mlsanityData to 14 Oct 2026

Responsible Artificial Intelligence: A Structured Literature Review

arXiv 2403.06910 ↗First posted 11 Mar 2024cs.AI cs.CY cs.LGcited by 6

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Abstract (arXiv)

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.

Key facts. Cited by 6 papers in the map (data through 14 Oct 2026). Builds on 7 of its 263 references that are in the map. First posted on arXiv 11 Mar 2024.

Builds on

7 of 263 references.

References · 7

  1. 2020Formalizing Trust in Artificial Intelligence: Prerequisites, Causes and Goals of Human Trust in AIcited by 167
  2. 2019One Explanation Does Not Fit All: A Toolkit and Taxonomy of AI Explainability Techniquescited by 164
  3. 2020When Machine Learning Meets Privacy: A Survey and Outlookcited by 68
  4. 2020FedAT: A High-Performance and Communication-Efficient Federated Learning System with Asynchronous Tierscited by 64
  5. 2020Chasing Your Long Tails: Differentially Private Prediction in Health Care Settingscited by 37
  6. 2021FedV: Privacy-Preserving Federated Learning over Vertically Partitioned Datacited by 31
  7. 2020Beyond Near- and Long-Term: Towards a Clearer Account of Research Priorities in AI Ethics and Societycited by 7

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 5; 2026: 1.

6 papers.