Bias Detection and Fairness in CRM-Based AI Models
DOI:
https://doi.org/10.63282/3050-9262.IJAIDSML-V7I1P142Keywords:
CRM Artificial Intelligence, Algorithmic Bias, Fairness Metrics, Responsible AI, Explainable AI, Ethical CRM, AI Governance, AI, Customer Relationship Management, AI Model Transparency, Disparate Impact, Data BiasAbstract
Customer Relationship Management (CRM) platforms increasingly rely on Artificial Intelligence (AI) models to automate decision-making across sales, service, marketing, and support operations. These models influence critical business outcomes such as lead prioritization, credit eligibility, customer retention strategies, case routing, and service prioritization. However, the growing adoption of AI in CRM systems introduces significant risks related to algorithmic bias and fairness. Bias in CRM-based AI models can lead to discriminatory outcomes, reduced customer trust, regulatory non-compliance, and reputational damage. This paper presents a comprehensive analysis of bias sources in CRM-based AI models, techniques for bias detection, fairness metrics, and mitigation strategies. It further discusses governance frameworks and platform-specific considerations for ensuring responsible and ethical AI deployment in modern CRM ecosystems.
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