# What's the Problem, Linda? The Conjunction Fallacy as a Fairness Problem

arXiv: https://arxiv.org/abs/2305.09535 · HTML: https://mlsanity.com/paper/2305.09535/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 13 of its 58 references that are in the map.
- First posted on arXiv 16 May 2023.

## Builds on (most cited first, 13 of 13)

- [Causality for Machine Learning](https://mlsanity.com/paper/1911.10500/) (2019)
- [Towards Causal Representation Learning](https://mlsanity.com/paper/2102.11107/) (2021)
- [Towards a Critical Race Methodology in Algorithmic Fairness](https://mlsanity.com/paper/1912.03593/) (2019)
- [Thinking Fast and Slow in AI](https://mlsanity.com/paper/2010.06002/) (2020)
- [Survey on Causal-based Machine Learning Fairness Notions](https://mlsanity.com/paper/2010.09553/) (2020)
- [The Use and Misuse of Counterfactuals in Ethical Machine Learning](https://mlsanity.com/paper/2102.05085/) (2021)
- [Discrimination in the Age of Algorithms](https://mlsanity.com/paper/1902.03731/) (2019)
- [What's Sex Got To Do With Fair Machine Learning?](https://mlsanity.com/paper/2006.01770/) (2020)
- [Causal Discovery for Fairness](https://mlsanity.com/paper/2206.06685/) (2022)
- [Causal Relational Learning](https://mlsanity.com/paper/2004.03644/) (2020)
- [Subverting machines, fluctuating identities: Re-learning human categorization](https://mlsanity.com/paper/2205.13740/) (2022)
- [Counterfactual Situation Testing: Uncovering Discrimination under Fairness given the Difference](https://mlsanity.com/paper/2302.11944/) (2023)
- [Arguments using ontological and causal knowledge](https://mlsanity.com/paper/1401.4144/) (2014)

## Abstract (arXiv)

The field of Artificial Intelligence (AI) is focusing on creating automated decision-making (ADM) systems that operate as close as possible to human-like intelligence. This effort has pushed AI researchers into exploring cognitive fields like psychology. The work of Daniel Kahneman and the late Amos Tversky on biased human decision-making, including the study of the conjunction fallacy, has experienced a second revival because of this. Under the conjunction fallacy a human decision-maker will go against basic probability laws and rank as more likely a conjunction over one of its parts. It has been proven overtime through a set of experiments with the Linda Problem being the most famous one. Although this interdisciplinary effort is welcomed, we fear that AI researchers ignore the driving force behind the conjunction fallacy as captured by the Linda Problem: the fact that Linda must be stereotypically described as a woman. In this paper we revisit the Linda Problem and formulate it as a fairness problem. In doing so we introduce perception as a parameter of interest through the structural causal perception framework. Using an illustrative decision-making example, we showcase the proposed conceptual framework and its potential impact for developing fair ADM systems.