Bias Detection and Mitigation in AI-Driven Target Marketing: Exploring Fairness in Automated Consumer Profiling


Authors : Vishvesh Soni

Volume/Issue : Volume 9 - 2024, Issue 5 - May

Google Scholar : https://tinyurl.com/yfhm5aes

Scribd : https://tinyurl.com/4nerkktu

DOI : https://doi.org/10.38124/ijisrt/IJISRT24MAY2203

Abstract : In this work, bias identification and mitigation in AI-driven target marketing are examined with an emphasis on guaranteeing fairness in automated consumer profiling. Significant biases in AI models were found by preliminary investigation, especially impacted by characteristics like purchasing history and geographic location, which closely correspond with protected characteristics like race and socioeconomic position. With a Disparate Impact (DI) of 0.60, a Statistical Parity Difference (SPD) of -0.25, and an Equal Opportunity Difference (EOD) of -0.30, the fairness measures computed for the original models revealed significant biases against certain population groups. We used three main mitigating strategies: pre-processing, in-processing, and post-processing, to counteract these biases. Re- sampling and balancing of training data as part of pre- processing raised the DI to 0.85, SPD to -0.10, and EOD to -0.15. The measures were much better with in- processing, which adds fairness restrictions straight into the learning algorithms, with a DI of 0.90, an SPD of -0.05, and an EOD of -0.10. The most successful were post- processing modifications, which changed model outputs to guarantee fairness; they produced a DI of 0.95, an SPD of -0.02, and an EOD of -0.05. These results support the research already in publication and demonstrate that bias in AI is a complicated and enduring problem that calls for a multidimensional strategy. The paper highlights how crucial ongoing audits, openness, and multidisciplinary cooperation are to reducing prejudice. Marketers, AI practitioners, and legislators will find the ramifications profound, which emphasizes the requirement of moral AI methods to preserve customer confidence and follow laws. This approach advances the larger discussion on AI ethics, promotes justice, and reduces prejudice in AI- driven marketing systems.

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In this work, bias identification and mitigation in AI-driven target marketing are examined with an emphasis on guaranteeing fairness in automated consumer profiling. Significant biases in AI models were found by preliminary investigation, especially impacted by characteristics like purchasing history and geographic location, which closely correspond with protected characteristics like race and socioeconomic position. With a Disparate Impact (DI) of 0.60, a Statistical Parity Difference (SPD) of -0.25, and an Equal Opportunity Difference (EOD) of -0.30, the fairness measures computed for the original models revealed significant biases against certain population groups. We used three main mitigating strategies: pre-processing, in-processing, and post-processing, to counteract these biases. Re- sampling and balancing of training data as part of pre- processing raised the DI to 0.85, SPD to -0.10, and EOD to -0.15. The measures were much better with in- processing, which adds fairness restrictions straight into the learning algorithms, with a DI of 0.90, an SPD of -0.05, and an EOD of -0.10. The most successful were post- processing modifications, which changed model outputs to guarantee fairness; they produced a DI of 0.95, an SPD of -0.02, and an EOD of -0.05. These results support the research already in publication and demonstrate that bias in AI is a complicated and enduring problem that calls for a multidimensional strategy. The paper highlights how crucial ongoing audits, openness, and multidisciplinary cooperation are to reducing prejudice. Marketers, AI practitioners, and legislators will find the ramifications profound, which emphasizes the requirement of moral AI methods to preserve customer confidence and follow laws. This approach advances the larger discussion on AI ethics, promotes justice, and reduces prejudice in AI- driven marketing systems.

Video Explanation for Published paper

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