Data to 7 Oct 2026

Improving fairness in machine learning systems: What do industry practitioners need?

arXiv 1812.05239 ↗First posted 13 Dec 2018cs.HC cs.CY cs.LGcited by 262

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Abstract (arXiv)

The potential for machine learning (ML) systems to amplify social inequities and unfairness is receiving increasing popular and academic attention. A surge of recent work has focused on the development of algorithmic tools to assess and mitigate such unfairness. If these tools are to have a positive impact on industry practice, however, it is crucial that their design be informed by an understanding of real-world needs. Through 35 semi-structured interviews and an anonymous survey of 267 ML practitioners, we conduct the first systematic investigation of commercial product teams' challenges and needs for support in developing fairer ML systems. We identify areas of alignment and disconnect between the challenges faced by industry practitioners and solutions proposed in the fair ML research literature. Based on these findings, we highlight directions for future ML and HCI research that will better address industry practitioners' needs.

Key facts. Cited by 262 papers in the map (data through 7 Oct 2026). First posted on arXiv 13 Dec 2018.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 49; 2022: 50; 2023: 45; 2024: 43; 2025: 55; 2026: 20.

262 papers.

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2021Towards Personalized Federated Learningcited by 462
2021D'ya like DAGs? A Survey on Structure Learning and Causal Discoverycited by 209
2021The Values Encoded in Machine Learning Researchcited by 172
2023Should ChatGPT be Biased? Challenges and Risks of Bias in Large Language Modelscited by 144
2022Bias Mitigation for Machine Learning Classifiers: A Comprehensive Surveycited by 125
2021Re-imagining Algorithmic Fairness in India and Beyondcited by 102
2021Towards a Science of Human-AI Decision Making: A Survey of Empirical Studiescited by 85
2021Demystifying the Draft EU Artificial Intelligence Actcited by 82
2023Fairlearn: Assessing and Improving Fairness of AI Systemscited by 74
2021Software Engineering for AI-Based Systems: A Surveycited by 68
2021Characterizing Fairness Over the Set of Good Models Under Selective Labelscited by 65
2021Assessing the Fairness of AI Systems: AI Practitioners' Processes, Challenges, and Needs for Supportcited by 61

38 more

2022Interactive Model Cards: A Human-Centered Approach to Model Documentationcited by 61
2021Collaboration Challenges in Building ML-Enabled Systems: Communication, Documentation, Engineering, and Processcited by 56
2021Fair Preprocessing: Towards Understanding Compositional Fairness of Data Transformers in Machine Learning Pipelinecited by 49
2022Evaluation Gaps in Machine Learning Practicecited by 48
2021Disembodied Machine Learning: On the Illusion of Objectivity in NLPcited by 37
2022Multi-Objective Hyperparameter Optimization in Machine Learning -- An Overviewcited by 37
2023Improving Human-AI Collaboration With Descriptions of AI Behaviorcited by 35
2021Evaluating Fairness of Machine Learning Models Under Uncertain and Incomplete Informationcited by 33
2022Exploring How Machine Learning Practitioners (Try To) Use Fairness Toolkitscited by 33
2022Fairness Reprogrammingcited by 33
2022Sensible AI: Re-imagining Interpretability and Explainability using Sensemaking Theorycited by 32
2022A Validity Perspective on Evaluating the Justified Use of Data-driven Decision-making Algorithmscited by 30
2022Understanding Practices, Challenges, and Opportunities for User-Engaged Algorithm Auditing in Industry Practicecited by 30
2023Understanding Frontline Workers' and Unhoused Individuals' Perspectives on AI Used in Homeless Servicescited by 29
2021Discovering and Validating AI Errors With Crowdsourced Failure Reportscited by 28
2022Seeing Like a Toolkit: How Toolkits Envision the Work of AI Ethicscited by 28
2022Understanding Machine Learning Practitioners' Data Documentation Perceptions, Needs, Challenges, and Desideratacited by 28
2024Farsight: Fostering Responsible AI Awareness During AI Application Prototypingcited by 28
2022Aligning Eyes between Humans and Deep Neural Network through Interactive Attention Alignmentcited by 27
2022From plane crashes to algorithmic harm: applicability of safety engineering frameworks for responsible MLcited by 26
2022Operationalizing Machine Learning: An Interview Studycited by 25
2024Evaluating Human-AI Collaboration: A Review and Methodological Frameworkcited by 25
2021Adaptive Sampling for Minimax Fair Classificationcited by 24
2021Amortized Generation of Sequential Algorithmic Recourses for Black-box Modelscited by 24
2021A Sociotechnical View of Algorithmic Fairnesscited by 24
2021PreferenceNet: Encoding Human Preferences in Auction Design with Deep Learningcited by 22
2022Debiasing Methods for Fairer Neural Models in Vision and Language Research: A Surveycited by 22
2022Fairify: Fairness Verification of Neural Networkscited by 22
2023A Meta-Summary of Challenges in Building Products with ML Components -- Collecting Experiences from 4758+ Practitionerscited by 22
2021Fair Clustering Under a Bounded Costcited by 21
2021Machine Learning Practices Outside Big Tech: How Resource Constraints Challenge Responsible Developmentcited by 21
2023Model Compression in Practice: Lessons Learned from Practitioners Creating On-device Machine Learning Experiencescited by 21
2024AboutMe: Using Self-Descriptions in Webpages to Document the Effects of English Pretraining Data Filterscited by 21
2024Towards Trustworthy AI: A Review of Ethical and Robust Large Language Modelscited by 21
2021Technical Challenges for Training Fair Neural Networkscited by 20
2021Fairness On The Ground: Applying Algorithmic Fairness Approaches to Production Systemscited by 20
2022Seamful XAI: Operationalizing Seamful Design in Explainable AIcited by 20
2024FairSIN: Achieving Fairness in Graph Neural Networks through Sensitive Information Neutralizationcited by 20

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