mlsanityData to 7 Oct 2026

Explainable AI is Responsible AI: How Explainability Creates Trustworthy and Socially Responsible Artificial Intelligence

arXiv 2312.01555 ↗First posted 4 Dec 2023cs.AI cs.CY cs.LGcited by 2

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Abstract (arXiv)

Artificial intelligence (AI) has been clearly established as a technology with the potential to revolutionize fields from healthcare to finance - if developed and deployed responsibly. This is the topic of responsible AI, which emphasizes the need to develop trustworthy AI systems that minimize bias, protect privacy, support security, and enhance transparency and accountability. Explainable AI (XAI) has been broadly considered as a building block for responsible AI (RAI), with most of the literature considering it as a solution for improved transparency. This work proposes that XAI and responsible AI are significantly more deeply entwined. In this work, we explore state-of-the-art literature on RAI and XAI technologies. Based on our findings, we demonstrate that XAI can be utilized to ensure fairness, robustness, privacy, security, and transparency in a wide range of contexts. Our findings lead us to conclude that XAI is an essential foundation for every pillar of RAI.

Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Builds on 29 of its 160 references that are in the map. First posted on arXiv 4 Dec 2023.

Builds on

29 of 160 references.

References · 29

  1. 2016Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localizationcited by 6,690
  2. 2017A Unified Approach to Interpreting Model Predictionscited by 6,488
  3. 2016"Why Should I Trust You?": Explaining the Predictions of Any Classifiercited by 5,040
  4. 2019A Survey on Bias and Fairness in Machine Learningcited by 1,911
  5. 2019Explainable Artificial Intelligence (XAI): Concepts, Taxonomies, Opportunities and Challenges toward Responsible AIcited by 1,543
  6. 2020Transformer Interpretability Beyond Attention Visualizationcited by 620
  7. 2019Score-CAM: Score-Weighted Visual Explanations for Convolutional Neural Networkscited by 575
  8. 2021Trustworthy AI: From Principles to Practicescited by 206
  9. 2021Explainable AI (XAI): A Systematic Meta-Survey of Current Challenges and Future Opportunitiescited by 125
  10. 2017Runaway Feedback Loops in Predictive Policingcited by 97
  11. 2021Exploiting Explanations for Model Inversion Attackscited by 48
  12. 2021Interpretable Data-Based Explanations for Fairness Debuggingcited by 30
17 more
  1. 2020An Explainable 3D Residual Self-Attention Deep Neural Network FOR Joint Atrophy Localization and Alzheimer's Disease Diagnosis using Structural MRIcited by 27
  2. 2020Transparency, Auditability and eXplainability of Machine Learning Models in Credit Scoringcited by 26
  3. 2021Designing ECG Monitoring Healthcare System with Federated Transfer Learning and Explainable AIcited by 26
  4. 2021The Role of Explainability in Assuring Safety of Machine Learning in Healthcarecited by 26
  5. 2020Explainable Goal-Driven Agents and Robots -- A Comprehensive Reviewcited by 23
  6. 2020FairLens: Auditing Black-box Clinical Decision Support Systemscited by 23
  7. 2022Shap-CAM: Visual Explanations for Convolutional Neural Networks based on Shapley Valuecited by 23
  8. 2021Combat COVID-19 Infodemic Using Explainable Natural Language Processing Modelscited by 17
  9. 2020Explainable Artificial Intelligence for Manufacturing Cost Estimation and Machining Feature Visualizationcited by 15
  10. 2022An Audit of Misinformation Filter Bubbles on YouTube: Bubble Bursting and Recent Behavior Changescited by 15
  11. 2020Designing Fair AI for Managing Employees in Organizations: A Review, Critique, and Design Agendacited by 12
  12. 2022Towards Involving End-users in Interactive Human-in-the-loop AI Fairnesscited by 12
  13. 2022Greybox XAI: a Neural-Symbolic learning framework to produce interpretable predictions for image classificationcited by 11
  14. 2021Visual explanation of black-box model: Similarity Difference and Uniqueness (SIDU) methodcited by 10
  15. 2020The Criminality From Face Illusioncited by 8
  16. 2020Improving EEG Decoding via Clustering-based Multi-task Feature Learningcited by 5
  17. 2022Explainable Artificial Intelligence to Detect Image Spam Using Convolutional Neural Networkcited by 3

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2026: 2.

2 papers.

  1. 2026Trustworthy AI in Digital Health: A Comprehensive Review of Robustness and Explainabilitycited by 0
  2. 2026Vision Language Model Fusion for Explainable Face Recognitioncited by 0