Improving Fairness-Accuracy Tradeoffs by Leveraging Uncertainty in Sensitive Attribute Predictions

Основні поняття

Enforcing fairness constraints on samples with reliable sensitive attribute predictions can significantly improve the fairness-accuracy tradeoff compared to using all samples or samples with uncertain sensitive attributes.

Анотація

The paper proposes a framework called FairDSR to handle fairness in machine learning when demographic information is partially available. The framework consists of two phases:

1. Uncertainty-Aware Sensitive Attribute Prediction:

   • A semi-supervised approach is used to train an attribute classifier that predicts sensitive attributes and estimates the uncertainty of the predictions.
   • The attribute classifier is trained using a student-teacher framework with a consistency loss to ensure the student model focuses on samples with low uncertainty.
   • Monte Carlo dropout is used to estimate the uncertainty of the sensitive attribute predictions.

2. Enforcing Fairness with Reliable Proxy Sensitive Attributes:

   • The label classifier is trained with fairness constraints, but these constraints are only applied to samples whose sensitive attributes are predicted with low uncertainty.
   • Two additional variants are proposed: FairDSR (weighted) and FairDSR (uncertain). The weighted approach applies fairness constraints to all samples but weights them based on the uncertainty of the sensitive attribute predictions. The uncertain approach trains the model without fairness constraints but only on samples with higher uncertainty in the sensitive attribute predictions.

The experiments on five real-world datasets show that the proposed framework can significantly improve the fairness-accuracy tradeoff compared to existing methods that use proxy sensitive attributes or true sensitive attributes. The results also demonstrate the importance of the consistency loss in the attribute classifier and the impact of the uncertainty threshold on the fairness-accuracy tradeoff.

Статистика

"Demographic information can be missing for various reasons, e.g., due to legal restrictions, prohibiting the collection of sensitive information of individuals, or voluntary disclosure of such information."
"The data in this setting can be divided into two sets: D1 and D2. The dataset D1 does not contain demographic information, while D2 contains both sensitive and non-sensitive information."
"Without demographic information in D1, it is more challenging to enforce group fairness notions such as statistical parity (Dwork et al., 2012) and equalized odds (Hardt et al., 2016)."

Цитати

"Enforcing fairness constraints on samples with uncertain demographic information can negatively impact the fairness-accuracy tradeoff."
"Our experiments on five datasets showed that the proposed framework yields models with significantly better fairness-accuracy tradeoffs than classic attribute classifiers."
"Surprisingly, our framework can outperform models trained with fairness constraints on the true sensitive attributes in most benchmarks."

Ключові висновки, отримані з

Fairness Under Demographic Scarce Regime

by Patr... о arxiv.org 09-19-2024

https://arxiv.org/pdf/2307.13081.pdf

Глибші Запити

How can the proposed framework be extended to handle more than two demographic groups?

The proposed framework, FairDSR, can be extended to handle more than two demographic groups by modifying the sensitive attribute classifier to predict multiple classes instead of binary outcomes. This involves several key steps:

Multi-Class Attribute Classifier: The sensitive attribute classifier can be adapted to output probabilities for each demographic group using a softmax layer. This allows the model to predict the likelihood of each sample belonging to one of the multiple demographic groups.

Fairness Constraints for Multi-Class: The fairness constraints need to be redefined to accommodate multiple groups. Metrics such as demographic parity, equalized odds, and equal opportunity can be generalized to multi-class settings. For instance, demographic parity can be enforced by ensuring that the proportion of positive outcomes is similar across all demographic groups.

Uncertainty Estimation: The uncertainty estimation process can remain similar, but it should account for the multi-class nature of the predictions. Techniques like Monte Carlo dropout can still be employed to quantify uncertainty across multiple classes, allowing the framework to focus on samples with reliable predictions for fairness enforcement.

Training and Evaluation: The training process should incorporate a balanced representation of all demographic groups in both the training and validation datasets. Evaluation metrics should also reflect the performance across all groups, ensuring that the model does not favor any particular demographic.

