What are the biases in my word embedding?

Abstract

This paper presents an algorithm for enumerating biases in word embeddings. The algorithm exposes a large number of offensive associations related to sensitive features such as race and gender on publicly available embeddings, including a supposedly "debiased" embedding. These biases are concerning in light of the widespread use of word embeddings. The associations are identified by geometric patterns in word embeddings that run parallel between people's names and common lower-case tokens. The algorithm is highly unsupervised: it does not even require the sensitive features to be pre-specified. This is desirable because: (a) many forms of discrimination--such as racial discrimination--are linked to social constructs that may vary depending on the context, rather than to categories with fixed definitions; and (b) it makes it easier to identify biases against intersectional groups, which depend on combinations of sensitive features. The inputs to our algorithm are a list of target tokens, e.g. names, and a word embedding. It outputs a number of Word Embedding Association Tests (WEATs) that capture various biases present in the data. We illustrate the utility of our approach on publicly available word embeddings and lists of names, and evaluate its output using crowdsourcing. We also show how removing names may not remove potential proxy bias.

arXiv commentAt AIES 2019: the AAAI/ACM Conference on Artificial Intelligence, Ethics, and Society

References in corpus (8)

• Scikit-learn: Machine Learning in Python
• Distributed Representations of Words and Phrases and their Compositionality
• Semantics derived automatically from language corpora contain human-like biases
• Man is to Computer Programmer as Woman is to Homemaker? Debiasing Word Embeddings
• Word Embeddings Quantify 100 Years of Gender and Ethnic Stereotypes
• Preventing Fairness Gerrymandering: Auditing and Learning for Subgroup Fairness
• No Classification without Representation: Assessing Geodiversity Issues in Open Data Sets for the Developing World
• Evaluating gender portrayal in Bangladeshi TV

Cited by in corpus (5)

• Gender bias and stereotypes in Large Language Models
• Evaluating Biased Attitude Associations of Language Models in an Intersectional Context
• An investigation of structures responsible for gender bias in BERT and DistilBERT
• Mark my Word: A Sequence-to-Sequence Approach to Definition Modeling
• Adversarial Reweighting for Speaker Verification Fairness