# Situated Data, Situated Systems: A Methodology to Engage with Power Relations in Natural Language Processing Research

arXiv: https://arxiv.org/abs/2011.05911 · HTML: https://mlsanity.com/paper/2011.05911/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Nov 2020.

## Built on by (most cited first, 6 of 6)

- [Gender Bias in Machine Translation](https://mlsanity.com/paper/2104.06001/) (2021)
- [A Systematic Literature Review of Human-Centered, Ethical, and Responsible AI](https://mlsanity.com/paper/2302.05284/) (2023)
- [(Un)fair devices: Moving beyond AI accuracy in personal sensing](https://mlsanity.com/paper/2303.15585/) (2023)
- [Surveys Considered Harmful? Reflecting on the Use of Surveys in AI Research, Development, and Governance](https://mlsanity.com/paper/2408.01458/) (2024)
- [Ethics Whitepaper: Whitepaper on Ethical Research into Large Language Models](https://mlsanity.com/paper/2410.19812/) (2024)
- [Investigating the Capabilities and Limitations of Machine Learning for Identifying Bias in English Language Data with Information and Heritage Professionals](https://mlsanity.com/paper/2504.00860/) (2025)

## Abstract (arXiv)

We propose a bias-aware methodology to engage with power relations in natural language processing (NLP) research. NLP research rarely engages with bias in social contexts, limiting its ability to mitigate bias. While researchers have recommended actions, technical methods, and documentation practices, no methodology exists to integrate critical reflections on bias with technical NLP methods. In this paper, after an extensive and interdisciplinary literature review, we contribute a bias-aware methodology for NLP research. We also contribute a definition of biased text, a discussion of the implications of biased NLP systems, and a case study demonstrating how we are executing the bias-aware methodology in research on archival metadata descriptions.