Summary of Reducing Annotator Bias by Belief Elicitation, By Terne Sasha Thorn Jakobsen et al.
Reducing annotator bias by belief elicitation
by Terne Sasha Thorn Jakobsen, Andreas Bjerre-Nielsen, Robert Böhm
First submitted to arxiv on: 21 Oct 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); General Economics (econ.GN)
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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 proposes a simple method to reduce annotator bias in crowdsourced annotations for Artificial Intelligence (AI) development. Annotator bias can lead to representational bias against minority group perspectives, which is problematic. The proposed method asks annotators about their beliefs on other annotators’ judgments of an instance, assuming these beliefs provide more representative and less biased labels than actual judgments. Two controlled experiments were conducted, involving 1,590 participants from different political backgrounds (Democrats and Republicans), to judge statements as arguments and report beliefs about others’ judgments. The results show that bias is consistently reduced when asking for beliefs instead of judgments. This method has the potential to reduce the risk of annotator bias, improving the generalisability of AI systems and preventing harm to unrepresented socio-demographic groups. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research helps make sure Artificial Intelligence (AI) is fair and doesn’t discriminate against certain groups of people. Sometimes, when people help label data for AI, their own biases can sneak into the labels. This can be a problem because it means AI might not understand or represent minority perspectives correctly. The researchers came up with a new way to handle this bias without needing many people to help or lots of data points. They asked people about what they think others would say about an issue, and found that when people do this, their own biases are reduced. This could make AI more fair and helpful for everyone. |
