Summary of The Perspectivist Paradigm Shift: Assumptions and Challenges Of Capturing Human Labels, by Eve Fleisig et al.
The Perspectivist Paradigm Shift: Assumptions and Challenges of Capturing Human Labels
by Eve Fleisig, Su Lin Blodgett, Dan Klein, Zeerak Talat
First submitted to arxiv on: 9 May 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computation and Language (cs.CL); Computers and Society (cs.CY)
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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 position paper challenges traditional practices in machine learning data labeling by treating annotator disagreement as a valuable source of information. The authors examine various causes of disagreement, some of which are challenged by perspectivist approaches, while others remain to be addressed. They also discuss practical and normative challenges for work operating under these assumptions. The paper concludes with recommendations for the data labeling pipeline and future research directions. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper shows that annotator disagreement is not always a bad thing in machine learning. Instead of trying to minimize it, we can use it as valuable information. The authors look at why people disagree on labels, some reasons are challenged by new ways of thinking, while others need more work. They also talk about the challenges of using these disagreements and how they think future research should move forward. |
Keywords
» Artificial intelligence » Data labeling » Machine learning
