Summary of Discipline and Label: a Weird Genealogy and Social Theory Of Data Annotation, by Andrew Smart et al.
Discipline and Label: A WEIRD Genealogy and Social Theory of Data Annotation
by Andrew Smart, Ding Wang, Ellis Monk, Mark Díaz, Atoosa Kasirzadeh, Erin Van Liemt, Sonja Schmer-Galunder
First submitted to arxiv on: 9 Feb 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper explores the critical aspects of machine learning, specifically focusing on data annotation. Recent studies have highlighted the importance of rater diversity in ensuring fairness and model performance. Additionally, researchers are examining working conditions for data annotators, the impact of subjectivity on labels, and potential psychological harms from certain annotation tasks. This study delves into the psychological and perceptual aspects of data annotation, drawing parallels with critiques of computerized lab-based experiments in the 1970s. The paper questions whether data annotations permit generalization beyond specific settings or locations where they were obtained. Data annotation is a crucial step in machine learning, and this research aims to better understand its impact on AI model development. By analyzing recent studies and synthesizing evidence from various lines of inquiry, this study argues that data annotation can perpetuate outdated social categories if not approached with care. A framework for understanding the interplay between global social conditions and the subjective experience of data annotation work is proposed. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Data annotation is a crucial part of machine learning. Researchers are trying to understand how different people do their job, why this matters, and what we can learn from it. They want to know if the way we do our job affects how well AI models work or how fair they are. Some people think that doing data annotation tasks in one place might not be the same as doing them somewhere else. This research looks at how data annotation works and why it’s important. It says that we need to understand how different cultures and places can affect our results. The study also talks about how some people might have different opinions on what is right or wrong, which can affect how we do our job. |
Keywords
» Artificial intelligence » Generalization » Machine learning
