mlsanityData to 7 Oct 2026

Trustworthy AI: A Computational Perspective

arXiv 2107.06641 ↗First posted 12 Jul 2021cs.AIcited by 93

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Abstract (arXiv)

In the past few decades, artificial intelligence (AI) technology has experienced swift developments, changing everyone's daily life and profoundly altering the course of human society. The intention of developing AI is to benefit humans, by reducing human labor, bringing everyday convenience to human lives, and promoting social good. However, recent research and AI applications show that AI can cause unintentional harm to humans, such as making unreliable decisions in safety-critical scenarios or undermining fairness by inadvertently discriminating against one group. Thus, trustworthy AI has attracted immense attention recently, which requires careful consideration to avoid the adverse effects that AI may bring to humans, so that humans can fully trust and live in harmony with AI technologies. Recent years have witnessed a tremendous amount of research on trustworthy AI. In this survey, we present a comprehensive survey of trustworthy AI from a computational perspective, to help readers understand the latest technologies for achieving trustworthy AI. Trustworthy AI is a large and complex area, involving various dimensions. In this work, we focus on six of the most crucial dimensions in achieving trustworthy AI: (i) Safety & Robustness, (ii) Non-discrimination & Fairness, (iii) Explainability, (iv) Privacy, (v) Accountability & Auditability, and (vi) Environmental Well-Being. For each dimension, we review the recent related technologies according to a taxonomy and summarize their applications in real-world systems. We also discuss the accordant and conflicting interactions among different dimensions and discuss potential aspects for trustworthy AI to investigate in the future.

Key facts. Cited by 93 papers in the map (data through 7 Oct 2026). Builds on 231 of its 390 references that are in the map. First posted on arXiv 12 Jul 2021.

Builds on

231 of 390 references.

References · 231

  1. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,204
  2. 2016Semi-Supervised Classification with Graph Convolutional Networkscited by 11,282
  3. 2015Distilling the Knowledge in a Neural Networkcited by 10,076
  4. 2014Explaining and Harnessing Adversarial Examplescited by 7,287
  5. 2016Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localizationcited by 6,690
  6. 2017A Unified Approach to Interpreting Model Predictionscited by 6,488
  7. 2017Towards Deep Learning Models Resistant to Adversarial Attackscited by 6,326
  8. 2013Intriguing properties of neural networkscited by 5,114
  9. 2016"Why Should I Trust You?": Explaining the Predictions of Any Classifiercited by 5,040
  10. 2019Advances and Open Problems in Federated Learningcited by 3,021
  11. 2016Towards Evaluating the Robustness of Neural Networkscited by 2,976
  12. 2018Federated Optimization in Heterogeneous Networkscited by 2,971
38 more
  1. 2019ALBERT: A Lite BERT for Self-supervised Learning of Language Representationscited by 2,772
  2. 2016Deep Learning with Differential Privacycited by 2,682
  3. 2015Learning Deep Features for Discriminative Localizationcited by 2,430
  4. 2013Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Mapscited by 2,348
  5. 2015Deep Compression: Compressing Deep Neural Networks with Pruning, Trained Quantization and Huffman Codingcited by 2,291
  6. 2016Membership Inference Attacks against Machine Learning Modelscited by 2,221
  7. 2020Extracting Training Data from Large Language Modelscited by 2,144
  8. 2016Equality of Opportunity in Supervised Learningcited by 2,000
  9. 2020Shortcut Learning in Deep Neural Networkscited by 1,928
  10. 2019A Survey on Bias and Fairness in Machine Learningcited by 1,911
  11. 2014FitNets: Hints for Thin Deep Netscited by 1,619
  12. 2019Explainable Artificial Intelligence (XAI): Concepts, Taxonomies, Opportunities and Challenges toward Responsible AIcited by 1,543
  13. 2019Theoretically Principled Trade-off between Robustness and Accuracycited by 1,510
  14. 2020Reliable evaluation of adversarial robustness with an ensemble of diverse parameter-free attackscited by 1,442
  15. 2017Towards A Rigorous Science of Interpretable Machine Learningcited by 1,400
  16. 2019Certified Adversarial Robustness via Randomized Smoothingcited by 1,324
  17. 2016Man is to Computer Programmer as Woman is to Homemaker? Debiasing Word Embeddingscited by 1,304
  18. 2019Energy and Policy Considerations for Deep Learning in NLPcited by 1,242
  19. 2020RealToxicityPrompts: Evaluating Neural Toxic Degeneration in Language Modelscited by 1,235
  20. 2019On the Convergence of FedAvg on Non-IID Datacited by 1,220
  21. 2017Explanation in Artificial Intelligence: Insights from the Social Sciencescited by 1,201
  22. 2016Group Equivariant Convolutional Networkscited by 1,154
  23. 2017Counterfactual Explanations without Opening the Black Box: Automated Decisions and the GDPRcited by 1,086
  24. 2017Targeted Backdoor Attacks on Deep Learning Systems Using Data Poisoningcited by 1,012
  25. 2019Deep Leakage from Gradientscited by 1,004
  26. 2018A Survey Of Methods For Explaining Black Box Modelscited by 970
  27. 2020Language (Technology) is Power: A Critical Survey of "Bias" in NLPcited by 951
  28. 2018Robustness May Be at Odds with Accuracycited by 873
  29. 2018The Secret Sharer: Evaluating and Testing Unintended Memorization in Neural Networkscited by 847
  30. 2019Are Sixteen Heads Really Better than One?cited by 803
  31. 2017Counterfactual Fairnesscited by 802
  32. 2018LEAF: A Benchmark for Federated Settingscited by 780
  33. 2016Universal adversarial perturbationscited by 774
  34. 2016Sequence-Level Knowledge Distillationcited by 761
  35. 2019GNNExplainer: Generating Explanations for Graph Neural Networkscited by 735
  36. 2020Fast is better than free: Revisiting adversarial trainingcited by 723
  37. 2014Certifying and removing disparate impactcited by 715
  38. 2019Graph Neural Networks for Social Recommendationcited by 711

50 most cited of 231 All 231 (JSON)

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 3; 2022: 17; 2023: 24; 2024: 26; 2025: 17; 2026: 6.

