mlsanityData to 7 Oct 2026

Socially Responsible AI Algorithms: Issues, Purposes, and Challenges

arXiv 2101.02032 ↗First posted 1 Jan 2021cs.CY cs.AIcited by 56

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Abstract (arXiv)

In the current era, people and society have grown increasingly reliant on artificial intelligence (AI) technologies. AI has the potential to drive us towards a future in which all of humanity flourishes. It also comes with substantial risks for oppression and calamity. Discussions about whether we should (re)trust AI have repeatedly emerged in recent years and in many quarters, including industry, academia, healthcare, services, and so on. Technologists and AI researchers have a responsibility to develop trustworthy AI systems. They have responded with great effort to design more responsible AI algorithms. However, existing technical solutions are narrow in scope and have been primarily directed towards algorithms for scoring or classification tasks, with an emphasis on fairness and unwanted bias. To build long-lasting trust between AI and human beings, we argue that the key is to think beyond algorithmic fairness and connect major aspects of AI that potentially cause AI's indifferent behavior. In this survey, we provide a systematic framework of Socially Responsible AI Algorithms that aims to examine the subjects of AI indifference and the need for socially responsible AI algorithms, define the objectives, and introduce the means by which we may achieve these objectives. We further discuss how to leverage this framework to improve societal well-being through protection, information, and prevention/mitigation.

Key facts. Cited by 56 papers in the map (data through 7 Oct 2026). Builds on 79 of its 234 references that are in the map. First posted on arXiv 1 Jan 2021.

Builds on

79 of 234 references.

References · 79

  1. 2017A Unified Approach to Interpreting Model Predictionscited by 6,488
  2. 2016"Why Should I Trust You?": Explaining the Predictions of Any Classifiercited by 5,040
  3. 2013Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Mapscited by 2,348
  4. 2016Membership Inference Attacks against Machine Learning Modelscited by 2,221
  5. 2016Equality of Opportunity in Supervised Learningcited by 2,000
  6. 2019A Survey on Bias and Fairness in Machine Learningcited by 1,911
  7. 2019Invariant Risk Minimizationcited by 1,832
  8. 2017Understanding Black-box Predictions via Influence Functionscited by 1,714
  9. 2017Counterfactual Explanations without Opening the Black Box: Automated Decisions and the GDPRcited by 1,086
  10. 2018Model Cards for Model Reportingcited by 1,068
  11. 2017Counterfactual Fairnesscited by 802
  12. 2016The Mythos of Model Interpretabilitycited by 716
38 more
  1. 2014Certifying and removing disparate impactcited by 715
  2. 2015Distillation as a Defense to Adversarial Perturbations against Deep Neural Networkscited by 675
  3. 2016Stealing Machine Learning Models via Prediction APIscited by 602
  4. 2019Fooling LIME and SHAP: Adversarial Attacks on Post hoc Explanation Methodscited by 440
