# The Neutrality Fallacy: When Algorithmic Fairness Interventions are (Not) Positive Action

arXiv: https://arxiv.org/abs/2404.12143 · HTML: https://mlsanity.com/paper/2404.12143/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 61 references that are in the map.
- First posted on arXiv 18 Apr 2024.

## Builds on (most cited first, 9 of 9)

- [Equality of Opportunity in Supervised Learning](https://mlsanity.com/paper/1610.02413/) (2016)
- [Measurement and Fairness](https://mlsanity.com/paper/1912.05511/) (2019)
- [Towards a Critical Race Methodology in Algorithmic Fairness](https://mlsanity.com/paper/1912.03593/) (2019)
- [Ground(less) Truth: A Causal Framework for Proxy Labels in Human-Algorithm Decision-Making](https://mlsanity.com/paper/2302.06503/) (2023)
- [Affirmative Algorithms: The Legal Grounds for Fairness as Awareness](https://mlsanity.com/paper/2012.14285/) (2020)
- [Algorithmic Unfairness through the Lens of EU Non-Discrimination Law: Or Why the Law is not a Decision Tree](https://mlsanity.com/paper/2305.13938/) (2023)
- [Counterfactual Prediction Under Outcome Measurement Error](https://mlsanity.com/paper/2302.11121/) (2023)
- [The Misuse of AUC: What High Impact Risk Assessment Gets Wrong](https://mlsanity.com/paper/2305.18159/) (2023)
- [Are There Exceptions to Goodhart's Law? On the Moral Justification of Fairness-Aware Machine Learning](https://mlsanity.com/paper/2202.08536/) (2022)

## Built on by (most cited first, 6 of 6)

- [Rigor in AI: Doing Rigorous AI Work Requires a Broader, Responsible AI-Informed Conception of Rigor](https://mlsanity.com/paper/2506.14652/) (2025)
- [Disability data futures: Achievable imaginaries for AI and disability data justice](https://mlsanity.com/paper/2411.03885/) (2024)
- [A Critical Review of Predominant Bias in Neural Networks](https://mlsanity.com/paper/2502.11031/) (2025)
- [Laypeople's Attitudes Towards Fair, Affirmative, and Discriminatory Decision-Making Algorithms](https://mlsanity.com/paper/2505.07339/) (2025)
- [A Global Comparison of Schemas, Transparency, and Interoperability in Public-Sector AI Registers and Inventories](https://mlsanity.com/paper/2609.24883/) (2026)
- [Fairness Theatre: Evaluating Post-Hoc Fairness Interventions in Vendor-Controlled Early Warning Systems](https://mlsanity.com/paper/2609.38552/) (2026)

## Abstract (arXiv)

Various metrics and interventions have been developed to identify and mitigate unfair outputs of machine learning systems. While individuals and organizations have an obligation to avoid discrimination, the use of fairness-aware machine learning interventions has also been described as amounting to 'algorithmic positive action' under European Union (EU) non-discrimination law. As the Court of Justice of the European Union has been strict when it comes to assessing the lawfulness of positive action, this would impose a significant legal burden on those wishing to implement fair-ml interventions. In this paper, we propose that algorithmic fairness interventions often should be interpreted as a means to prevent discrimination, rather than a measure of positive action. Specifically, we suggest that this category mistake can often be attributed to neutrality fallacies: faulty assumptions regarding the neutrality of fairness-aware algorithmic decision-making. Our findings raise the question of whether a negative obligation to refrain from discrimination is sufficient in the context of algorithmic decision-making. Consequently, we suggest moving away from a duty to 'not do harm' towards a positive obligation to actively 'do no harm' as a more adequate framework for algorithmic decision-making and fair ml-interventions.