# Responsible Artificial Intelligence: A Structured Literature Review

**Authors:** Sabrina Goellner, Marina Tropmann-Frick, Bostjan Brumen  
**arXiv:** [2403.06910](https://arxiv.org/abs/2403.06910)  
**Date:** 2024-03-11  
**Summary page:** https://www.summarizepaper.com/en/paper/2403.06910v1/

## Key points

- Research aims to advance responsible artificial intelligence (AI) within EU policy discussions
- Importance of trust in AI emphasized by the European Union, recognizing its dual nature as a beneficial tool and potential weapon
- Urgent need for international regulation to ensure ethical AI development and compliance with guidelines
- Introduction of a comprehensive definition of responsible AI to aid lawmakers and machine learning practitioners
- Advocacy for a human-centric approach to responsible AI, focusing on ethics, model explainability, privacy, security, and trust as essential pillars
- Categorization of research papers based on features related to Trustworthy AI, Ethical AI, Explainable AI, Privacy-preserving AI, and Secure AI
- Emphasis on Privacy-Preserving and Secure AI in research papers with significant focus on Federated Learning as an emerging field
- Diverse topics covered within Ethical AI studies ranging from ethical issues to frameworks integrating ethical considerations into development processes
- Proposals for new approaches within Explainable AI (XAI) to enhance model explainability and stakeholder analyses
- Review of current state of trustworthiness in Trustworthy AI studies or presentation of frameworks designed to enhance trust in AI systems

## Summary

Our research aims to advance the concept of responsible artificial intelligence (AI) within the context of EU policy discussions. The European Union has recently emphasized the importance of trust in AI, recognizing its potential both as a beneficial tool and a potential weapon. This dual nature underscores the urgent need for international regulation to ensure ethical AI development and compliance with established guidelines. To aid lawmakers and machine learning practitioners in navigating the evolving landscape of AI regulation, our study introduces a comprehensive definition of responsible AI. Through a structured literature review, we explore current understanding and propose an approach for developing a future framework centered around this concept. Our findings advocate for a human-centric approach to responsible AI, emphasizing ethics, model explainability, privacy, security, and trust as essential pillars. In conducting quantitative analysis on 254 high-quality studies, we categorized research papers based on features related to Trustworthy AI, Ethical AI, Explainable AI, Privacy-preserving AI, and Secure AI. Most papers focused on Privacy-Preserving and Secure AI with a significant emphasis on Federated Learning as an emerging research field. In the realm of Ethical AI studies covered diverse topics ranging from ethical issues to frameworks integrating ethical considerations into development processes. Within Explainable AI (XAI), researchers proposed new approaches to enhance model explainability and stakeholder analyses. Trustworthy AI studies primarily reviewed the current state of trustworthiness in research or presented frameworks specifically designed to enhance trust in AI systems. Overall our research contributes by providing a concise definition of Responsible AI and analyzing the state-of-the-art developments in this critical field. We formulated research questions aimed at understanding general definitions and key components of Responsible AI. By searching databases such as ACM Digital Library IEEE Explore SpringerLink and Elsevier ScienceDirect for peer-reviewed publications related to Artificial Intelligence ethics explainability trustworthiness and privacy preservation; we aimed to gather comprehensive insights into responsible AI practices for guiding future regulatory frameworks effectively.