insight - Machine Learning - # Uplift Modeling Fairness Evaluation

Fairness Evaluation for Uplift Modeling in the Absence of Ground Truth

Q: How can the SGT method be improved to account for noise in predictions?

To improve the SGT method's robustness against noise in predictions, several strategies can be implemented: Ensemble Models: Utilizing ensemble methods like bagging or boosting can help reduce the impact of noisy predictions by aggregating multiple models' outputs. Outlier Detection: Implementing outlier detection techniques to identify and potentially remove noisy data points before generating surrogate labels. Regularization: Incorporating regularization techniques during model training can help prevent overfitting to noisy data and improve generalization. Data Cleaning: Conducting thorough data cleaning processes to eliminate outliers, errors, or inconsistencies that may contribute to noisy predictions.

Q: How are sparse data applications affected when using SGT for evaluating fairness?

In sparse data applications, where there is a limited amount of observations or instances available, using SGT for evaluating fairness poses both challenges and advantages: Challenges: Limited Data: Sparse datasets may not provide enough samples to generate reliable surrogate ground truth labels, leading to potential biases in evaluations. Noise Sensitivity: Sparse datasets are more susceptible to noise in predictions, which can affect the accuracy of fairness evaluations based on SGT. Advantages: Proxy Labels: Despite sparse data limitations, SGT offers a valuable proxy for ground truth labels that enables enhanced fairness evaluation even with limited observations. Simplified Evaluation: The straightforward re-scoring operation of SGT allows practitioners in sparse data settings to conduct comprehensive fairness assessments without complex modeling requirements.

Q: How could the concept of SGT be extended to multi-class classification and regression scenarios?

Extending the concept of Surrogate Ground Truth (SGT) from binary classification scenarios to multi-class classification and regression involves adapting the algorithm and methodology as follows: Multi-Class Classification: Generate Surrogate Labels: Develop a mechanism similar to binary classification but tailored for multi-class outcomes by considering each class separately within an uplift modeling framework. Re-Scoring Operation: Modify the re-scoring operation in Algorithm 1 to accommodate multiple classes while maintaining consistency between treatment and control group scores. Regression Scenarios: Counterfactual Estimation: Extend counterfactual estimation techniques used in uplift modeling from binary outcomes towards continuous variables representing regression scenarios. Outcome Prediction Adjustment: Adjust prediction adjustments based on observed outcomes versus predicted values from treatment/control models for regression tasks instead of binary decisions. By customizing these aspects specific to multi-class classification and regression contexts within uplift modeling frameworks, practitioners can effectively apply the concept of SGT across diverse predictive analytics scenarios.

Core Concepts

Generating surrogate ground truth enhances fairness evaluation in uplift modeling campaigns.

Abstract

This article discusses a framework to evaluate fairness in uplift modeling campaigns without ground truth. It proposes a method to generate surrogate ground truth (SGT) to assess fairness comprehensively. The study focuses on real-world marketing campaigns and demonstrates the effectiveness of SGT in improving campaign performance and fairness evaluation.
INTRODUCTION

AI-based decision-making systems challenge fairness evaluation.
Uplift modeling identifies candidates benefiting from treatment.
PROBLEM DEFINITION

Lack of ground truth hinders algorithmic fairness evaluation.
CONTRIBUTION

Proposed SGT generation framework enhances binary fairness evaluation.
BACKGROUND

Uplift modeling predicts incremental impact for each individual.
Commonly used binary fairness metrics depend on true labels.
SURROGATE GROUND TRUTH GENERATION ALGORITHM

Two-step process: beginning and end of the campaign.
Re-scoring operation generates surrogate lift for comprehensive evaluation.
EXPERIMENTS & RESULTS

Performance comparison of different strategies using SGT at top decile.
SGT closes 44% of the gap towards Oracle on average across all campaigns.
ENHANCED BINARY FAIRNESS EVALUATION

Evaluation based on protected attributes: age, gender, income.
SGT enables more holistic view unlocking additional metrics beyond baseline approach.

Stats

arXiv:2403.12069v1 [cs.CY] 12 Feb 2024

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Key Insights Distilled From

Fairness Evaluation for Uplift Modeling in the Absence of Ground Truth

by Serdar Kadio... at arxiv.org 03-20-2024

https://arxiv.org/pdf/2403.12069.pdf

Fairness Evaluation for Uplift Modeling in the Absence of Ground Truth

Deeper Inquiries

How can the SGT method be improved to account for noise in predictions?

To improve the SGT method's robustness against noise in predictions, several strategies can be implemented:

Ensemble Models: Utilizing ensemble methods like bagging or boosting can help reduce the impact of noisy predictions by aggregating multiple models' outputs.
Outlier Detection: Implementing outlier detection techniques to identify and potentially remove noisy data points before generating surrogate labels.
Regularization: Incorporating regularization techniques during model training can help prevent overfitting to noisy data and improve generalization.
Data Cleaning: Conducting thorough data cleaning processes to eliminate outliers, errors, or inconsistencies that may contribute to noisy predictions.

How are sparse data applications affected when using SGT for evaluating fairness?

In sparse data applications, where there is a limited amount of observations or instances available, using SGT for evaluating fairness poses both challenges and advantages:

Challenges:

Limited Data: Sparse datasets may not provide enough samples to generate reliable surrogate ground truth labels, leading to potential biases in evaluations.
Noise Sensitivity: Sparse datasets are more susceptible to noise in predictions, which can affect the accuracy of fairness evaluations based on SGT.

Advantages:

Proxy Labels: Despite sparse data limitations, SGT offers a valuable proxy for ground truth labels that enables enhanced fairness evaluation even with limited observations.
Simplified Evaluation: The straightforward re-scoring operation of SGT allows practitioners in sparse data settings to conduct comprehensive fairness assessments without complex modeling requirements.

How could the concept of SGT be extended to multi-class classification and regression scenarios?

Extending the concept of Surrogate Ground Truth (SGT) from binary classification scenarios to multi-class classification and regression involves adapting the algorithm and methodology as follows:

Multi-Class Classification:

Generate Surrogate Labels: Develop a mechanism similar to binary classification but tailored for multi-class outcomes by considering each class separately within an uplift modeling framework.
Re-Scoring Operation: Modify the re-scoring operation in Algorithm 1 to accommodate multiple classes while maintaining consistency between treatment and control group scores.

Regression Scenarios:

Counterfactual Estimation: Extend counterfactual estimation techniques used in uplift modeling from binary outcomes towards continuous variables representing regression scenarios.
Outcome Prediction Adjustment: Adjust prediction adjustments based on observed outcomes versus predicted values from treatment/control models for regression tasks instead of binary decisions.

By customizing these aspects specific to multi-class classification and regression contexts within uplift modeling frameworks, practitioners can effectively apply the concept of SGT across diverse predictive analytics scenarios.

Fairness Evaluation for Uplift Modeling in the Absence of Ground Truth