Analyzing Fairness of Natural Language Representations for Downstream Classification Tasks

Konsep Inti

Analyzing the fairness of natural language representations, such as sentence and document encodings, and proposing a method to improve fairness while maintaining classification accuracy.

Abstrak

The paper analyzes the fairness of natural language representations, specifically sentence and document encodings, in the context of binary classification tasks. It focuses on two real-world datasets: the Hindi Legal Document Corpus (HLDC) for bail prediction and the Multilingual Twitter Corpus (MTC) for hate speech recognition.

The authors examine the fairness of two common encoding strategies, vector averaging and vector extrema, by analyzing the differences in the reconstruction errors of the principal components for different subgroups based on protected attributes (religion, ethnicity, and gender). They find that the vector averaging approach shows bias towards certain subgroups, while vector extrema is more fair in this regard.

To balance the trade-off between fairness and accuracy, the authors propose using a convex combination of the two encoding strategies. They provide recommendations on choosing an optimal combination based on the available leeway to compromise on accuracy in favor of stricter representation-level fairness requirements.

The key highlights of the paper are:

1. Analyzing fairness of natural language representations using PCA reconstruction errors for different subgroups.
2. Identifying biases in common encoding strategies like vector averaging and vector extrema.
3. Proposing a convex combination of the two encoding approaches to improve fairness while maintaining classification accuracy.
4. Providing guidelines on choosing the optimal combination based on the specific fairness-accuracy trade-off requirements.

Statistik

"The average document length in the HLDC dataset is 187.2 words."
"The MTC-gen and MTC-eth datasets have an average sentence length of 17.28 words."

Kutipan

"We find a noticeable difference in performance on the datasets when each of vector extrema and vector average are used. Vector-average consistently performs better than vector extrema on all the datasets."
"We find around 10x difference between the gaps in the random splits within group and the gaps in splits across groups of gender in the MTC-gen dataset when using vector average."

Wawasan Utama Disaring Dari

Closing the Gap in the Trade-off between Fair Representations and Accuracy

by Biswajit Rou... pada arxiv.org 04-16-2024

https://arxiv.org/pdf/2404.09664.pdf

Pertanyaan yang Lebih Dalam

How can the proposed fairness analysis be extended to other types of natural language representations beyond sentence/document encodings?

The proposed fairness analysis based on PCA reconstruction errors can be extended to other types of natural language representations by considering different levels of linguistic units. For instance, instead of just focusing on sentence or document encodings, one could analyze word embeddings or even sub-word embeddings for bias towards specific groups. By conducting similar PCA reconstruction error analyses on word or sub-word level representations, researchers can gain insights into how biases manifest at different linguistic levels. Additionally, exploring contextual embeddings like BERT or GPT models and examining their biases through similar fairness analyses could provide a more comprehensive understanding of representation-level fairness in NLP.

What are the potential implications of representation-level fairness on the downstream model performance and real-world deployment of these classification systems?

Representation-level fairness plays a crucial role in determining the overall fairness and effectiveness of downstream classification models. Biases in the underlying representations can lead to discriminatory outcomes in the classification tasks, impacting the model's performance and fairness. If the representations encode biases towards certain groups, it can result in unequal treatment or inaccurate predictions for those groups, leading to ethical concerns and potential legal implications. In real-world deployment, biased representations can perpetuate societal inequalities, reinforce stereotypes, and harm marginalized communities. Ensuring representation-level fairness is essential for building trustworthy and ethical AI systems that uphold principles of fairness and equity.

Can the optimal trade-off between fairness and accuracy be dynamically adjusted based on the specific requirements of the application domain?

Yes, the optimal trade-off between fairness and accuracy can be dynamically adjusted based on the specific requirements of the application domain. Different applications may have varying degrees of tolerance for accuracy trade-offs in exchange for fairness. By incorporating domain-specific constraints and considerations, such as legal regulations, ethical guidelines, or societal impact, the trade-off between fairness and accuracy can be tailored to meet the specific needs of the application. For instance, in sensitive domains like healthcare or criminal justice, where fairness is paramount, a higher emphasis on fairness over accuracy may be preferred. On the other hand, in applications where accuracy is critical, such as fraud detection, a more balanced trade-off between fairness and accuracy may be necessary. By flexibly adjusting this trade-off, organizations can align their AI systems with the values and requirements of the domain, ensuring responsible and effective deployment of classification systems.