# The Long Arc of Fairness: Formalisations and Ethical Discourse

arXiv: https://arxiv.org/abs/2203.06038 · HTML: https://mlsanity.com/paper/2203.06038/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 44 references that are in the map.
- First posted on arXiv 8 Mar 2022.

## Builds on (most cited first, 12 of 12)

- [Equality of Opportunity in Supervised Learning](https://mlsanity.com/paper/1610.02413/) (2016)
- [Counterfactual Fairness](https://mlsanity.com/paper/1703.06856/) (2017)
- [Fair prediction with disparate impact: A study of bias in recidivism prediction instruments](https://mlsanity.com/paper/1610.07524/) (2016)
- [Measurement and Fairness](https://mlsanity.com/paper/1912.05511/) (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)
- [On the Apparent Conflict Between Individual and Group Fairness](https://mlsanity.com/paper/1912.06883/) (2019)
- [On Formalizing Fairness in Prediction with Machine Learning](https://mlsanity.com/paper/1710.03184/) (2017)
- [How Do Fair Decisions Fare in Long-term Qualification?](https://mlsanity.com/paper/2010.11300/) (2020)
- [Downstream Effects of Affirmative Action](https://mlsanity.com/paper/1808.09004/) (2018)
- [A Causal Bayesian Networks Viewpoint on Fairness](https://mlsanity.com/paper/1907.06430/) (2019)
- [Allocating Opportunities in a Dynamic Model of Intergenerational Mobility](https://mlsanity.com/paper/2101.08451/) (2021)

## Built on by (most cited first, 13 of 13)

- [Large Language Models as Recommender Systems: A Study of Popularity Bias](https://mlsanity.com/paper/2406.01285/) (2024)
- [Can Fairness be Automated? Guidelines and Opportunities for Fairness-aware AutoML](https://mlsanity.com/paper/2303.08485/) (2023)
- [Geographical Erasure in Language Generation](https://mlsanity.com/paper/2310.14777/) (2023)
- [What Is Fairness? On the Role of Protected Attributes and Fictitious Worlds](https://mlsanity.com/paper/2205.09622/) (2022)
- [Fairness and Sequential Decision Making: Limits, Lessons, and Opportunities](https://mlsanity.com/paper/2301.05753/) (2023)
- [Manifestations of Xenophobia in AI Systems](https://mlsanity.com/paper/2212.07877/) (2022)
- [FairSense: Long-Term Fairness Analysis of ML-Enabled Systems](https://mlsanity.com/paper/2501.01665/) (2025)
- [Insights From Insurance for Fair Machine Learning](https://mlsanity.com/paper/2306.14624/) (2023)
- [Counterfactual Situation Testing: From Single to Multidimensional Discrimination](https://mlsanity.com/paper/2502.01267/) (2025)
- [Legally Binding but Unfair? Towards Assessing Fairness of Privacy Policies](https://mlsanity.com/paper/2403.08115/) (2024)
- [Privilege Scores](https://mlsanity.com/paper/2502.01211/) (2025)
- [A Critical Review of Predominant Bias in Neural Networks](https://mlsanity.com/paper/2502.11031/) (2025)
- [Algorithmic Fairness Over Time: Advances & Prospects](https://mlsanity.com/paper/2610.15015/) (2026)

## Abstract (arXiv)

In recent years, the idea of formalising and modelling fairness for algorithmic decision making (ADM) has advanced to a point of sophisticated specialisation. However, the relations between technical (formalised) and ethical discourse on fairness are not always clear and productive. Arguing for an alternative perspective, we review existing fairness metrics and discuss some common issues. For instance, the fairness of procedures and distributions is often formalised and discussed statically, disregarding both structural preconditions of the status quo and downstream effects of a given intervention. We then introduce dynamic fairness modelling, a more comprehensive approach that realigns formal fairness metrics with arguments from the ethical discourse. A dynamic fairness model incorporates (1) ethical goals, (2) formal metrics to quantify decision procedures and outcomes and (3) mid-term or long-term downstream effects. By contextualising these elements of fairness-related processes, dynamic fairness modelling explicates formerly latent ethical aspects and thereby provides a helpful tool to navigate trade-offs between different fairness interventions. To illustrate the framework, we discuss an example application -- the current European efforts to increase the number of women on company boards, e.g. via quota solutions -- and present early technical work that fits within our framework.