# Towards the Right Kind of Fairness in AI

arXiv: https://arxiv.org/abs/2102.08453 · HTML: https://mlsanity.com/paper/2102.08453/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 20 references that are in the map.
- First posted on arXiv 16 Feb 2021.

## Builds on (most cited first, 8 of 8)

- [Equality of Opportunity in Supervised Learning](https://mlsanity.com/paper/1610.02413/) (2016)
- [A Survey on Bias and Fairness in Machine Learning](https://mlsanity.com/paper/1908.09635/) (2019)
- [Fair prediction with disparate impact: A study of bias in recidivism prediction instruments](https://mlsanity.com/paper/1610.07524/) (2016)
- [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)
- [Fairness in Criminal Justice Risk Assessments: The State of the Art](https://mlsanity.com/paper/1703.09207/) (2017)
- [Aequitas: A Bias and Fairness Audit Toolkit](https://mlsanity.com/paper/1811.05577/) (2018)
- [Identifying and Correcting Label Bias in Machine Learning](https://mlsanity.com/paper/1901.04966/) (2019)

## Built on by (most cited first, 15 of 15)

- [The Possibility of Fairness: Revisiting the Impossibility Theorem in Practice](https://mlsanity.com/paper/2302.06347/) (2023)
- [How to be fair? A study of label and selection bias](https://mlsanity.com/paper/2403.14282/) (2024)
- [Embracing Contradiction: Theoretical Inconsistency Will Not Impede the Road of Building Responsible AI Systems](https://mlsanity.com/paper/2505.18139/) (2025)
- [Maximal Fairness](https://mlsanity.com/paper/2304.06057/) (2023)
- [Navigating Fairness Measures and Trade-Offs](https://mlsanity.com/paper/2307.08484/) (2023)
- [Fairness Hacking: The Malicious Practice of Shrouding Unfairness in Algorithms](https://mlsanity.com/paper/2311.06826/) (2023)
- [Beyond Demographic Parity: Redefining Equal Treatment](https://mlsanity.com/paper/2303.08040/) (2023)
- [FairCompass: Operationalising Fairness in Machine Learning](https://mlsanity.com/paper/2312.16726/) (2023)
- [A Human-in-the-Loop Fairness-Aware Model Selection Framework for Complex Fairness Objective Landscapes](https://mlsanity.com/paper/2410.13286/) (2024)
- [Explaining how your AI system is fair](https://mlsanity.com/paper/2105.00667/) (2021)
- [Improving Ethical Outcomes with Machine-in-the-Loop: Broadening Human Understanding of Data Annotations](https://mlsanity.com/paper/2112.09738/) (2021)
- [Fairness in KI-Systemen](https://mlsanity.com/paper/2307.08486/) (2023)
- [Fairpriori: Improving Biased Subgroup Discovery for Deep Neural Network Fairness](https://mlsanity.com/paper/2407.01595/) (2024)
- [MIFair: A Mutual-Information Framework for Intersectionality and Multiclass Fairness](https://mlsanity.com/paper/2604.28030/) (2026)
- [Variable Selection in the Context of AI Fairness](https://mlsanity.com/paper/2608.11251/) (2026)

## Abstract (arXiv)

Fairness is a concept of justice. Various definitions exist, some of them conflicting with each other. In the absence of an uniformly accepted notion of fairness, choosing the right kind for a specific situation has always been a central issue in human history. When it comes to implementing sustainable fairness in artificial intelligence systems, this old question plays a key role once again: How to identify the most appropriate fairness metric for a particular application? The answer is often a matter of context, and the best choice depends on ethical standards and legal requirements. Since ethics guidelines on this topic are kept rather general for now, we aim to provide more hands-on guidance with this document. Therefore, we first structure the complex landscape of existing fairness metrics and explain the different options by example. Furthermore, we propose the "Fairness Compass", a tool which formalises the selection process and makes identifying the most appropriate fairness definition for a given system a simple, straightforward procedure. Because this process also allows to document the reasoning behind the respective decisions, we argue that this approach can help to build trust from the user through explaining and justifying the implemented fairness.