Are we chasing ghosts? Quantifying unattributable polarization, and attributing the rest to annotator groups
arXiv:2602.06055v2 Announce Type: replace Abstract: Standard agreement metrics often fail to capture systematic differences in opinion between minority and majority-group annotators, jeopardizing tasks such as hate speech and toxicity detection. Polarization has recently been proposed as a more robust way of distinguishing minor disagreements from systematic differences in opinion, but existing approaches do not provide practical tools for attributing it to specific annotator groups. We evaluate current methods and identify two major limitations in realistic settings: (1) the presence of ``inh
The proliferation of AI systems requires more nuanced and reliable methods for data annotation, particularly in sensitive areas like content moderation, which existing metrics fail to address adequately.
Improved methods for quantifying and attributing polarization in annotation data directly impact the fairness, safety, and effectiveness of AI models, especially those used in critical decision-making or public-facing applications.
The ability to accurately identify and attribute 'unattributable polarization' moves beyond simple disagreement metrics, allowing developers to diagnose systemic biases introduced by annotator groups and build more robust AI.
- · AI developers
- · Content moderation platforms
- · Fairness & ethics in AI research
- · Large Language Models
- · AI systems with unaddressed biases
- · Unreliable annotation services
- · Standard agreement metrics
More robust and less biased AI models emerge due to better understanding and mitigation of annotator-induced polarization.
Public trust in AI systems handling sensitive topics, such as hate speech detection, could increase as these systems become demonstrably fairer.
New regulatory frameworks may emerge that mandate specific standards for polarization quantification and mitigation in AI training data.
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Read at arXiv cs.CL