Trustworthy AI in Software Systems: From Explainability to Regulatory Compliance
DOI:
https://doi.org/10.63282/3050-922X.IJERET-V5I1P109Keywords:
Trustworthy AI, Explainability, AI Governance, Software Ethics, Regulatory Compliance, AI Lifecycle, Fairness, Transparency, AI in Software SystemsAbstract
Artificial Intelligence (AI) is one of the disruptive technologies of the modern software systems that has rendered the software systems automatable, including predictive analytics and intelligent decision-making. The broad DIY use of AI will, nevertheless, present ethical, legal, and government policy challenges that will need to be overcome in order to establish trustworthiness. In the present paper, the principles of trustworthy AI as they apply to explainability, fairness, accountability, robustness, and regulatory compliance are discussed. In the context of this paper, the issue of explainability frameworks (e.g., SHAP, LIME, and counterfactuals) as the means of providing human interpretability and the means of governance in balancing the use of AI and the values within the society is addressed. The methodology holds a Trustworthy AI Lifecycle model that incorporates technical, ethical, and legal requirements, and is validated in the case studies of healthcare, finance, and autonomous systems. The evidence indicates that businesses with established systems of governance are more transparent, comply with regulations, and consider the discretion of stakeholders. The findings indicate that AI governance, interpretable models, and compliance assurance mechanisms will play a crucial role in ensuring that most AI implementations become sustainable. It is concluded in this paper that interdisciplinary research is the key to the fulfillment of trustworthy AI in software-based systems; it should involve the realms of AI ethics, software engineering, and regulatory science
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