# Fairness in representation: quantifying stereotyping as a representational harm

arXiv: https://arxiv.org/abs/1901.09565 · HTML: https://mlsanity.com/paper/1901.09565/

## Key facts

- Cited by 26 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Jan 2019.

## Built on by (most cited first, 26 of 26)

- [The Values Encoded in Machine Learning Research](https://mlsanity.com/paper/2106.15590/) (2021)
- [Fairness for Unobserved Characteristics: Insights from Technological Impacts on Queer Communities](https://mlsanity.com/paper/2102.04257/) (2021)
- [Your fairness may vary: Pretrained language model fairness in toxic text classification](https://mlsanity.com/paper/2108.01250/) (2021)
- [Measuring Representational Harms in Image Captioning](https://mlsanity.com/paper/2206.07173/) (2022)
- [On The Impact of Machine Learning Randomness on Group Fairness](https://mlsanity.com/paper/2307.04138/) (2023)
- [Bias and Fairness in Chatbots: An Overview](https://mlsanity.com/paper/2309.08836/) (2023)
- [Position is Power: System Prompts as a Mechanism of Bias in Large Language Models (LLMs)](https://mlsanity.com/paper/2505.21091/) (2025)
- [The worst of both worlds: A comparative analysis of errors in learning from data in psychology and machine learning](https://mlsanity.com/paper/2203.06498/) (2022)
- [Gender Stereotyping Impact in Facial Expression Recognition](https://mlsanity.com/paper/2210.05332/) (2022)
- [Fairness and representation in satellite-based poverty maps: Evidence of urban-rural disparities and their impacts on downstream policy](https://mlsanity.com/paper/2305.01783/) (2023)
- [An Empirical Investigation into Benchmarking Model Multiplicity for Trustworthy Machine Learning: A Case Study on Image Classification](https://mlsanity.com/paper/2311.14859/) (2023)
- [Precarity: Modeling the Long Term Effects of Compounded Decisions on Individual Instability](https://mlsanity.com/paper/2104.12037/) (2021)
- [Obstructing Classification via Projection](https://mlsanity.com/paper/2105.09047/) (2021)
- [Feature-based Individual Fairness in k-Clustering](https://mlsanity.com/paper/2109.04554/) (2021)
- [Muslim-Violence Bias Persists in Debiased GPT Models](https://mlsanity.com/paper/2310.18368/) (2023)
- [Measuring Machine Learning Harms from Stereotypes Requires Understanding Who Is Harmed by Which Errors in What Ways](https://mlsanity.com/paper/2402.04420/) (2024)
- [It's Not Fairness, and It's Not Fair: The Failure of Distributional Equality and the Promise of Relational Equality in Complete-Information Hiring Games](https://mlsanity.com/paper/2209.05602/) (2022)
- [An Investigation of Representation and Allocation Harms in Contrastive Learning](https://mlsanity.com/paper/2310.01583/) (2023)
- [Break Out of a Pigeonhole: A Unified Framework for Examining Miscalibration, Bias, and Stereotype in Recommender Systems](https://mlsanity.com/paper/2312.17443/) (2023)
- [Multi-Group Proportional Representation in Retrieval](https://mlsanity.com/paper/2407.08571/) (2024)
- [Unequal Voices: How LLMs Construct Constrained Queer Narratives](https://mlsanity.com/paper/2507.15585/) (2025)
- [Explainability for identification of vulnerable groups in machine learning models](https://mlsanity.com/paper/2203.00317/) (2022)
- [Measuring and mitigating voting access disparities: a study of race and polling locations in Florida and North Carolina](https://mlsanity.com/paper/2205.14867/) (2022)
- [Legally Binding but Unfair? Towards Assessing Fairness of Privacy Policies](https://mlsanity.com/paper/2403.08115/) (2024)
- [Mitigation of Gender and Ethnicity Bias in AI-Generated Stories through Model Explanations](https://mlsanity.com/paper/2509.04515/) (2025)
- [Unmasking LAION-5B: Age, Gender, Race, and Emotion Biases in Large-Scale Image Datasets](https://mlsanity.com/paper/2606.23204/) (2026)

## Abstract (arXiv)

While harms of allocation have been increasingly studied as part of the subfield of algorithmic fairness, harms of representation have received considerably less attention. In this paper, we formalize two notions of stereotyping and show how they manifest in later allocative harms within the machine learning pipeline. We also propose mitigation strategies and demonstrate their effectiveness on synthetic datasets.