Trust, Transparency, and Ethics: A Framework for Sustainable LLM Integration in Enterprise Information Systems

Abstract

Large Language Models are an evolutionary power in business information systems that fundamentally change the way companies process information, streamline workflows, and enable strategic decision-making. The deployment of these advanced transformer-based architectures into mission-critical business contexts brings unprecedented powers while also generating difficult-to-manage challenges around trust, transparency, and ethics. Trust deficits arise due to the probabilistic nature of model outputs, such as hallucinations, domain-specific constraints, and intrinsic biases against stakeholders' confidence. Transparency imperatives are triggered by the textualizing frameworks that demand explainability of automated decisions, which is contrary to the architecture of deep learning that manifests its transparency in environments marked by opaqueness. Fairness, accountability, data privacy, and workforce change are the ethical issues that require high-level governance structures to balance innovation and responsible deployment.  This paradigm accommodates these interrelated aspects by structured observation of technical processes, organizational forces, and social implications. Adoption environment shows industry-specific trends of adoption in terms of regulatory restraints, organizational maturity, and environment. An end-to-end implementation would require explainable AI methods, mitigation, and bias-detection methods, human-in-the-loop topologies, and real-time monitoring networks.  By understanding the sociotechnical nature of LLM integration, businesses can walk the complex line between technological capacity and ethical responsibility and eventually achieve sustainable use within organizations in line with both the business objectives and values of society.