# Whose Ground Truth? Accounting for Individual and Collective Identities Underlying Dataset Annotation arXiv: https://arxiv.org/abs/2112.04554 ยท HTML: https://mlsanity.com/paper/2112.04554/ ## Key facts - Cited by 52 papers in the map (data through 14 Oct 2026). - Builds on 9 of its 30 references that are in the map. - First posted on arXiv 8 Dec 2021. ## Builds on (most cited first, 9 of 9) - [Datasheets for Datasets](https://mlsanity.com/paper/1803.09010/) (2018) - [Towards Accountability for Machine Learning Datasets: Practices from Software Engineering and Infrastructure](https://mlsanity.com/paper/2010.13561/) (2020) - [On Releasing Annotator-Level Labels and Information in Datasets](https://mlsanity.com/paper/2110.05699/) (2021) - [Re-imagining Algorithmic Fairness in India and Beyond](https://mlsanity.com/paper/2101.09995/) (2021) - [The Dataset Nutrition Label: A Framework To Drive Higher Data Quality Standards](https://mlsanity.com/paper/1805.03677/) (2018) - [Do Datasets Have Politics? Disciplinary Values in Computer Vision Dataset Development](https://mlsanity.com/paper/2108.04308/) (2021) - [Garbage In, Garbage Out? Do Machine Learning Application Papers in Social Computing Report Where Human-Labeled Training Data Comes From?](https://mlsanity.com/paper/1912.08320/) (2019) - [Beyond Fair Pay: Ethical Implications of NLP Crowdsourcing](https://mlsanity.com/paper/2104.10097/) (2021) - [Characterizing the Global Crowd Workforce: A Cross-Country Comparison of Crowdworker Demographics](https://mlsanity.com/paper/1812.05948/) (2018) ## Built on by (most cited first, 30 of 52) - [The 'Problem' of Human Label Variation: On Ground Truth in Data, Modeling and Evaluation](https://mlsanity.com/paper/2211.02570/) (2022) - [On the Algorithmic Bias of Aligning Large Language Models with RLHF: Preference Collapse and Matching Regularization](https://mlsanity.com/paper/2405.16455/) (2024) - [FACET: Fairness in Computer Vision Evaluation Benchmark](https://mlsanity.com/paper/2309.00035/) (2023) - [AI's Regimes of Representation: A Community-centered Study of Text-to-Image Models in South Asia](https://mlsanity.com/paper/2305.11844/) (2023) - [Supporting Human-AI Collaboration in Auditing LLMs with LLMs](https://mlsanity.com/paper/2304.09991/) (2023) - ["HOT" ChatGPT: The promise of ChatGPT in detecting and discriminating hateful, offensive, and toxic comments on social media](https://mlsanity.com/paper/2304.10619/) (2023) - [Content Moderation by LLM: From Accuracy to Legitimacy](https://mlsanity.com/paper/2409.03219/) (2024) - [Capturing Perspectives of Crowdsourced Annotators in Subjective Learning Tasks](https://mlsanity.com/paper/2311.09743/) (2023) - [Farsight: Fostering Responsible AI Awareness During AI Application Prototyping](https://mlsanity.com/paper/2402.15350/) (2024) - [It Takes Two to Tango: Navigating Conceptualizations of NLP Tasks and Measurements of Performance](https://mlsanity.com/paper/2305.09022/) (2023) - [Ground(less) Truth: A Causal Framework for Proxy Labels in Human-Algorithm Decision-Making](https://mlsanity.com/paper/2302.06503/) (2023) - [Labeling instructions matter in biomedical image analysis](https://mlsanity.com/paper/2207.09899/) (2022) - [Direct Alignment with Heterogeneous Preferences](https://mlsanity.com/paper/2502.16320/) (2025) - [Wikibench: Community-Driven Data Curation for AI Evaluation on Wikipedia](https://mlsanity.com/paper/2402.14147/) (2024) - [Subgroup Robustness Grows On Trees: An Empirical Baseline Investigation](https://mlsanity.com/paper/2211.12703/) (2022) - [Ethical Considerations for Responsible Data Curation](https://mlsanity.com/paper/2302.03629/) (2023) - [GRASP: A Disagreement Analysis Framework to Assess Group Associations in Perspectives](https://mlsanity.com/paper/2311.05074/) (2023) - [Harm Amplification in Text-to-Image Models](https://mlsanity.com/paper/2402.01787/) (2024) - [Scaling Laws Do Not Scale](https://mlsanity.com/paper/2307.03201/) (2023) - [Skin Deep: Investigating Subjectivity in Skin Tone Annotations for Computer Vision Benchmark Datasets](https://mlsanity.com/paper/2305.09072/) (2023) - [Ground Truth Or Dare: Factors Affecting The Creation Of Medical Datasets For Training AI](https://mlsanity.com/paper/2309.12327/) (2023) - [D3CODE: Disentangling Disagreements in Data across Cultures on Offensiveness Detection and Evaluation](https://mlsanity.com/paper/2404.10857/) (2024) - [TRUE: Re-evaluating Factual Consistency Evaluation](https://mlsanity.com/paper/2204.04991/) (2022) - [Discipline and Label: A WEIRD Genealogy and Social Theory of Data Annotation](https://mlsanity.com/paper/2402.06811/) (2024) - [From LLM-anation to LLM-orchestrator: Coordinating Small Models for Data Labeling](https://mlsanity.com/paper/2506.16393/) (2025) - [Noise Audits Improve Moral Foundation Classification](https://mlsanity.com/paper/2210.07415/) (2022) - [Don't Label Twice: Quantity Beats Quality when Comparing Binary Classifiers on a Budget](https://mlsanity.com/paper/2402.02249/) (2024) - [A View From Somewhere: Human-Centric Face Representations](https://mlsanity.com/paper/2303.17176/) (2023) - [Whose Preferences? Differences in Fairness Preferences and Their Impact on the Fairness of AI Utilizing Human Feedback](https://mlsanity.com/paper/2406.05902/) (2024) - [The Risks of Machine Learning Systems](https://mlsanity.com/paper/2204.09852/) (2022) ## Abstract (arXiv) Human annotations play a crucial role in machine learning (ML) research and development. However, the ethical considerations around the processes and decisions that go into building ML datasets has not received nearly enough attention. In this paper, we survey an array of literature that provides insights into ethical considerations around crowdsourced dataset annotation. We synthesize these insights, and lay out the challenges in this space along two layers: (1) who the annotator is, and how the annotators' lived experiences can impact their annotations, and (2) the relationship between the annotators and the crowdsourcing platforms and what that relationship affords them. Finally, we put forth a concrete set of recommendations and considerations for dataset developers at various stages of the ML data pipeline: task formulation, selection of annotators, platform and infrastructure choices, dataset analysis and evaluation, and dataset documentation and release.