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A Framework for Responsible AI Systems: Building Societal Trust through Domain Definition, Trustworthy AI Design, Auditability, Accountability, and Governance

Andrés Herrera-Poyatos, Javier Del Ser, Marcos López de Prado, Fei-Yue Wang, Enrique Herrera-Viedma, Francisco Herrera

TL;DR

This work tackles the challenge of deploying AI in high-risk domains by proposing a holistic RAIS framework that unites domain definition, trustworthy AI design, auditability, accountability, and governance into a cohesive lifecycle. It argues that trustworthy design alone is insufficient and that proactive and reactive accountability, underpinned by continuous auditing and participatory governance, is essential for societal trust. The paper details each dimension, contrasts the framework with existing approaches, and demonstrates applicability through an autonomous-vehicles scenario, while offering design insights and outlining future research directions. By aligning technical design with regulatory and institutional responsibilities, the RAIS framework provides a practical blueprint for certifiable, transparent, and ethically aligned AI systems with real-world impact.

Abstract

Responsible Artificial Intelligence (RAI) addresses the ethical and regulatory challenges of deploying AI systems in high-risk scenarios. This paper proposes a comprehensive framework for the design of an RAI system (RAIS) that integrates five key dimensions: domain definition, trustworthy AI design, auditability, accountability, and governance. Unlike prior work that treats these components in isolation, our proposal emphasizes their inter-dependencies and iterative feedback loops, enabling proactive and reactive accountability throughout the AI lifecycle. Beyond presenting the framework, we synthesize recent developments in global AI governance and analyze limitations in existing principles-based approaches, highlighting fragmentation, implementation gaps, and the need for participatory governance. The paper also identifies critical challenges and research directions for the RAIS framework, including sector-specific adaptation and operationalization, to support certification, post-deployment monitoring, and risk-based auditing. By bridging technical design and institutional responsibility, this work offers a practical blueprint for embedding responsibility throughout the AI lifecycle, enabling transparent, ethically aligned, and legally compliant AI-based systems.

A Framework for Responsible AI Systems: Building Societal Trust through Domain Definition, Trustworthy AI Design, Auditability, Accountability, and Governance

TL;DR

This work tackles the challenge of deploying AI in high-risk domains by proposing a holistic RAIS framework that unites domain definition, trustworthy AI design, auditability, accountability, and governance into a cohesive lifecycle. It argues that trustworthy design alone is insufficient and that proactive and reactive accountability, underpinned by continuous auditing and participatory governance, is essential for societal trust. The paper details each dimension, contrasts the framework with existing approaches, and demonstrates applicability through an autonomous-vehicles scenario, while offering design insights and outlining future research directions. By aligning technical design with regulatory and institutional responsibilities, the RAIS framework provides a practical blueprint for certifiable, transparent, and ethically aligned AI systems with real-world impact.

Abstract

Responsible Artificial Intelligence (RAI) addresses the ethical and regulatory challenges of deploying AI systems in high-risk scenarios. This paper proposes a comprehensive framework for the design of an RAI system (RAIS) that integrates five key dimensions: domain definition, trustworthy AI design, auditability, accountability, and governance. Unlike prior work that treats these components in isolation, our proposal emphasizes their inter-dependencies and iterative feedback loops, enabling proactive and reactive accountability throughout the AI lifecycle. Beyond presenting the framework, we synthesize recent developments in global AI governance and analyze limitations in existing principles-based approaches, highlighting fragmentation, implementation gaps, and the need for participatory governance. The paper also identifies critical challenges and research directions for the RAIS framework, including sector-specific adaptation and operationalization, to support certification, post-deployment monitoring, and risk-based auditing. By bridging technical design and institutional responsibility, this work offers a practical blueprint for embedding responsibility throughout the AI lifecycle, enabling transparent, ethically aligned, and legally compliant AI-based systems.

Paper Structure

This paper contains 31 sections, 9 figures, 2 tables.

Table of Contents

  1. Introduction
  2. Responsible AI Systems: Why and How?
  3. Why do we need Responsible AI Systems? Enabling Societal Trust in AI, Regulation and High-Risk Scenarios
  4. How has Responsible AI been approached to date?
  5. Responsible AI System Framework: The Consensus for Societal Trust in AI
  6. Responsible AI Systems Framework
  7. Comparison to other RAIS frameworks
  8. Societal Trust in AI
  9. Domain Definitions for Responsible AI Systems
  10. Operational Design Domain
  11. Data Quality
  12. Stakeholders context
  13. Trustworthy AI Design
  14. Trustworthy AI Paradigm
  15. On the Essential Role of Explanations in Responsible AI
  16. ...and 16 more sections

Figures (9)

  • Figure 1: Risk levels defined by the AI Act regulatory framework.
  • Figure 2: Proposed RAIS framework.
  • Figure 3: Domain definition and its elements in the proposed RAIS framework.
  • Figure 4: Trustworthy AI model design within the proposed framework.
  • Figure 5: Auditability & certification within the proposed RAIS framework.
  • ...and 4 more figures

Theorems & Definitions (3)

  • Definition 1
  • Definition 2
  • Definition 3