Evaluating Statistical and Causal Fairness in NLP Models: Disparities and Combination Approaches

Core Concepts

Statistical and causal fairness metrics can produce inconsistent results, and debiasing methods targeting one type of fairness may not improve the other. Combining statistical and causal debiasing techniques can achieve better overall fairness.

Abstract

The paper examines the disparities between statistical and causal fairness metrics in evaluating gender bias in NLP models. It first demonstrates that statistical and causal bias metrics can sometimes disagree on the direction of bias in a model. The authors then cross-evaluate existing debiasing methods, including statistical resampling and reweighting, as well as causal counterfactual data augmentation (CDA), on both types of fairness metrics. They find that methods targeting one type of fairness may not improve the other, and may even worsen it.
To address this issue, the authors propose combination approaches that integrate both statistical and causal debiasing techniques. These methods, such as undersampling with CDA (US-CDA) and reweighting with CDA (RW-CDA), are shown to achieve the best overall performance on reducing bias according to both statistical and causal fairness metrics, outperforming the individual debiasing methods. The results highlight the importance of considering multiple fairness notions when evaluating and mitigating biases in NLP models.

Stats

"Statistical PPR gap (SGPPR) between binary genders g (female) and ¬g (male) can be defined as: E[Ŷ = 1 | G = g] - E[Ŷ = 1 | G = ¬g]"
"Statistical TPR gap of binary genders for class y can be formulated as: SGTPR^y = TPRs(g, y) - TPRs(¬g, y)"
"Causal PPR Gap (CGPPR) can be estimated by the average causal effect of the protected characteristic on the model's prediction being positive: E[Ŷ = 1 | do(G = g)] - E[Ŷ = 1 | do(G = ¬g)]"
"Causal TPR gap can be defined as: CGTPR^y = TPRc(g, y) - TPRc(¬g, y)"

Quotes

"Statistical fairness calls for statistically equivalent outcomes for all protected groups. Statistical bias metrics estimate the difference in prediction outcomes between protected groups based on observational data."
"Causal fairness shifts the focus from statistical association to identifying root causes of unfairness through causal reasoning. Causal bias metrics measure the effect of the protected attribute on the model's predictions via interventions that change the value of the protected attribute."

Key Insights Distilled From

Addressing Both Statistical and Causal Gender Fairness in NLP Models

by Hannah Chen,... at arxiv.org 04-02-2024

https://arxiv.org/pdf/2404.00463.pdf

Deeper Inquiries

What other types of fairness notions beyond statistical and causal fairness could be considered for evaluating and mitigating biases in NLP models

In addition to statistical and causal fairness, other types of fairness notions that could be considered for evaluating and mitigating biases in NLP models include individual fairness and group fairness.

Individual Fairness: This notion focuses on treating similar individuals similarly, regardless of their protected attributes. It aims to ensure that similar individuals receive similar predictions or outcomes from the model, irrespective of their background characteristics. By incorporating individual fairness, NLP models can strive to provide equitable treatment to individuals based on their unique characteristics and not solely on group membership.

Group Fairness: Group fairness, also known as demographic parity, emphasizes equal representation and outcomes for different groups within the dataset. It aims to ensure that the predictions or decisions made by the model are balanced across various demographic groups, such as gender, race, or age. By considering group fairness, NLP models can mitigate biases that disproportionately impact certain groups and promote equal opportunities for all groups represented in the data.

By incorporating these additional fairness notions alongside statistical and causal fairness, NLP practitioners can adopt a more comprehensive approach to evaluating and addressing biases in their models, leading to more equitable and inclusive AI systems.

How can the proposed combination debiasing methods be extended to handle non-binary protected attributes, such as race or intersectional identities

To extend the proposed combination debiasing methods to handle non-binary protected attributes, such as race or intersectional identities, several adaptations and considerations can be made:

Data Representation: Modify the data representation to accommodate non-binary attributes by encoding them appropriately in the dataset. This may involve creating new features or representations that capture the nuances of intersectional identities.

Debiasing Techniques: Extend the combination debiasing methods to incorporate strategies that specifically address non-binary attributes. This could involve developing debiasing algorithms that consider multiple dimensions of identity simultaneously, such as race and gender, to ensure fair treatment across intersecting identities.

Evaluation Metrics: Adjust the evaluation metrics to account for the complexities of non-binary attributes. Develop new bias metrics that capture the unique challenges and biases associated with intersectional identities, allowing for a more nuanced assessment of fairness in NLP models.

By adapting the proposed combination debiasing methods to handle non-binary protected attributes, NLP practitioners can work towards creating more inclusive and equitable AI systems that consider the diverse identities and experiences present in real-world data.

Can the insights from this work on disparities between fairness metrics be generalized to other domains beyond NLP, such as tabular data or computer vision

The insights from this work on disparities between fairness metrics in NLP can indeed be generalized to other domains beyond NLP, such as tabular data or computer vision. The fundamental principles of statistical and causal fairness, as well as the challenges and trade-offs associated with different bias metrics, are applicable across various machine learning domains.

Tabular Data: In tabular data settings, similar disparities between statistical and causal fairness metrics may arise, impacting the evaluation and mitigation of biases in predictive models. By considering the lessons learned from NLP research, practitioners working with tabular data can adopt a more nuanced approach to bias evaluation and mitigation.

Computer Vision: In computer vision applications, the need for fair and unbiased models is equally crucial. By understanding the disparities between fairness metrics and the limitations of different debiasing techniques, researchers and practitioners in computer vision can develop more robust and equitable image classification and recognition systems.

By leveraging the insights and methodologies developed in the context of NLP, researchers and practitioners in other domains can enhance their approaches to bias evaluation, mitigation, and fairness in machine learning models.