Ethical Challenges and Responsible Deployment of Generative AI in Academic and Financial Domains

Modern-day institutions are largely being transformed by AI using generative technology to automate, to create, and to make decisions at large-scale. However, its use in areas of highest sensitivity - for example, academic and finance - presents complex ethical challenges that cannot be addressed through technical measures alone. This paper highlights a cross-domain comparative analysis of GenAI ethic-allusions emphasizing on algorithmic bias, data privacy protection, issue of transparency, and accountability. Referring to the interdisciplinary literature, we propose the "Ethical Risk Quantification Model (ERQM)," a mathematical model to analyze the ethical risk as a function of essential socio-technical parameters. The findings indicate that problems of authorship integrity and research transparency plague academia and bias and accountability gap escalate in finance. Integrating qualitative ethics with quantifiable regulation, Driverless AI’s ERQM provides a single tool for responsible AI governance in every industry.

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