2024)
- [Disentangling and Operationalizing AI Fairness at LinkedIn](https://mlsanity.com/paper/2306.00025/) (2023)
- [Quantifying and mitigating the impact of label errors on model disparity metrics](https://mlsanity.com/paper/2310.02533/) (2023)
- [Understanding challenges to the interpretation of disaggregated evaluations of algorithmic fairness](https://mlsanity.com/paper/2506.04193/) (2025)
- [Exploring Bengali Religious Dialect Biases in Large Language Models with Evaluation Perspectives](https://mlsanity.com/paper/2407.18376/) (2024)
- [Finding Representative Group Fairness Metrics Using Correlation Estimations](https://mlsanity.com/paper/2109.05697/) (2021)
- [On Optimal Steering to Achieve Exact Fairness](https://mlsanity.com/paper/2509.15759/) (2025)
- [An Operational Perspective to Fairness Interventions: Where and How to Intervene](https://mlsanity.com/paper/2302.01574/) (2023)
- [Resource-constrained Fairness](https://mlsanity.com/paper/2406.01290/) (2024)
- [Federated fairness-aware classification under differential privacy](https://mlsanity.com/paper/2603.24392/) (2026)

## Abstract (arXiv)

Many technical approaches have been proposed for ensuring that decisions made by machine learning systems are fair, but few of these proposals have been stress-tested in real-world systems. This paper presents an example of one team's approach to the challenge of applying algorithmic fairness approaches to complex production systems within the context of a large technology company. We discuss how we disentangle normative questions of product and policy design (like, "how should the system trade off between different stakeholders' interests and needs?") from empirical questions of system implementation (like, "is the system achieving the desired tradeoff in practice?"). We also present an approach for answering questions of the latter sort, which allows us to measure how machine learning systems and human labelers are making these tradeoffs across different relevant groups. We hope our experience integrating fairness tools and approaches into large-scale and complex production systems will be useful to other practitioners facing similar challenges, and illuminating to academics and researchers looking to better address the needs of practitioners.