# Antoniak and Mimno 2021 – Bad Seeds: Evaluating Lexical Methods for Bias Measurement

## 1. background

What is a seed lexicon? It's an organization of words into sets to measure bias. So if I wanted to measure the bias in word embeddings between Male and Female names, first I would collect a sets of these names.

How to test for bias in word embeddings:

- Use Word Embedding Association Test (WEAT). Given two sets \(\mathcal{X}\) and \(\mathcal{Y}\) from a seed lexicon, e.g. Male and Female names, and two other sets \(A\) and \(B\), e.g. professional work words and domestic work words, I might want to measure if the \(\mathcal{X}\) names are associated with one set in a way that the \(\mathcal{Y}\) names are not. So, I take:

\[ s(\mathcal{X}, \mathcal{Y}, A, B) = \sum_{x\in\mathcal{X}}s(x, A, B) - \sum_{y\in\mathcal{Y}}s(y, A, B) \] where \[ s(w,A,B) = \frac{1}{|A|}\sum_{a\in A} \text{sim}(w, a) - \frac{1}{|B|}\sum_{b\in B} \text{sim}(w, b) \] and \(\text{sim}\) is cosine-similarity. Intuition: \(s(w, A, B)\) measures how much more similar \(w\) is to the set of words \(A\) and \(B\). \(s(\mathcal{X}, \mathcal{Y}, A, B)\) measures that effect in aggregate.

- Given two sets from a seed lexicon, do PCA on all the embeddings. Measure how much variance is explained by the first component of the resulting vectors. Assuming that the first component separates based on
*bias*, i.e. encoded cultural biases, then we will see a lot of explained separation along this axis.

## 2. key idea

Who even comes up with seed-lexicons? How sensitive are bias tests, e.g. WEAT and PCA, to swapping out different lexicons? Answer: they're somewhat sensitive. So, you should examine where you get your seed lexicons.