AI Based Model for Prediction of Responsible AI Score and Responsible AI Maturity of Organisations

Abstract

Artificial Intelligence (AI) has evolved from a largely experimental and theoretical field into a powerful and pervasive technology that now forms the backbone of modern digital ecosystems. Over the past decade, rapid advancements in computational capabilities, the explosion of large-scale data, and significant progress in machine learning and deep learning have accelerated the integration of AI across diverse sectors, including healthcare, finance, education, governance, transportation, and manufacturing (Floridi et al., 2018; Crawford, 2021). Today, AI systems are capable of performing increasingly complex tasks such as natural language processing, image and speech recognition, predictive analytics, and even autonomous decision-making. While these advancements have undeniably improved efficiency, productivity, and innovation, they have also introduced a range of ethical, legal, and societal concerns that demand careful and critical examination (Mittelstadt et al., 2016).

As AI systems become more deeply embedded in high-stakes decision-making contexts, concerns surrounding bias, discrimination, lack of transparency, privacy violations, and accountability have become more pronounced (Mehrabi et al., 2021). Real-world instances—such as biased recruitment algorithms, inequitable credit scoring systems, and opaque judicial decision-support tools—illustrate how poorly governed AI systems can reinforce existing inequalities and create unintended consequences. Importantly, these challenges are not solely technical in nature; they are influenced by broader social, cultural, and institutional dynamics, making them inherently complex and multifaceted. In response, there has been a growing global emphasis on Responsible Artificial Intelligence (RAI), which seeks to ensure that AI systems are developed and deployed in ways that are ethical, fair, transparent, and aligned with human values (European Commission, 2019; OECD, 2019).

Responsible AI is underpinned by key principles such as fairness, accountability, transparency, privacy, robustness, and inclusiveness. These principles have been widely articulated by international organizations, academic communities, and leading technology companies (Jobin et al., 2019). However, despite a broad consensus on these foundational ideas, their practical implementation remains a significant challenge. Many organizations struggle to translate high-level ethical guidelines into actionable practices within existing AI development workflows, resulting in fragmented and inconsistent adoption of responsible AI practices (Rakova et al., 2021; Schiff et al., 2020).

Artificial Intelligence (AI) has become a cornerstone of technological advancement, revolutionizing industries such as healthcare, finance, transportation, and more. According to Statista, the global revenue from the Artificial Intelligence (AI) software market is projected to reach $126 billion by 2025. Gartner reports that 37% of organizations have implemented AI in some form, indicating not only a growing acceptance of AI technology but also an increasing integration of AI skills into business processes. Over the past four years, the percentage of enterprises employing AI has surged by 270%. Servion Global Solutions predicts that by 2025, 95% of customer interactions will be powered by AI. Additionally, a 2020 report from Statista reveals that the global AI software market is expected to grow by approximately 54% year-on-year, reaching a forecast size of $22.6 billion.

The proliferation of AI tools and applications underscores the critical need for structured development practices that ensure their reliability, scalability, and ethical integrity. As AI systems increasingly influence decision-making processes and operational workflows, the importance of adhering to best practices throughout the project life cycle cannot be overstated.

The project life cycle of AI tools encompasses several stages, from the initial problem identification and requirement analysis to data collection and preparation, model development and training, model evaluation and validation, deployment and integration, and ongoing maintenance and monitoring. Each stage presents unique challenges and opportunities, requiring meticulous planning, execution, and evaluation to achieve desired outcomes.

Despite the growing body of literature on AI development, there remains a lack of comprehensive reviews that systematically analyze best practices across the entire project life cycle. Existing studies often focus on specific aspects, such as model training techniques or data management strategies, without providing an integrated view of the end-to-end process. This gap in the literature highlights the need for a systematic review that consolidates knowledge from diverse sources and offers a holistic perspective on AI development best practices.

This study aims to fill this gap by conducting a systematic review of the project life cycle of AI tools, with a focus on identifying and analyzing the best development practices followed across different stages. By synthesizing findings from scholarly articles, case studies, and industry reports, this review seeks to provide valuable insights and practical guidelines for AI practitioners and researchers. The goal is to facilitate the optimization of AI development processes, promoting the creation of robust, scalable, and ethically sound AI solutions.

The remainder of this paper is structured as follows: the methodology section outlines the approach taken to conduct the systematic review, including the search strategy, inclusion and exclusion criteria, and data extraction methods. The literature review section provides an overview of the AI project life cycle stages and summarizes the best practices identified in each stage. The results section presents the synthesized findings, highlighting common themes and practices. The discussion section explores the implications of these findings, addresses common challenges and limitations, and suggests directions for future research. Finally, the conclusion summarizes the key insights and offers recommendations for AI development.