On the Interpretability and Significance of Bias Metrics in Texts: a PMI-based Approach (2023.acl-short)
Copied to clipboard
| Challenge: | Word embeddings have been used to quantify biases in texts for years, but their statistical properties and advantages have not been studied. |
| Approach: | They propose to use PMI-based metric to quantify bias in corpora by conditional probabilities and odds ratio to approximate it. |
| Outcome: | The proposed measure can be approximated by an odds ratio, which makes statistical inferences cost-effective and meaningful. |
Similar Papers
Measuring Social Biases in Grounded Vision and Language Embeddings (2021.naacl-main)
Copied to clipboard
| Challenge: | Existing methods to measure social biases in word embeddings are limited to visually grounded word embeds . a new study generalizes word embedment associations to visually ground word embeddas . |
| Approach: | They generalize word embeddings' biases to visually grounded word embeds . they propose two generalizations that answer questions about how biase, language, and vision interact . |
| Outcome: | The proposed measures are applied to a new dataset that includes 10,228 images from COCO, Conceptual Captions, and Google Images. |
Robustness and Reliability of Gender Bias Assessment in Word Embeddings: The Role of Base Pairs (2020.aacl-main)
Copied to clipboard
| Challenge: | Existing methods to quantify gender bias in word embeddings are not robust and cannot identify common types of bias. |
| Approach: | They propose to quantify gender bias by using cosine similarity to a pair of gender words and using analogies. |
| Outcome: | The proposed methods are not robust and cannot identify common types of bias, while analogies are unsuitable indicators. |
Unpacking Bias: An Empirical Study of Bias Measurement Metrics, Mitigation Algorithms, and Their Interactions (2024.lrec-main)
Copied to clipboard
| Challenge: | Word embeddings (WE) models reflect gender, racial, and religious stereotypes from the corpus on which they are trained. |
| Approach: | They propose a method that carefully controls for word sets and vector normalization to address these factors. |
| Outcome: | The proposed method detects consistency between different mitigation methods and the evaluation words used by the mitigation methods. |
Mind Your Bias: A Critical Review of Bias Detection Methods for Contextual Language Models (2022.findings-emnlp)
Copied to clipboard
| Challenge: | Existing methods for detection of biases in contextual language models are inconsistent and inconclusive. |
| Approach: | They propose to use word embedding association test to detect biases in contextual language models to compare them with other methods. |
| Outcome: | The proposed methods are inconsistent and inconclusive for language models with word embeddings. |
The Medium Is Not the Message: Deconfounding Document Embeddings via Linear Concept Erasure (2025.emnlp-main)
Copied to clipboard
| Challenge: | Embedding-based similarity metrics can be influenced by content dimensions and spurious attributes like the text’s source or language. |
| Approach: | They propose a debiasing algorithm that removes observed confounders from encoder representations and removes them from the encoder. |
| Outcome: | The proposed method improves on out-of-distribution benchmarks and on benchmarks, but performance is not affected. |
A Transparent Framework for Evaluating Unintended Demographic Bias in Word Embeddings (P19-1)
Copied to clipboard
| Challenge: | Word embeddings suffer from unintended demographic biases, a new study shows . word embedders can cause downstream NLP systems to be unfair, the authors argue . |
| Approach: | They propose a metric to evaluate the fairness of word embeddings via the relative negative sentiment associated with demographic identity terms from various protected groups. |
| Outcome: | The proposed metric measures fairness in word embeddings via the relative negative sentiment associated with demographic identity terms from various protected groups. |
When do Word Embeddings Accurately Reflect Surveys on our Beliefs About People? (2020.acl-main)
Copied to clipboard
| Challenge: | a study of word embeddings shows that social biases are more accurate than survey data for some dimensions of meaning. |
| Approach: | a new study investigates the extent to which word embeddings accurately reflect biases . they find that biased word embeds mirror survey data across 17 dimensions of social meaning . |
| Outcome: | a new study shows that word embeddings accurately reflect biases on average across dimensions of social meaning . biased embedders are more reflective of survey data for some dimensions of meaning than others, the study finds . |
On Measuring Social Biases in Sentence Encoders (N19-1)
Copied to clipboard
| Challenge: | Word embeddings such as word2vec and GloVe exhibit human-like implicit biases based on gender, race, and other social constructs. |
| Approach: | They propose a simple generaliza test to measure bias in word embeddings by comparing two sets of target-concept words to two sets . |
| Outcome: | The proposed test shows that word2vec and word2Ve exhibit human-like implicit biases based on gender, race, and other social constructs. |
No Word Embedding Model Is Perfect: Evaluating the Representation Accuracy for Social Bias in the Media (2022.findings-emnlp)
Copied to clipboard
| Challenge: | Recent work has relied on word embedding bias measures, such as WEAT, but these methods can be inaccurate due to several representation issues, such low-resource settings and token frequency differences. |
| Approach: | They propose to use WEAT to quantify social bias in US online news articles and embed embedding algorithms to account for the aforementioned issues. |
| Outcome: | The proposed algorithms do not match the literature, but they reduce the gap. |
Measuring Fairness with Biased Rulers: A Comparative Study on Bias Metrics for Pre-trained Language Models (2022.naacl-main)
Copied to clipboard
| Challenge: | An increasing awareness of biased patterns in natural language processing resources such as BERT has motivated many metrics to quantify ‘bias’ and ‘fairness’. |
| Approach: | They combine literature survey, correlation analysis and empirical evaluations to evaluate compatibility of fairness metrics for pre-trained language models and their downstream tasks. |
| Outcome: | The proposed measures are not compatible with each other and highly depend on (i) templates, (ii) attribute and target seeds and (iv) the choice of embeddings. |