Ethical and Explainable AI Frameworks for Responsible Business Analytics in the U.S. Economy

Abstract

The rising adoption of artificial intelligence (AI) in business analytics has changed the manner in which organizations are analyzing the massive data on consumers and making business decisions. Automated analytics in the U.S. financial services industry are becoming more important to regulatory bodies and financial institutions as a tool to detect risk, rank consumer complaints, and track institutional behavior. This application of AI, with the stakes so high, contains such a lively ethical question as the absence of transparency, a weak accountability level, and the possibility of harming consumers due to a non-transparent or biased decision-making process. This study attempts to solve these problems by suggesting an ethical and elucidable AI framework of responsible business analytics based on consumer complaint data on the Consumer Financial Protection is provided under this section.Consumer Financial Protection comes in this section. Bureau Consumer Complaint Database. The authors of the research investigate those mortgage debt consumer complaints that were posted in 2019-2022 and use natural language processing to categorize the narrative of complaints into meaningful issue categories. To manage responsible AI usage, the framework proposed will combine interpretable machine learning models with post-hoc explain ability frameworks that will justify the automated decisions by humans in a manner that is understandable. Explainable AI methods are applied to point out important textual characteristics that determine the classification of a complaint to allow transparency and auditing to business stakeholders and the regulators. Besides model performance evaluation, the study also considers ethical aspects of transparency, accountability, and fairness of automated complaint analytics. The framework is evaluated based on various criteria of evaluation, such as classification accuracy, explanation fidelity, stability and human interpretability. This study will fill the gap between theoretical ethical ideals and realistic business practices because it shows how explainable AI can be integrated into the framework of a regulatory-oriented analytics pipeline. The results provide contributions to the academic and industry practice through the provision of a reusable framework that can support the use of AI in a regulated business setting by instilling trust. This study demonstrates that explainable and ethical AI systems are capable of increasing regulatory trust, organizational accountability, and responsible decision-making in the U.S. economy without affecting analytical effectiveness.