# Biased Embeddings from Wild Data: Measuring, Understanding and Removing

arXiv: https://arxiv.org/abs/1806.06301 · HTML: https://mlsanity.com/paper/1806.06301/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Jun 2018.

## Built on by (most cited first, 6 of 6)

- [Theories of "Gender" in NLP Bias Research](https://mlsanity.com/paper/2205.02526/) (2022)
- [Sociodemographic Bias in Language Models: A Survey and Forward Path](https://mlsanity.com/paper/2306.08158/) (2023)
- [ESCAPE: Countering Systematic Errors from Machine's Blind Spots via Interactive Visual Analysis](https://mlsanity.com/paper/2303.09657/) (2023)
- [EXTRACT: Explainable Transparent Control of Bias in Embeddings](https://mlsanity.com/paper/2311.00115/) (2023)
- [Data Bias Management](https://mlsanity.com/paper/2305.09686/) (2023)
- [Compositional Fusion of Signals in Data Embedding](https://mlsanity.com/paper/2311.11085/) (2023)

## Abstract (arXiv)

Many modern Artificial Intelligence (AI) systems make use of data embeddings, particularly in the domain of Natural Language Processing (NLP). These embeddings are learnt from data that has been gathered "from the wild" and have been found to contain unwanted biases. In this paper we make three contributions towards measuring, understanding and removing this problem. We present a rigorous way to measure some of these biases, based on the use of word lists created for social psychology applications; we observe how gender bias in occupations reflects actual gender bias in the same occupations in the real world; and finally we demonstrate how a simple projection can significantly reduce the effects of embedding bias. All this is part of an ongoing effort to understand how trust can be built into AI systems.