mlsanityData to 7 Oct 2026

Out of Context: Investigating the Bias and Fairness Concerns of "Artificial Intelligence as a Service"

arXiv 2302.01448 ↗First posted 2 Feb 2023cs.LG cs.AI cs.CYcited by 15

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Abstract (arXiv)

"AI as a Service" (AIaaS) is a rapidly growing market, offering various plug-and-play AI services and tools. AIaaS enables its customers (users) - who may lack the expertise, data, and/or resources to develop their own systems - to easily build and integrate AI capabilities into their applications. Yet, it is known that AI systems can encapsulate biases and inequalities that can have societal impact. This paper argues that the context-sensitive nature of fairness is often incompatible with AIaaS' 'one-size-fits-all' approach, leading to issues and tensions. Specifically, we review and systematise the AIaaS space by proposing a taxonomy of AI services based on the levels of autonomy afforded to the user. We then critically examine the different categories of AIaaS, outlining how these services can lead to biases or be otherwise harmful in the context of end-user applications. In doing so, we seek to draw research attention to the challenges of this emerging area.

Key facts. Cited by 15 papers in the map (data through 7 Oct 2026). Builds on 61 of its 157 references that are in the map. First posted on arXiv 2 Feb 2023.

Builds on

61 of 157 references.

References · 61

  1. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,204
  2. 2020Language Models are Few-Shot Learnerscited by 32,199
  3. 2016Equality of Opportunity in Supervised Learningcited by 2,000
  4. 2019A Survey on Bias and Fairness in Machine Learningcited by 1,911
  5. 2018Datasheets for Datasetscited by 1,371
  6. 2016Man is to Computer Programmer as Woman is to Homemaker? Debiasing Word Embeddingscited by 1,304
  7. 2018Model Cards for Model Reportingcited by 1,068
  8. 2014Certifying and removing disparate impactcited by 715
  9. 2016Fair prediction with disparate impact: A study of bias in recidivism prediction instrumentscited by 671
  10. 2018Mitigating Unwanted Biases with Adversarial Learningcited by 663
  11. 2018A Reductions Approach to Fair Classificationcited by 585
  12. 2016Inherent Trade-Offs in the Fair Determination of Risk Scorescited by 582
38 more
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  2. 2017Men Also Like Shopping: Reducing Gender Bias Amplification using Corpus-level Constraintscited by 420
  3. 2017On Fairness and Calibrationcited by 406
  4. 2020Closing the AI Accountability Gap: Defining an End-to-End Framework for Internal Algorithmic Auditingcited by 380
  5. 2016Fairness Beyond Disparate Treatment & Disparate Impact: Learning Classification without Disparate Mistreatmentcited by 368
  6. 2019Mitigating Gender Bias in Natural Language Processing: Literature Reviewcited by 287
  7. 2018Improving fairness in machine learning systems: What do industry practitioners need?cited by 262
  8. 2018Women also Snowboard: Overcoming Bias in Captioning Modelscited by 236
  9. 2019Mitigating Bias in Algorithmic Hiring: Evaluating Claims and Practicescited by 170
  10. 2018Auto-Keras: An Efficient Neural Architecture Search Systemcited by 169
  11. 2016Fairness in Learning: Classic and Contextual Banditscited by 167
  12. 2017No Classification without Representation: Assessing Geodiversity Issues in Open Data Sets for the Developing Worldcited by 152
  13. 2020Minimax Pareto Fairness: A Multi Objective Perspectivecited by 152
  14. 2019Does Object Recognition Work for Everyone?cited by 149
  15. 2021Expanding Explainability: Towards Social Transparency in AI systemscited by 124
  16. 2019Artificial Intelligence: the global landscape of ethics guidelinescited by 107
  17. 2021Re-imagining Algorithmic Fairness in India and Beyondcited by 102
  18. 2018'It's Reducing a Human Being to a Percentage'; Perceptions of Justice in Algorithmic Decisionscited by 91
  19. 2020Saving Face: Investigating the Ethical Concerns of Facial Recognition Auditingcited by 89
  20. 2020Why Fairness Cannot Be Automated: Bridging the Gap Between EU Non-Discrimination Law and AIcited by 81
  21. 2019Human-AI Collaboration in Data Science: Exploring Data Scientists' Perceptions of Automated AIcited by 79
  22. 2017Feature Engineering for Predictive Modeling using Reinforcement Learningcited by 78
  23. 2019Image Counterfactual Sensitivity Analysis for Detecting Unintended Biascited by 74
  24. 2020The De-democratization of AI: Deep Learning and the Compute Divide in Artificial Intelligence Researchcited by 67
  25. 2021Assessing the Fairness of AI Systems: AI Practitioners' Processes, Challenges, and Needs for Supportcited by 61
  26. 2019Mathematical Notions vs. Human Perception of Fairness: A Descriptive Approach to Fairness for Machine Learningcited by 57
  27. 2021Everyday algorithm auditing: Understanding the power of everyday users in surfacing harmful algorithmic behaviorscited by 47
  28. 2020Fair Bayesian Optimizationcited by 45
  29. 2017Calibrated Fairness in Banditscited by 42
  30. 2019What does it mean to solve the problem of discrimination in hiring? Social, technical and legal perspectives from the UK on automated hiring systemscited by 38
  31. 2020Where Responsible AI meets Reality: Practitioner Perspectives on Enablers for shifting Organizational Practicescited by 38
  32. 2021The AI Index 2021 Annual Reportcited by 34
  33. 2021One Label, One Billion Faces: Usage and Consistency of Racial Categories in Computer Visioncited by 33
  34. 2022Exploring How Machine Learning Practitioners (Try To) Use Fairness Toolkitscited by 33
  35. 2017One button machine for automating feature engineering in relational databasescited by 32
  36. 2018FactSheets: Increasing Trust in AI Services through Supplier's Declarations of Conformitycited by 31
  37. 2021Reviewable Automated Decision-Making: A Framework for Accountable Algorithmic Systemscited by 30
  38. 2022Understanding Practices, Challenges, and Opportunities for User-Engaged Algorithm Auditing in Industry Practicecited by 30

50 most cited of 61 All 61 (JSON)

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 6; 2024: 5; 2025: 3; 2026: 1.

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  2. 2024Do Generative AI Models Output Harm while Representing Non-Western Cultures: Evidence from A Community-Centered Approachcited by 25
  3. 2023Automated Ableism: An Exploration of Explicit Disability Biases in Sentiment and Toxicity Analysis Modelscited by 20
  4. 2025Position is Power: System Prompts as a Mechanism of Bias in Large Language Models (LLMs)cited by 19
  5. 2023Investigating Practices and Opportunities for Cross-functional Collaboration around AI Fairness in Industry Practicecited by 15
  6. 2024Position: Towards Bidirectional Human-AI Alignmentcited by 15
  7. 2023CALM : A Multi-task Benchmark for Comprehensive Assessment of Language Model Biascited by 13
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  9. 2024Blind Spots and Biases: Exploring the Role of Annotator Cognitive Biases in NLPcited by 11
  10. 2023A Systematic Literature Review of Human-Centered, Ethical, and Responsible AIcited by 5
  11. 2024Fairness Concerns in App Reviews: A Study on AI-based Mobile Appscited by 4
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3 more
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  3. 2026How Supply Chain Dependencies Complicate Bias Measurement and Accountability Attribution in AI Hiring Applicationscited by 0