  5. 2019A Survey on Explainable Artificial Intelligence (XAI): Towards Medical XAIcited by 403
  6. 2017Adversarial Examples: Attacks and Defenses for Deep Learningcited by 350
  7. 2017Deep Models Under the GAN: Information Leakage from Collaborative Deep Learningcited by 326
  8. 2018AI Fairness 360: An Extensible Toolkit for Detecting, Understanding, and Mitigating Unwanted Algorithmic Biascited by 319
  9. 2018FakeNewsNet: A Data Repository with News Content, Social Context and Spatialtemporal Information for Studying Fake News on Social Mediacited by 281
  10. 2014RAPPOR: Randomized Aggregatable Privacy-Preserving Ordinal Responsecited by 262
  11. 2018Adversarial Attacks and Defences: A Surveycited by 259
  12. 2017Word Embeddings Quantify 100 Years of Gender and Ethnic Stereotypescited by 251
  13. 2019Causality for Machine Learningcited by 242
  14. 2020A Survey on Causal Inferencecited by 202
  15. 2019Fair Regression: Quantitative Definitions and Reduction-based Algorithmscited by 168
  16. 2019A Meta-Transfer Objective for Learning to Disentangle Causal Mechanismscited by 158
  17. 2016Unbiased Learning-to-Rank with Biased Feedbackcited by 155
  18. 2017A Convex Framework for Fair Regressioncited by 147
  19. 2019Identifying and Correcting Label Bias in Machine Learningcited by 146
  20. 2017Fair Inference On Outcomescited by 143
  21. 2019Fairness-Aware Ranking in Search & Recommendation Systems with Application to LinkedIn Talent Searchcited by 137
  22. 2013Hacking Smart Machines with Smarter Ones: How to Extract Meaningful Data from Machine Learning Classifierscited by 128
  23. 2020Uncertainty as a Form of Transparency: Measuring, Communicating, and Using Uncertaintycited by 128
  24. 2017Fairness Testing: Testing Software for Discriminationcited by 123
  25. 2018Robustly Disentangled Causal Mechanisms: Validating Deep Representations for Interventional Robustnesscited by 108
  26. 2017Runaway Feedback Loops in Predictive Policingcited by 97
  27. 2017Beyond News Contents: The Role of Social Context for Fake News Detectioncited by 91
  28. 2016Towards the Science of Security and Privacy in Machine Learningcited by 89
  29. 2014Automated Experiments on Ad Privacy Settings: A Tale of Opacity, Choice, and Discriminationcited by 83
  30. 2020Causal Interpretability for Machine Learning -- Problems, Methods and Evaluationcited by 82
  31. 2019Neural Network Attributions: A Causal Perspectivecited by 77
  32. 2016A causal framework for discovering and removing direct and indirect discriminationcited by 71
  33. 2018Mobile Sensor Data Anonymizationcited by 70
  34. 2020Privacy in Deep Learning: A Surveycited by 70
  35. 2018Theoretical Impediments to Machine Learning With Seven Sparks from the Causal Revolutioncited by 66
  36. 2020Trustworthy AIcited by 62
  37. 2019Conscientious Classification: A Data Scientist's Guide to Discrimination-Aware Classificationcited by 55
  38. 2016On the Safety of Machine Learning: Cyber-Physical Systems, Decision Sciences, and Data Productscited by 52

