# Kantian Deontology Meets AI Alignment: Towards Morally Grounded Fairness Metrics

arXiv: https://arxiv.org/abs/2311.05227 · HTML: https://mlsanity.com/paper/2311.05227/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 66 references that are in the map.
- First posted on arXiv 9 Nov 2023.

## Builds on (most cited first, 15 of 15)

- [Equality of Opportunity in Supervised Learning](https://mlsanity.com/paper/1610.02413/) (2016)
- [Counterfactual Fairness](https://mlsanity.com/paper/1703.06856/) (2017)
- [Artificial Intelligence, Values and Alignment](https://mlsanity.com/paper/2001.09768/) (2020)
- [Minimax Pareto Fairness: A Multi Objective Perspective](https://mlsanity.com/paper/2011.01821/) (2020)
- [Fairness in Machine Learning: Lessons from Political Philosophy](https://mlsanity.com/paper/1712.03586/) (2017)
- [Why Fairness Cannot Be Automated: Bridging the Gap Between EU Non-Discrimination Law and AI](https://mlsanity.com/paper/2005.05906/) (2020)
- [Post-processing for Individual Fairness](https://mlsanity.com/paper/2110.13796/) (2021)
- [A Moral Framework for Understanding of Fair ML through Economic Models of Equality of Opportunity](https://mlsanity.com/paper/1809.03400/) (2018)
- [Empirical observation of negligible fairness-accuracy trade-offs in machine learning for public policy](https://mlsanity.com/paper/2012.02972/) (2020)
- [Algorithmic Fairness from a Non-ideal Perspective](https://mlsanity.com/paper/2001.09773/) (2020)
- [Algorithmic Auditing and Social Justice: Lessons from the History of Audit Studies](https://mlsanity.com/paper/2109.06974/) (2021)
- [Affirmative Algorithms: The Legal Grounds for Fairness as Awareness](https://mlsanity.com/paper/2012.14285/) (2020)
- [Beneficent Intelligence: A Capability Approach to Modeling Benefit, Assistance, and Associated Moral Failures through AI Systems](https://mlsanity.com/paper/2308.00868/) (2023)
- [Toward a Theory of Justice for Artificial Intelligence](https://mlsanity.com/paper/2110.14419/) (2021)
- [Disentangling and Operationalizing AI Fairness at LinkedIn](https://mlsanity.com/paper/2306.00025/) (2023)

## Built on by (most cited first, 1 of 1)

- [Towards Friendly AI: A Comprehensive Review and New Perspectives on Human-AI Alignment](https://mlsanity.com/paper/2412.15114/) (2024)

## Abstract (arXiv)

Deontological ethics, specifically understood through Immanuel Kant, provides a moral framework that emphasizes the importance of duties and principles, rather than the consequences of action. Understanding that despite the prominence of deontology, it is currently an overlooked approach in fairness metrics, this paper explores the compatibility of a Kantian deontological framework in fairness metrics, part of the AI alignment field. We revisit Kant's critique of utilitarianism, which is the primary approach in AI fairness metrics and argue that fairness principles should align with the Kantian deontological framework. By integrating Kantian ethics into AI alignment, we not only bring in a widely-accepted prominent moral theory but also strive for a more morally grounded AI landscape that better balances outcomes and procedures in pursuit of fairness and justice.