# Beyond Fair Pay: Ethical Implications of NLP Crowdsourcing

arXiv: https://arxiv.org/abs/2104.10097 · HTML: https://mlsanity.com/paper/2104.10097/

## Key facts

- Cited by 53 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 65 references that are in the map.
- First posted on arXiv 20 Apr 2021.

## Builds on (most cited first, 6 of 6)

- [CrowS-Pairs: A Challenge Dataset for Measuring Social Biases in Masked Language Models](https://mlsanity.com/paper/2010.00133/) (2020)
- [Social Bias Frames: Reasoning about Social and Power Implications of Language](https://mlsanity.com/paper/1911.03891/) (2019)
- [A Computational Approach to Understanding Empathy Expressed in Text-Based Mental Health Support](https://mlsanity.com/paper/2009.08441/) (2020)
- [Learning to Speak and Act in a Fantasy Text Adventure Game](https://mlsanity.com/paper/1903.03094/) (2019)
- [Conversational Markers of Constructive Discussions](https://mlsanity.com/paper/1604.07407/) (2016)
- [Personalized Emphasis Framing for Persuasive Message Generation](https://mlsanity.com/paper/1607.08898/) (2016)

## Built on by (most cited first, 30 of 53)

- [Repairing the Cracked Foundation: A Survey of Obstacles in Evaluation Practices for Generated Text](https://mlsanity.com/paper/2202.06935/) (2022)
- [The PRISM Alignment Dataset: What Participatory, Representative and Individualised Human Feedback Reveals About the Subjective and Multicultural Alignment of Large Language Models](https://mlsanity.com/paper/2404.16019/) (2024)
- [POTATO: The Portable Text Annotation Tool](https://mlsanity.com/paper/2212.08620/) (2022)
- [CrowdWorkSheets: Accounting for Individual and Collective Identities Underlying Crowdsourced Dataset Annotation](https://mlsanity.com/paper/2206.08931/) (2022)
- [Bridging the Gap: A Survey on Integrating (Human) Feedback for Natural Language Generation](https://mlsanity.com/paper/2305.00955/) (2023)
- [Whose Ground Truth? Accounting for Individual and Collective Identities Underlying Dataset Annotation](https://mlsanity.com/paper/2112.04554/) (2021)
- [Just What do You Think You're Doing, Dave?' A Checklist for Responsible Data Use in NLP](https://mlsanity.com/paper/2109.06598/) (2021)
- [ConvAbuse: Data, Analysis, and Benchmarks for Nuanced Abuse Detection in Conversational AI](https://mlsanity.com/paper/2109.09483/) (2021)
- [Analyzing Dataset Annotation Quality Management in the Wild](https://mlsanity.com/paper/2307.08153/) (2023)
- [Quantifying the Dialect Gap and its Correlates Across Languages](https://mlsanity.com/paper/2310.15135/) (2023)
- [Metadata Archaeology: Unearthing Data Subsets by Leveraging Training Dynamics](https://mlsanity.com/paper/2209.10015/) (2022)
- [Position: Measure Dataset Diversity, Don't Just Claim It](https://mlsanity.com/paper/2407.08188/) (2024)
- [Bugs in the Data: How ImageNet Misrepresents Biodiversity](https://mlsanity.com/paper/2208.11695/) (2022)
- [Consistency is Key: Disentangling Label Variation in Natural Language Processing with Intra-Annotator Agreement](https://mlsanity.com/paper/2301.10684/) (2023)
- [Ethics Sheet for Automatic Emotion Recognition and Sentiment Analysis](https://mlsanity.com/paper/2109.08256/) (2021)
- [Listening to Affected Communities to Define Extreme Speech: Dataset and Experiments](https://mlsanity.com/paper/2203.11764/) (2022)
- [A Material Lens on Coloniality in NLP](https://mlsanity.com/paper/2311.08391/) (2023)
- [A Review of Human Evaluation for Style Transfer](https://mlsanity.com/paper/2106.04747/) (2021)
- [Incorporating Worker Perspectives into MTurk Annotation Practices for NLP](https://mlsanity.com/paper/2311.02802/) (2023)
- [Ethical Considerations for Responsible Data Curation](https://mlsanity.com/paper/2302.03629/) (2023)
- [Dancing Between Success and Failure: Edit-level Simplification Evaluation using SALSA](https://mlsanity.com/paper/2305.14458/) (2023)
- [Prompting in the Dark: Assessing Human Performance in Prompt Engineering for Data Labeling When Gold Labels Are Absent](https://mlsanity.com/paper/2502.11267/) (2025)
- [Are Language Models Worse than Humans at Following Prompts? It's Complicated](https://mlsanity.com/paper/2301.07085/) (2023)
- [Exploring Musical Roots: Applying Audio Embeddings to Empower Influence Attribution for a Generative Music Model](https://mlsanity.com/paper/2401.14542/) (2024)
- [Counting on Consensus: Selecting the Right Inter-annotator Agreement Metric for NLP Annotation and Evaluation](https://mlsanity.com/paper/2603.06865/) (2026)
- [Quantifying and Avoiding Unfair Qualification Labour in Crowdsourcing](https://mlsanity.com/paper/2105.12762/) (2021)
- [Risk-graded Safety for Handling Medical Queries in Conversational AI](https://mlsanity.com/paper/2210.00572/) (2022)
- [The Iron(ic) Melting Pot: Reviewing Human Evaluation in Humour, Irony and Sarcasm Generation](https://mlsanity.com/paper/2311.05552/) (2023)
- [FAIR Enough: How Can We Develop and Assess a FAIR-Compliant Dataset for Large Language Models' Training?](https://mlsanity.com/paper/2401.11033/) (2024)
- [Voices in a Crowd: Searching for Clusters of Unique Perspectives](https://mlsanity.com/paper/2407.14259/) (2024)

## Abstract (arXiv)

The use of crowdworkers in NLP research is growing rapidly, in tandem with the exponential increase in research production in machine learning and AI. Ethical discussion regarding the use of crowdworkers within the NLP research community is typically confined in scope to issues related to labor conditions such as fair pay. We draw attention to the lack of ethical considerations related to the various tasks performed by workers, including labeling, evaluation, and production. We find that the Final Rule, the common ethical framework used by researchers, did not anticipate the use of online crowdsourcing platforms for data collection, resulting in gaps between the spirit and practice of human-subjects ethics in NLP research. We enumerate common scenarios where crowdworkers performing NLP tasks are at risk of harm. We thus recommend that researchers evaluate these risks by considering the three ethical principles set up by the Belmont Report. We also clarify some common misconceptions regarding the Institutional Review Board (IRB) application. We hope this paper will serve to reopen the discussion within our community regarding the ethical use of crowdworkers.