Summary of Ethnography and Machine Learning: Synergies and New Directions, by Zhuofan Li and Corey M. Abramson
Ethnography and Machine Learning: Synergies and New Directions
by Zhuofan Li, Corey M. Abramson
First submitted to arxiv on: 8 Dec 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Computers and Society (cs.CY); Methodology (stat.ME)
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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 The proposed chapter combines ethnography and machine learning to enable productive coevolution of field methods and machine learning. It highlights the value and challenges of using machine learning alongside qualitative field research, particularly for large comparative studies. The chapter discusses recent methodological trends in this area, provides examples from several large projects, and concludes with a roadmap for future development. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper shows how combining ethnography and machine learning can be useful. Ethnography helps us understand people’s lives, while machine learning uses big data to perform tasks. This chapter explains why using both together is helpful for some types of research. It also shares examples from several projects that use this combination and offers a plan for making it work better in the future. |
Keywords
» Artificial intelligence » Machine learning
