An Expedited Examination of Responsible AI Frameworks: Directing Ethical AI Development - Shuford, Jeff (Journal of Artificial Intelligence General science (JAIGS) ISSN:3006-4023, 2024)
In recent years, the rapid expansion of Artificial Intelligence (AI) and its integration into various aspects of daily life have ignited significant discourse on the ethical considerations governing its application. This study addresses these concerns by swiftly reviewing multiple frameworks designed to guide the development and utilization of Responsible AI (RAI) applications. Through this exploration, we analyze each framework's alignment with the Software Development Life Cycle (SDLC) phases, revealing a predominant focus on the Requirements Elicitation phase, with limited coverage of other stages. Furthermore, we note a scarcity of supportive tools, predominantly offered by private entities. Our findings underscore the absence of a comprehensive framework capable of accommodating both technical and non-technical stakeholders across all SDLC phases, thus revealing a notable gap in the current landscape. This study sheds light on the imperative need for a unified framework encompassing all RAI principles and SDLC phases, accessible to users of varying expertise and objectives.