# Toward A Logical Theory Of Fairness and Bias

arXiv: https://arxiv.org/abs/2306.13659 · HTML: https://mlsanity.com/paper/2306.13659/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 72 references that are in the map.
- First posted on arXiv 8 Jun 2023.

## Builds on (most cited first, 10 of 10)

- [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)
- [Fairness Beyond Disparate Treatment & Disparate Impact: Learning Classification without Disparate Mistreatment](https://mlsanity.com/paper/1610.08452/) (2016)
- [Fairness-Aware Explainable Recommendation over Knowledge Graphs](https://mlsanity.com/paper/2006.02046/) (2020)
- [Causal Modeling for Fairness in Dynamical Systems](https://mlsanity.com/paper/1909.09141/) (2019)
- [Towards Formal Definitions of Blameworthiness, Intention, and Moral Responsibility](https://mlsanity.com/paper/1810.05903/) (2018)
- [A Logic of "Black Box" Classifier Systems](https://mlsanity.com/paper/2210.07161/) (2022)
- [Towards Logical Specification of Statistical Machine Learning](https://mlsanity.com/paper/1907.10327/) (2019)
- [Reformulating the Situation Calculus and the Event Calculus in the General Theory of Stable Models and in Answer Set Programming](https://mlsanity.com/paper/1401.4607/) (2014)

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

- [Evaluating AI Group Fairness: a Fuzzy Logic Perspective](https://mlsanity.com/paper/2406.18939/) (2024)

## Abstract (arXiv)

Fairness in machine learning is of considerable interest in recent years owing to the propensity of algorithms trained on historical data to amplify and perpetuate historical biases. In this paper, we argue for a formal reconstruction of fairness definitions, not so much to replace existing definitions but to ground their application in an epistemic setting and allow for rich environmental modelling. Consequently we look into three notions: fairness through unawareness, demographic parity and counterfactual fairness, and formalise these in the epistemic situation calculus.