Who is Responsible? The Data, Models, Users or Regulations? Responsible Generative AI for a Sustainable Future

2025-01-15

AI Summary

This paper addresses the implementation gap in ethical governance of generative AI (Gen AI) in the post-ChatGPT era. Methodologically, it introduces the first end-to-end Responsible Gen AI (RAI) practice framework—spanning governance, technology, evaluation, and deployment—integrating philosophical responsibility theory, eXplainable AI (XAI), benchmark alignment, cross-sector application modeling, and KPI-based quantitative assessment. It pioneers an AI-readiness-oriented testbed evaluation methodology and establishes a comprehensive RAI Key Performance Indicator (KPI) system. Contributions include: (1) systematically bridging the chasm between normative ethical principles and engineering practice while redefining accountability structures; and (2) releasing an open-source resource repository—including standards, tools, and benchmark datasets—to provide researchers, policymakers, and industry practitioners with scalable, reusable, and trustworthy implementation guidance.

Technology Category

• Philosophy and Ethics of AI: Artificial General Intelligence
• Natural Language Processing: Ethics — Bias, Fairness, Transparency & Privacy
• Humans and AI: AI for Accessibility

Application Category

• Responsible Web: Fairness, accountability, transparency and ethics of web technologies
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Social Networks and Social Media: Generative AI / large language models and their impact on social systems

Abstract

Responsible Artificial Intelligence (RAI) has emerged as a crucial framework for addressing ethical concerns in the development and deployment of Artificial Intelligence (AI) systems. A significant body of literature exists, primarily focusing on either RAI guidelines and principles or the technical aspects of RAI, largely within the realm of traditional AI. However, a notable gap persists in bridging theoretical frameworks with practical implementations in real-world settings, as well as transitioning from RAI to Responsible Generative AI (Gen AI). To bridge this gap, we present this article, which examines the challenges and opportunities in implementing ethical, transparent, and accountable AI systems in the post-ChatGPT era, an era significantly shaped by Gen AI. Our analysis includes governance and technical frameworks, the exploration of explainable AI as the backbone to achieve RAI, key performance indicators in RAI, alignment of Gen AI benchmarks with governance frameworks, reviews of AI-ready test beds, and RAI applications across multiple sectors. Additionally, we discuss challenges in RAI implementation and provide a philosophical perspective on the future of RAI. This comprehensive article aims to offer an overview of RAI, providing valuable insights for researchers, policymakers, users, and industry practitioners to develop and deploy AI systems that benefit individuals and society while minimizing potential risks and societal impacts. A curated list of resources and datasets covered in this survey is available on GitHub {https://github.com/anas-zafar/Responsible-AI}.

Problem

• Bridging theoretical RAI with practical implementations.
• Transitioning from Responsible AI to Responsible Generative AI.
• Implementing ethical, transparent, and accountable AI systems.

Innovation

• Bridge RAI to Gen AI
• Explainable AI as backbone
• Align Gen AI with governance