93 papers.

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  1. 2024A Survey on RAG Meeting LLMs: Towards Retrieval-Augmented Large Language Modelscited by 450
  2. 2021Trustworthy AI: From Principles to Practicescited by 206
  3. 2023Recommender Systems in the Era of Large Language Models (LLMs)cited by 188
  4. 2022A Comprehensive Survey on Trustworthy Graph Neural Networks: Privacy, Robustness, Fairness, and Explainabilitycited by 118
  5. 2022Trustworthy Graph Neural Networks: Aspects, Methods and Trendscited by 88
  6. 2025A Survey of WebAgents: Towards Next-Generation AI Agents for Web Automation with Large Foundation Modelscited by 87
  7. 2023Generative Diffusion Models on Graphs: Methods and Applicationscited by 79
  8. 2024When LLMs Meet Cybersecurity: A Systematic Literature Reviewcited by 70
  9. 2023How Robust is GPT-3.5 to Predecessors? A Comprehensive Study on Language Understanding Taskscited by 58
  10. 2021Graph Trend Filtering Networks for Recommendationscited by 53
  11. 2024A Survey of Mambacited by 39
  12. 2024Graph Machine Learning in the Era of Large Language Models (LLMs)cited by 33
38 more
  1. 2022A Comprehensive Survey on Trustworthy Recommender Systemscited by 32
  2. 2023Fairness and Bias in Algorithmic Hiring: a Multidisciplinary Surveycited by 30
  3. 2022Disentangled Contrastive Learning for Social Recommendationcited by 27
  4. 2022Knowledge-enhanced Black-box Attacks for Recommendationscited by 25
  5. 2023Uncovering and Quantifying Social Biases in Code Generationcited by 25
  6. 2023A Survey of Trustworthy Federated Learning with Perspectives on Security, Robustness, and Privacycited by 24
  7. 2023Fair Attribute Completion on Graph with Missing Attributescited by 22
  8. 2024The Better Angels of Machine Personality: How Personality Relates to LLM Safetycited by 21
  9. 2022The privacy issue of counterfactual explanations: explanation linkage attackscited by 20
  10. 2021Jointly Attacking Graph Neural Network and its Explanationscited by 18
  11. 2022Trustworthy Federated Learning via Blockchaincited by 15
  12. 2023A Survey on Fairness-aware Recommender Systemscited by 14
  13. 2022A Review of Speech-centric Trustworthy Machine Learning: Privacy, Safety, and Fairnesscited by 13
  14. 2023AI-Generated Images as Data Source: The Dawn of Synthetic Eracited by 13
  15. 2024The METRIC-framework for assessing data quality for trustworthy AI in medicine: a systematic reviewcited by 13
  16. 2023Optimizing Privacy, Utility and Efficiency in Constrained Multi-Objective Federated Learningcited by 12
  17. 2024Safety in Graph Machine Learning: Threats and Safeguardscited by 11
  18. 2025FakeScope: Large Multimodal Expert Model for Transparent AI-Generated Image Forensicscited by 11
  19. 2024Towards More Trustworthy and Interpretable LLMs for Code through Syntax-Grounded Explanationscited by 10
  20. 2022Understanding the Impact of Adversarial Robustness on Accuracy Disparitycited by 9
  21. 2024Selective, Interpretable, and Motion Consistent Privacy Attribute Obfuscation for Action Recognitioncited by 8
  22. 2024Machine Learning Robustness: A Primercited by 8
  23. 2022Towards trustworthy Energy Disaggregation: A review of challenges, methods and perspectives for Non-Intrusive Load Monitoringcited by 7
  24. 2023A Survey on Trustworthy Edge Intelligence: From Security and Reliability To Transparency and Sustainabilitycited by 7
  25. 2024AFSPP: Agent Framework for Shaping Preference and Personality with Large Language Modelscited by 7
  26. 2024A Survey of Explainable Knowledge Tracingcited by 7
  27. 2022Combating Health Misinformation in Social Media: Characterization, Detection, Intervention, and Open Issuescited by 6
  28. 2023The Ethics of AI in Gamescited by 6
  29. 2024The Devil is in the Neurons: Interpreting and Mitigating Social Biases in Pre-trained Language Modelscited by 6
  30. 2024Trustworthy Text-to-Image Diffusion Models: A Timely and Focused Surveycited by 6
  31. 2024Out-Of-Distribution Detection with Diversification (Provably)cited by 6
  32. 2025Understanding the Anchoring Effect of LLM with Synthetic Data: Existence, Mechanism, and Potential Mitigationscited by 6
  33. 2022Trustworthy Anomaly Detection: A Surveycited by 5
  34. 2022Data Smells in Public Datasetscited by 5
  35. 2023FederatedTrust: A Solution for Trustworthy Federated Learningcited by 5
  36. 2023Measuring Sentiment Bias in Machine Translationcited by 5
  37. 2024Transformers-based architectures for stroke segmentation: A reviewcited by 5
  38. 2022A Survey on Uncertainty Toolkits for Deep Learningcited by 4

50 most cited of 93 All 93 (JSON)