On Modelling a Reliable Framework for Responsible and Ethical AI in Digitalization and Automation: Advancements and Challenges

Abstract

The rapid evolution of artificial intelligence (AI) has ushered in transformative advancements across industries, from healthcare to finance, while simultaneously raising critical ethical and societal challenges. This paper aims at comprehensively explore the latest developments and trends in creating a framework for responsible and ethical AI, emphasizing the need for Digitalization that prioritizes transparency, fairness, accountability, and human/environment safety. Key advancements, such as improved algorithmic auditing, bias mitigation techniques, and interdisciplinary collaboration, will be highlighted alongside persistent challenges, including data privacy concerns, cultural variability in ethical standards, and the risk of unintended consequences in using autonomous systems. By examining case studies and emerging best practices based on a comprehensive state-of-the-art literature review, this paper will underscore the importance of balancing innovation with oversight to ensure AI can provide improved services applied to Digitalization and Business Transformation while simultaneously minimizing all risks. Ultimately, it is herein attempted to define a suitable framework, named FRE-AIDT, for applying relevant policies towards a global, inclusive dialogue to shape a future in business transformation and digitalization where AI aligns with human values and priorities while addressing the complexities of its real-world deployment and in parallel minimizing all possible relevant risks for societies and the environment. To effectively confront these pressing challenges, worldwide initiatives have been admitted actively to establish robust frameworks that ensure AI systems are not only responsible and ethical but also fundamentally aligned with societal values and priorities. This paper delves into vital advancements in this field, based on case studies and state-of-the-art literature, defining models and emphasizing initiatives such as the EU AI Act, the USA's AI-related policies, and the IEEE's ethical AI framework, attempting to provide a comprehensive summary of these initiatives and models towards modelling the proposed FRE-AIDT framework.

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