Responsible Artificial Intelligence: A Structured Literature Review

TL;DR

This paper provides a comprehensive review of responsible AI, defining its core principles, analyzing current understanding, and proposing a human-centric framework emphasizing ethics, explainability, privacy, security, and trust.

Contribution

It introduces the first unified definition of responsible AI and offers a structured literature review to guide future frameworks and regulations.

Findings

Highlights the importance of ethics, explainability, and trust in responsible AI

Proposes a human-centric approach for developing responsible AI frameworks

Identifies key focal areas for future AI regulation and development

Abstract

Our research endeavors to advance the concept of responsible artificial intelligence (AI), a topic of increasing importance within EU policy discussions. The EU has recently issued several publications emphasizing the necessity of trust in AI, underscoring the dual nature of AI as both a beneficial tool and a potential weapon. This dichotomy highlights the urgent need for international regulation. Concurrently, there is a need for frameworks that guide companies in AI development, ensuring compliance with such regulations. Our research aims to assist lawmakers and machine learning practitioners in navigating the evolving landscape of AI regulation, identifying focal areas for future attention. This paper introduces a comprehensive and, to our knowledge, the first unified definition of responsible AI. Through a structured literature review, we elucidate the current understanding of responsible AI. Drawing from this analysis, we propose an approach for developing a future framework centered around this concept. Our findings advocate for a human-centric approach to Responsible AI. This approach encompasses the implementation of AI methods with a strong emphasis on ethics, model explainability, and the pillars of privacy, security, and trust.