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October 2, 2025AnalyticsOpen Access

Fairness in Predictive Marketing: Auditing and Mitigating Demographic Bias in Machine Learning for Customer Targeting

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Authors

SPSayee Phaneendhar PasupuletiJKJagadeesh KolaSKSai Phaneendra Manikantesh Kodete

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Overview

Fairness audit improves predictive marketing in customer targeting, indicating effective bias mitigation strategies.

Key Points

  • Significant disparities in model behavior that can lead to discriminatory targeting were uncovered.
  • Reweighing improved the Disparate Impact Ratio for older individuals from 0.65 to 0.82, enhancing model fairness.
  • Logistic regression and random forest classifiers were trained on the Bank Marketing dataset to predict customer behavior.
  • Implementing fairness auditing in business intelligence systems can enhance ethical AI practices in marketing technologies.

Cite This Study

Pasupuleti et al. (2025) studied this question.

synapsesocial.com/papers/68de68ea83cbc991d0a21451https://doi.org/10.3390/analytics4040026
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