Towards Ethical AI: A Structured Review of Responsible Development and Deployment Practices
TL;DR
This article critically examines existing ethical frameworks that address major principles such as fairness, transparency, accountability, and inclusivity by their origin–governmental, corporate, academic, and algorithmic–and compares their applicability across diverse contexts.
Abstract
Responsible Artificial Intelligence (RAI) has emerged as a critical concern in the evolving technological landscape. With the widespread integration of AI across industries and research communities, it is imperative to evaluate its ethical principles and societal implications. Numerous real-world cases highlight the urgent need for such evaluations. This necessity can be addressed through ethical consideration frameworks that serve as guidelines for AI application development. Although terms such as "Trustworthy AI" and "Responsible AI" are frequently used, their practical implications warrant deeper investigation. This article critically examines existing ethical frameworks that address major principles such as fairness, transparency, accountability, and inclusivity. It categorizes these frameworks by their origin–governmental, corporate, academic, and algorithmic–and compares their applicability across diverse contexts. Furthermore, a simplified framework tailored for academic researchers is proposed to instill ethical awareness in early-stage AI development. By synthesizing diverse perspectives and highlighting practical implementations, this paper aims to contribute meaningfully to the ongoing discourse on Responsible AI.
