Lipstick on a Pig: Debiasing Methods Cover up Systematic Gender Biases in Word Embeddings But do not Remove Them (1903.03862v2)

Abstract: Word embeddings are widely used in NLP for a vast range of tasks. It was shown that word embeddings derived from text corpora reflect gender biases in society. This phenomenon is pervasive and consistent across different word embedding models, causing serious concern. Several recent works tackle this problem, and propose methods for significantly reducing this gender bias in word embeddings, demonstrating convincing results. However, we argue that this removal is superficial. While the bias is indeed substantially reduced according to the provided bias definition, the actual effect is mostly hiding the bias, not removing it. The gender bias information is still reflected in the distances between "gender-neutralized" words in the debiased embeddings, and can be recovered from them. We present a series of experiments to support this claim, for two debiasing methods. We conclude that existing bias removal techniques are insufficient, and should not be trusted for providing gender-neutral modeling.

Analyzing Debiasing Methods for Gender Bias in Word Embeddings

The paper, "Lipstick on a Pig: \Debiasing Methods Cover up Systematic Gender Biases\ in Word Embeddings But do not Remove Them," by Hila Gonen and Yoav Goldberg, provides a critical examination of existing gender debiasing techniques applied to word embeddings. The authors argue that while these methods reduce bias along predefined dimensions, they fail to fully eliminate the underlying biases embedded within the data space.

Overview of the Problem

Word embeddings are central to numerous NLP models, yet they often mirror the societal biases present in the training data. Gender bias, for instance, has been repeatedly identified across different models, manifesting through stereotypical associations in embeddings. A prominent example includes word2vec embedding trained on the Google News dataset responding to the analogy "man is to computer programmer as woman is to x" with "x = homemaker."

Existing Debiasing Techniques

The main focus is on debiasing techniques, particularly those proposed by Bolukbasi et al. and Zhao et al. The former employs a post-processing approach to reduce gender bias by eliminating gender-projections in embeddings. Zhao et al. explore a training-phase modification in the GloVe model to encode gender information into a specific dimension, which can subsequently be removed.

Despite achieving substantial bias reduction when measured by predefined metrics (e.g., gender projections), these methods fall short of thoroughly eliminating bias. The argument is substantiated via experiments showing persistent gender associations in ostensibly neutral words post-debiasing.

Experimental Analysis

The authors provide a detailed experimental framework to demonstrate hidden biases in debiased embeddings. Key observations include:

1. Clustering Consistency: After applying debiasing methods, gender-biased words tend to cluster according to original biases, with an accuracy of up to 92.5% for the Hard-Debiased model and 85.6% for the GN-GloVe model.
2. Correlation with Neighbours: Words previously exhibiting gender biases still form clusters with similarly biased words. This is quantified through the correlation between original bias metrics and counts of gender-specific neighbors.
3. Implicit Associations: Tests like the Word Embedding Association Test (WEAT) reveal statistically significant gender associations even after debiasing.
4. Classifier Results: A classifier trained on biased words still predicts gender associations with high accuracy in debiased embeddings, underscoring that these biases remain identifiable.

Implications and Future Directions

The findings highlight serious concerns about the effectiveness of current debiasing strategies. Simply removing a gender-direction does not suffice, as biases are often reflected structurally in the data space, beyond direct associations with gendered terms. This understanding necessitates revisiting how bias is defined, detected, and removed in NLP systems.

While the paper primarily focuses on the limitations of existing methods, it also alludes to the need for developing more comprehensive debiasing techniques. This may involve structuring training datasets, refining bias detection metrics, and creating algorithms capable of identifying and neutralizing subtle, indirect biases.

In future developments, there is a pressing need for AI models to incorporate advanced bias detection mechanisms that go beyond immediate surface-level indicators. Understanding the geometry of word representations and leveraging deeper statistical correlations might aid in developing robust models free from unacceptable biases.

Overall, this work provides a substantial contribution to the discourse on algorithmic fairness within NLP, compelling researchers to consider multidimensional aspects of bias as they continue to refine and innovate debiasing methodologies.