Black is to Criminal as Caucasian is to Police: Detecting and Removing Multiclass Bias in Word Embeddings (1904.04047v3)

Abstract: Online texts -- across genres, registers, domains, and styles -- are riddled with human stereotypes, expressed in overt or subtle ways. Word embeddings, trained on these texts, perpetuate and amplify these stereotypes, and propagate biases to machine learning models that use word embeddings as features. In this work, we propose a method to debias word embeddings in multiclass settings such as race and religion, extending the work of (Bolukbasi et al., 2016) from the binary setting, such as binary gender. Next, we propose a novel methodology for the evaluation of multiclass debiasing. We demonstrate that our multiclass debiasing is robust and maintains the efficacy in standard NLP tasks.

• The paper introduces a novel framework that extends binary debiasing techniques to detect and mitigate multiclass biases in word embeddings.
• The paper identifies bias subspaces using PCA and applies neutralizing and equalizing strategies to effectively remove bias components.
• The paper validates its approach with significant improvements in Mean Average Cosine scores while preserving embedding utility in downstream NLP tasks.

Overview of "Black is to Criminal as Caucasian is to Police: Detecting and Removing Multiclass Bias in Word Embeddings"

This paper addresses the critical issue of multiclass bias within word embeddings—a pivotal concern in machine learning systems sourced from human-generated data. While prior studies have concentrated predominantly on binary biases such as gender, this research extends the discourse to encompass multiclass attributes, notably race and religion.

Key Contributions

The authors present a comprehensive approach for detecting and mitigating multiclass bias in word embeddings. Their method is an extension of the binary debiasing technique by Bolukbasi et al. (2016), targeting bias subspaces for attributes that defy binary categorization. Data sourced from Reddit underscores the illustrative examples of gender, racial, and religious biases in word embeddings, elucidated through analogies that pair social classes with stereotypes, such as "black is to criminal" or "Muslim is to terrorist."

Methodological Framework

1. Bias Subspace Identification: The technique involves identifying a "bias subspace" by applying Principal Component Analysis (PCA) to sets of word embeddings associated with various biases. This subspace captures class-specific components that express the embedded biases across multiple classes.
2. Debiasing Methodologies: Employing "Neutralize and Equalize" and "Equalizing and Soften" strategies allows for the partial or complete removal of identified bias components from the embeddings. These debiasing operations preserve word embedding utility while aiming to rectify inherent biases.
3. Evaluation through MAC and Downstream Tasks: The researchers propose a new metric, Mean Average Cosine (MAC), for assessing bias levels post-debiasing. Statistical tests reveal significant improvements in MAC scores, indicating reduced biases. Furthermore, the impact of debiasing on downstream natural language processing tasks like NER, POS tagging, and chunking is analyzed, demonstrating only negligible performance variations, thereby affirming the utility maintenance of embeddings.

Results and Implications

The research demonstrates that the application of multiclass debiasing procedures results in statistically significant reductions in bias, as evidenced by improved MAC scores. While the debiasing effects on some tasks showed slight performance degradation, the overall utility of embeddings remains intact. This result suggests that multiclass bias subspaces can be effectively neutralized without substantial semantic losses in applications.

Theoretical and Practical Implications

Theoretically, this work advances our understanding of bias representation in word embeddings by introducing methodologies applicable to non-binary attributes. Practically, it offers a robust framework for developers to incorporate bias detection and mitigation into systems employing machine learning, specifically when dealing with diverse social categories.

Future Directions

Although the presented method successfully mitigates multiclass biases in word embeddings, further research could optimize these techniques. For example, exploring alternative methodologies for bias subspace calculation could provide enhanced precision, especially in disentangling overlapping biases. Additionally, extending the scope to include more complex social attributes, such as those with fluid or dynamic categorizations, would diversify the applicability of these findings.

In conclusion, this paper contributes significantly to the field by providing a nuanced exploration of bias within word embeddings and offering practical debiasing solutions. The methodology and findings not only broaden the scope of bias detection research but also have profound implications for building equitable machine learning systems.