Adaptation of the Fairness Mechanism: The in-processing fairness mechanisms, such as adversarial debiasing or exponentiated gradient methods, should be adapted to handle the multi-class outputs, ensuring that fairness is maintained across all demographic groups.

By implementing these modifications, the FairDSR framework can effectively address fairness in scenarios involving multiple demographic groups, enhancing its applicability in diverse real-world contexts.

What are the potential limitations of using uncertainty-based filtering in the sensitive attribute space, and how can they be addressed?

While uncertainty-based filtering in the sensitive attribute space offers several advantages, it also presents potential limitations:

Over-Reliance on Uncertainty Estimates: The effectiveness of the framework heavily depends on the accuracy of the uncertainty estimates. If the uncertainty measure is not reliable, it may lead to the exclusion of samples that could contribute positively to fairness or accuracy. To address this, it is crucial to employ robust uncertainty estimation techniques, such as Monte Carlo dropout, and to validate these estimates against known benchmarks.

Bias in Uncertainty Estimates: The model may exhibit bias in its uncertainty estimates, particularly if certain demographic groups are underrepresented in the training data. This could result in unfair treatment of these groups. To mitigate this, the training dataset should be carefully curated to ensure a balanced representation of all demographic groups, and techniques such as stratified sampling can be employed.

Threshold Sensitivity: The choice of the uncertainty threshold (H) can significantly impact the model's performance. A poorly chosen threshold may either exclude too many samples, reducing the dataset size and accuracy, or include too many uncertain samples, compromising fairness. To address this, a systematic approach to threshold selection should be implemented, potentially using cross-validation to identify the optimal threshold that balances fairness and accuracy.

Complexity in Multi-Class Scenarios: In scenarios with multiple demographic groups, uncertainty-based filtering may become more complex, as the uncertainty for each group needs to be assessed. This complexity can be managed by employing multi-class uncertainty estimation techniques and ensuring that the filtering process is adaptable to the number of classes.

By recognizing and addressing these limitations, the framework can enhance its robustness and effectiveness in promoting fairness in machine learning models.

How can the insights from this work be applied to other domains where sensitive information is partially available, such as healthcare or finance?

The insights from the FairDSR framework can be effectively applied to various domains, including healthcare and finance, where sensitive information is often partially available. Here are several ways these insights can be utilized:

Healthcare: In healthcare, patient demographic information may be incomplete due to privacy concerns or voluntary non-disclosure. The FairDSR framework can be employed to predict missing sensitive attributes, such as race or socioeconomic status, using available non-sensitive features. By applying uncertainty-based filtering, healthcare models can ensure that fairness is maintained when making predictions about treatment outcomes or resource allocation, ultimately leading to more equitable healthcare delivery.

Finance: In the finance sector, demographic information related to creditworthiness may be partially available due to regulations prohibiting the collection of sensitive data. The FairDSR framework can help financial institutions develop fair credit scoring models by inferring missing demographic attributes from non-sensitive features. By focusing on samples with reliable predictions, financial models can reduce bias in lending decisions, ensuring that individuals from historically marginalized groups are not unfairly disadvantaged.

Employment and Hiring: In recruitment processes, demographic information about candidates may be limited due to privacy policies. The insights from FairDSR can be applied to develop fair hiring algorithms that predict candidate suitability while ensuring that fairness constraints are enforced based on inferred demographic attributes. This can help organizations mitigate bias in hiring practices and promote diversity in the workplace.

Social Services: In social services, demographic data may be incomplete due to various factors, including privacy concerns. The FairDSR framework can be utilized to assess eligibility for social programs by inferring missing demographic information. By ensuring that fairness is maintained in the allocation of resources, social services can better serve underrepresented communities.

Policy Making: Policymakers can leverage the insights from this work to design fairer policies that account for demographic disparities. By using models that incorporate uncertainty-based filtering, policymakers can analyze the impact of proposed regulations on different demographic groups, ensuring that policies do not inadvertently perpetuate existing inequalities.

By applying the FairDSR framework and its insights across these domains, organizations can enhance fairness in their decision-making processes, ultimately leading to more equitable outcomes for individuals and communities.