50 most cited of 79 All 79 (JSON)

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 5; 2022: 9; 2023: 12; 2024: 12; 2025: 10; 2026: 8.

56 papers.

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  1. 2024SNIFFER: Multimodal Large Language Model for Explainable Out-of-Context Misinformation Detectioncited by 73
  2. 2023A Survey on Intersectional Fairness in Machine Learning: Notions, Mitigation, and Challengescited by 41
  3. 2023Interpreting Pretrained Language Models via Concept Bottleneckscited by 33
  4. 2023Human-Centric Multimodal Machine Learning: Recent Advances and Testbed on AI-based Recruitmentcited by 32
  5. 2021Towards Green Automated Machine Learning: Status Quo and Future Directionscited by 25
  6. 2022Evaluation Methods and Measures for Causal Learning Algorithmscited by 23
  7. 2024Deconstructing The Ethics of Large Language Models from Long-standing Issues to New-emerging Dilemmas: A Surveycited by 23
  8. 2021Responsible AI: Gender bias assessment in emotion recognitioncited by 22
  9. 2021A Framework for Fairness: A Systematic Review of Existing Fair AI Solutionscited by 18
  10. 2023Fairness through Aleatoric Uncertaintycited by 16
  11. 2023Equal Opportunity of Coverage in Fair Regressioncited by 16
  12. 2024A Survey on Safe Multi-Modal Learning Systemcited by 15
38 more
  1. 2024Assessing Empathy in Large Language Models with Real-World Physician-Patient Interactionscited by 13
  2. 2025Addressing Bias in LLMs: Strategies and Application to Fair AI-based Recruitmentcited by 12
  3. 2025PBa-LLM: Privacy- and Bias-aware NLP using Named-Entity Recognition (NER)cited by 10
  4. 2025Artificial Intelligence and Generative Models for Materials Discovery -- A Reviewcited by 8
  5. 2021Causal Learning for Socially Responsible AIcited by 7
  6. 2022Fair Classification via Domain Adaptation: A Dual Adversarial Learning Approachcited by 6
  7. 2022When Fairness Meets Privacy: Fair Classification with Semi-Private Sensitive Attributescited by 6
  8. 2022Trustworthy Anomaly Detection: A Surveycited by 5
  9. 2022Toward Understanding Bias Correlations for Mitigation in NLPcited by 5
  10. 2023A Rapid Review of Responsible AI frameworks: How to guide the development of ethical AIcited by 5
  11. 2023Adversarial Machine Learning for Social Good: Reframing the Adversary as an Allycited by 5
  12. 2022The Roles and Modes of Human Interactions with Automated Machine Learning Systemscited by 4
  13. 2022Liability regimes in the age of AI: a use-case driven analysis of the burden of proofcited by 4
  14. 2022Undesirable Biases in NLP: Addressing Challenges of Measurementcited by 4
  15. 2023Less is More: The Influence of Pruning on the Explainability of CNNscited by 4
  16. 2024Long-Term Fairness Inquiries and Pursuits in Machine Learning: A Survey of Notions, Methods, and Challengescited by 4
  17. 2025World Models in Artificial Intelligence: Sensing, Learning, and Reasoning Like a Childcited by 4
  18. 2022Debiasing Word Embeddings with Nonlinear Geometrycited by 3
  19. 2023Unveiling the Role of Message Passing in Dual-Privacy Preservation on GNNscited by 3
  20. 2024Trustworthy and Responsible AI for Human-Centric Autonomous Decision-Making Systemscited by 3
  21. 2025A Design Framework for operationalizing Trustworthy Artificial Intelligence in Healthcare: Requirements, Tradeoffs and Challenges for its Clinical Adoptioncited by 3
  22. 2025Toxicity in Online Platforms and AI Systems: A Survey of Needs, Challenges, Mitigations, and Future Directionscited by 3
  23. 2023Fair Few-shot Learning with Auxiliary Setscited by 2
  24. 2023Intersectionality and Testimonial Injustice in Medical Recordscited by 1
  25. 2023Simulating counterfactualscited by 1
  26. 2024A Mathematical Model of the Hidden Feedback Loop Effect in Machine Learning Systemscited by 1
  27. 2026Common Sense vs. Morality: The Curious Case of Narrative Focus Bias in LLMscited by 1
  28. 2021An Interpretable Graph-based Mapping of Trustworthy Machine Learning Researchcited by 0
  29. 2024Responsible AI: Portraits with Intelligent Bibliometricscited by 0
  30. 2024Evaluating AI Group Fairness: a Fuzzy Logic Perspectivecited by 0
  31. 2024Behavior Matters: An Alternative Perspective on Promoting Responsible Data Sciencecited by 0
  32. 2024Data Quality Awareness: A Journey from Traditional Data Management to Data Science Systemscited by 0
  33. 2024The Return of Pseudosciences in Artificial Intelligence: Have Machine Learning and Deep Learning Forgotten Lessons from Statistics and History?cited by 0
  34. 2025Privacy in Responsible AI: Approaches to Facial Recognition from Cloud Providerscited by 0
  35. 2025The Fair Game: Auditing & Debiasing AI Algorithms Over Timecited by 0
  36. 2025Large Language Models' Complicit Responses to Illicit Instructions across Socio-Legal Contextscited by 0
  37. 2025A Gray Literature Study on Fairness Requirements in AI-enabled Software Engineeringcited by 0
  38. 2026Feedback Indices to Evaluate LLM Responses to Rebuttals for Multiple Choice Type Questionscited by 0

50 most cited of 56 All 56 (JSON)