# Blind Spots and Biases: Exploring the Role of Annotator Cognitive Biases in NLP

arXiv: https://arxiv.org/abs/2404.19071 · HTML: https://mlsanity.com/paper/2404.19071/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- Builds on 22 of its 62 references that are in the map.
- First posted on arXiv 29 Apr 2024.

## Builds on (most cited first, 22 of 22)

- [Equality of Opportunity in Supervised Learning](https://mlsanity.com/paper/1610.02413/) (2016)
- [A Survey on Bias and Fairness in Machine Learning](https://mlsanity.com/paper/1908.09635/) (2019)
- [Man is to Computer Programmer as Woman is to Homemaker? Debiasing Word Embeddings](https://mlsanity.com/paper/1607.06520/) (2016)
- [LaMDA: Language Models for Dialog Applications](https://mlsanity.com/paper/2201.08239/) (2022)
- [Language (Technology) is Power: A Critical Survey of "Bias" in NLP](https://mlsanity.com/paper/2005.14050/) (2020)
- [Certifying and removing disparate impact](https://mlsanity.com/paper/1412.3756/) (2014)
- [A Reductions Approach to Fair Classification](https://mlsanity.com/paper/1803.02453/) (2018)
- [Inherent Trade-Offs in the Fair Determination of Risk Scores](https://mlsanity.com/paper/1609.05807/) (2016)
- [On Fairness and Calibration](https://mlsanity.com/paper/1709.02012/) (2017)
- [Persistent Anti-Muslim Bias in Large Language Models](https://mlsanity.com/paper/2101.05783/) (2021)
- [Fairness Beyond Disparate Treatment & Disparate Impact: Learning Classification without Disparate Mistreatment](https://mlsanity.com/paper/1610.08452/) (2016)
- [Mitigating Gender Bias in Natural Language Processing: Literature Review](https://mlsanity.com/paper/1906.08976/) (2019)
- [A Survey of Human-in-the-loop for Machine Learning](https://mlsanity.com/paper/2108.00941/) (2021)
- [A Framework for Understanding Sources of Harm throughout the Machine Learning Life Cycle](https://mlsanity.com/paper/1901.10002/) (2019)
- [Fairness in Learning: Classic and Contextual Bandits](https://mlsanity.com/paper/1605.07139/) (2016)
- [Re-imagining Algorithmic Fairness in India and Beyond](https://mlsanity.com/paper/2101.09995/) (2021)
- [CrowdWorkSheets: Accounting for Individual and Collective Identities Underlying Crowdsourced Dataset Annotation](https://mlsanity.com/paper/2206.08931/) (2022)
- [Don't Blame the Annotator: Bias Already Starts in the Annotation Instructions](https://mlsanity.com/paper/2205.00415/) (2022)
- [Calibrated Fairness in Bandits](https://mlsanity.com/paper/1707.01875/) (2017)
- [ChatGPT Needs SPADE (Sustainability, PrivAcy, Digital divide, and Ethics) Evaluation: A Review](https://mlsanity.com/paper/2305.03123/) (2023)
- [Out of Context: Investigating the Bias and Fairness Concerns of "Artificial Intelligence as a Service"](https://mlsanity.com/paper/2302.01448/) (2023)
- ["I'm" Lost in Translation: Pronoun Missteps in Crowdsourced Data Sets](https://mlsanity.com/paper/2304.13557/) (2023)

## Built on by (most cited first, 11 of 11)

- [WildFeedback: Aligning LLMs With In-situ User Interactions And Feedback](https://mlsanity.com/paper/2408.15549/) (2024)
- [A Comprehensive Survey of Bias in LLMs: Current Landscape and Future Directions](https://mlsanity.com/paper/2409.16430/) (2024)
- [Addressing Bias in Generative AI: Challenges and Research Opportunities in Information Management](https://mlsanity.com/paper/2502.10407/) (2025)
- [BharatBBQ: A Multilingual Bias Benchmark for Question Answering in the Indian Context](https://mlsanity.com/paper/2508.07090/) (2025)
- [Can Humans Dream of Electric Sheep? Human-Written Samples for Fine-Grained Vision-and-Language Hallucination Benchmarking](https://mlsanity.com/paper/2608.01021/) (2026)
- [Deceptive Humor: A Synthetic Multilingual Benchmark Dataset for Bridging Fabricated Claims with Humorous Content](https://mlsanity.com/paper/2503.16031/) (2025)
- [Uncovering Biases with Reflective Large Language Models](https://mlsanity.com/paper/2408.13464/) (2024)
- [LLM-Human Pipeline for Cultural Context Grounding of Conversations](https://mlsanity.com/paper/2410.13727/) (2024)
- [Expert Evaluation and the Limits of Human Feedback in Mental Health AI Safety Testing](https://mlsanity.com/paper/2601.18061/) (2026)
- [Keeping it Local, Tiny and Real: Automated Report Generation on Edge Computing Devices for Mechatronic-Based Cognitive Systems](https://mlsanity.com/paper/2511.02507/) (2025)
- [Scalable Evaluation of the Realism of Synthetic Environmental Augmentations in Images](https://mlsanity.com/paper/2603.04325/) (2026)

## Abstract (arXiv)

With the rapid proliferation of artificial intelligence, there is growing concern over its potential to exacerbate existing biases and societal disparities and introduce novel ones. This issue has prompted widespread attention from academia, policymakers, industry, and civil society. While evidence suggests that integrating human perspectives can mitigate bias-related issues in AI systems, it also introduces challenges associated with cognitive biases inherent in human decision-making. Our research focuses on reviewing existing methodologies and ongoing investigations aimed at understanding annotation attributes that contribute to bias.