Ethical and Scalable Automation: A Governance and Compliance Framework for Business Applications

TL;DR

This paper presents a comprehensive governance and compliance framework for ethical, controllable, and scalable AI deployment in business applications, addressing regulatory and ethical challenges with practical case studies.

Contribution

It introduces a novel framework that guides businesses in ethically and legally deploying AI, balancing performance, explainability, and compliance, validated through multiple case studies.

Findings

Framework improves transparency and compliance in AI deployment.

Case studies show effective balancing of performance and explainability.

Metrics confirm alignment between synthetic and real data distributions.

Abstract

The popularisation of applying AI in businesses poses significant challenges relating to ethical principles, governance, and legal compliance. Although businesses have embedded AI into their day-to-day processes, they lack a unified approach for mitigating its potential risks. This paper introduces a framework ensuring that AI must be ethical, controllable, viable, and desirable. Balancing these factors ensures the design of a framework that addresses its trade-offs, such as balancing performance against explainability. A successful framework provides practical advice for businesses to meet regulatory requirements in sectors such as finance and healthcare, where it is critical to comply with standards like GPDR and the EU AI Act. Different case studies validate this framework by integrating AI in both academic and practical environments. For instance, large language models are cost-effective alternatives for generating synthetic opinions that emulate attitudes to environmental issues. These case studies demonstrate how having a structured framework could enhance transparency and maintain performance levels as shown from the alignment between synthetic and expected distributions. This alignment is quantified using metrics like Chi-test scores, normalized mutual information, and Jaccard indexes. Future research should explore the framework's empirical validation in diverse industrial settings further, ensuring the model's scalability and adaptability.