# MAISTRO: Towards an Agile Methodology for AI System Development Projects
## 2 Related Works
In addition, the implementation of ethical guidelines, such as those proposed by IEEE and the European Union, has been advocated as an essential step to ensure that AI systems are fair and accountable, including fundamental right to the protection of personal data and its safeguard [15]. However, the practical application of these guidelines still faces challenges, particularly in the balance between transparency and protection of intellectual property or data privacy [16,17]. MAISTRO differentiates itself by integrating ethical guidelines from the beginning of the project, using tools to continuously monitor and evaluate the ethical alignment of the proposed solutions. This includes incorporating principles of transparency and clear explanation, as suggested by [PERSON] et al. [18], ensuring that decisions made by AI models are understandable and justifiable to all stakeholders. Despite significant advances in AI project management methodologies, several limitations persist. As pointed out by [PERSON] et al. [19], many traditional methodologies fail to keep up with the speed of technological change and business requirements. Furthermore,the difficulty of integrating ethical and transparency considerations into all phases of the project remains a significant barrier. The recently introduced ISO 42001:2023 [20] standard, despite not being considered a methodology, aims to guide organizations in the governance and management of Artificial Intelligence initiatives. Although it provides a fundamental framework for AI strategy, risk assessment and compliance, identification and mitigation of algorithmic biases, protection of privacy and ensuring fairness in AI systems, the ISO 42001:2023 standard does not comprehensively address certain aspects of iterative and multidisciplinary project management addressed by MAISTRO methodology and the importance of strategic alignment for value generation. Specifically, it does not offer detailed guidance on iterative development cycles, continuous stakeholder engagement, and ethical checkpoints built into each project phase-elements that are crucial in AI systems that rapidly evolve with changing data. On the other hand, MAISTRO places significant emphasis on agile sprints, transparent workflows, value generation and continuous ethical oversight, thus complementing and expanding what ISO 42001:2023 [20] currently covers in the domain of AI project governance. As reported above, although there are frameworks that address technical and management aspects, few offer clear guidance on how to incorporate these ethical principles in a practical and effective way [21]. The MAISTRO methodology was developed to address these limitations, offering a more flexible and adaptable approach that not only supports the complexity of AI projects, but also ensures that solutions are ethically responsible and transparent. Based on the considerations of the authors' research above, the evaluation items in Table 1 were highlighted. We can see that the main methodologies currently used in the market for developing AI systems present gaps that must be covered so that projects can be successful in accordance with contemporary demands. \begin{table}
\begin{tabular}{|p{113.8 pt}|p{113.8 pt}|p{113.8 pt}|p{113.8 pt}|} \hline  & **CRISP-DM** & **OSEMN** & **SEMMA** & **KDD** \\ \hline Adherence to Business Strategic & & & \\ \hline Pipeline Data Flow Application & & & \\ \hline Risk and Uncertainty Management & & & \\ \hline Legal and Ethical Concerns & & & \\ \hline Organizational and Cultural Considerations & & & \\ \hline Multidisciplinary Team Collaboration & & & \\ \hline Social and Environmental Impact & & & \\ \hline Agile Project Management Approach & & & \\ \hline Updated with Emerging Technology & & & \\ \hline Better User Experience & & & \\ \hline Continuous Improvement & & & \\ \hline \end{tabular}
\end{table} 
Table 1: Main gaps in current AI methodologies.