Adversarial Removal of Demographic Attributes from Text Data (1808.06640v2)

Abstract: Recent advances in Representation Learning and Adversarial Training seem to succeed in removing unwanted features from the learned representation. We show that demographic information of authors is encoded in -- and can be recovered from -- the intermediate representations learned by text-based neural classifiers. The implication is that decisions of classifiers trained on textual data are not agnostic to -- and likely condition on -- demographic attributes. When attempting to remove such demographic information using adversarial training, we find that while the adversarial component achieves chance-level development-set accuracy during training, a post-hoc classifier, trained on the encoded sentences from the first part, still manages to reach substantially higher classification accuracies on the same data. This behavior is consistent across several tasks, demographic properties and datasets. We explore several techniques to improve the effectiveness of the adversarial component. Our main conclusion is a cautionary one: do not rely on the adversarial training to achieve invariant representation to sensitive features.

Overview of Adversarial Removal of Demographic Attributes from Text Data

The paper "Adversarial Removal of Demographic Attributes from Text Data," presented at EMNLP 2018, explores an issue of significant importance in the deployment of machine learning-based systems: the potential unconscious bias from demographic attributes encoded within text data. It raises concerns about whether adversarial techniques, which aim to remove these biases, can be relied upon to produce invariant representations of sensitive features.

The research conducted by Elazar and Goldberg identifies a notable problem in text-based neural classifiers: demographic properties are often unintentionally encoded and can be recovered from intermediate representations. Their work challenges the reliability of adversarial training as a tool for eliminating these intrusive attributes, revealing that adversarial components often fall short of completely removing sensitive demographic data.

Methodological Approach

The paper employs a setup where the text data encompasses documents associated with target labels and protected demographic attributes such as race, gender, and age. They utilize adversarial training, constructing a classifier with an encoder that strives to make class predictions oblivious to demographic features.

Their experimentation involved several tasks and datasets to test the effectiveness of adversarial training. While adversarial components frequently settle at chance-level development-set accuracy (suggesting a successful removal process), a secondary post-hoc classifier (attacker network) trained on the encoded representations uncovered significant demographic data leakage from these supposedly sanitized representations.

Key Findings

Through empirical analysis, the research illuminates several critical points:

1. Demographic Encoding: Even when trained for unrelated tasks with balanced datasets, demographic details such as race and gender are distinctly captured in neural network representations.
2. Adversarial Training Limitations: Although adversarial networks seem effective in development settings given chance-level performance, they fail to prevent an attacker network from predicting demographic attributes at above chance levels, revealing the persistence of residual bias.
3. Scaling and Variant Approaches: Attempts to enhance the adversarial component through increased capacity, varied adversarial intensity (weighting), and multiple ensemble approaches did not entirely mitigate leakage—though these adjustments showed varying degrees of improved reduction.

Implications and Future Directions

The paper's cautionary note emphasizes the need for vigilance when employing adversarial techniques to achieve fairness in machine learning. While adversarial training demonstrates some efficacy in reducing demographic information's footprint, it is not infallible. The results suggest a deeper underlying challenge in achieving robust fairness through adversarial frameworks, especially in the field of natural language processing.

The data implies a need for novel methodologies or complementary approaches to enhance robustness against demographic attribute leakage. Future investigations could focus on more sophisticated models and algorithms that can address such intricate problems effectively, potentially incorporating external validation strategies to corroborate internal adversarial results.

In conclusion, Elazar and Goldberg's work provides important insights into the limitations of adversarial training techniques in ensuring the unbiased deployment of text-based automated systems. It is an invitation to the computational linguistics and machine learning communities to continue refining approaches to mitigate bias in learned representations comprehensively.