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Five sources of bias in natural language processing
Dirk Hovy1, Shrimai Prabhumoye2
1Marketing Department Bocconi University Milan Italy.
Language and Linguistics Compass
|July 22, 2022
Summary
This study summarizes recent work on demographic bias in natural language processing (NLP). It details five key sources of bias in NLP systems: data, annotation, input representations, models, and research design, offering potential solutions.
Area of Science:
- Natural Language Processing (NLP)
- Artificial Intelligence (AI)
- Computer Science
- Computational Linguistics
Background:
- Growing concern regarding demographic bias in NLP applications.
- Existing research often focuses on describing bias broadly.
- Need for actionable summaries of bias sources and mitigation strategies.
Purpose of the Study:
- To provide a concise, actionable summary of recent work on demographic bias in NLP.
- To identify and detail five primary sources of bias within NLP systems.
- To offer potential countermeasures for mitigating identified biases.
Main Methods:
- Literature review and synthesis of recent research on NLP bias.
- Categorization of bias into five distinct sources: data, annotation, input representations, models, and research design.
- Detailed exploration of each bias source with examples and links to related work.
Main Results:
- Identified five critical points where demographic bias can be introduced into NLP systems.
- Provided concrete examples illustrating bias at each stage.
- Linked to relevant research for deeper understanding and potential solutions.
Conclusions:
- Understanding the five identified sources is crucial for developing fairer NLP systems.
- Actionable insights and countermeasures are essential for addressing demographic bias.
- This work serves as a foundational guide for researchers and practitioners in NLP.
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