Algorithmic bias in machine learning systems is a major concern when predictive models are used in highstakes settings, including income prediction, hiring, healthcare, and resource allocation. This paper presents an empirical study of gender bias in six classification models Logistic Regression, Random Forest, Decisio...
Shreyanshi Gupta, Sangini Singh, Hansika Abhiraj et al.· International Conference on...· 0 citations
Machine unlearning addresses a critical challenge in modern AI based financial systems by enabling the removal of user data while maintaining model accuracy and efficiency without retraining the model from scratch. In applications such as credit card default prediction, models are trained on sensitive financial data, c...
Vikramaditya Roy, T. R. M. Aniketh, Aayush M. Pai et al.· International Conference on...· 0 citations
Customer retention continues to be a significant issue for contemporary firms because of increasing competition and evolving customer dynamics. Existing churn prediction models tend to concentrate on detecting churners but lack mechanisms for formulating adaptive decisions for retention purposes. In this study, we prop...
Abhay Pratap Singh, Prakyath S Kumar, Advait Gujar et al.· International Conference Com...· 0 citations
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