TBNAI
Whose Ground Truth? Embracing Ambiguity in Human-Centered AI
Publication: cs.AI updates on arXiv.org
Author: Jingyao Wu, Mohammad Tariqul Islam, Per R{\aa}dberg Nagb{\o}l, Julie Gerlings, Giovanni Leoni, Georgiana Fryza, Lars Pilegaard Thomsen, Jakob Mainz
Source published: 09/10/2026, 04:00:00 UTC
Briefing created: 09/10/2026, 04:41:31 UTC
Based on a feed excerpt; full article content has not been acquired. Source evidence retrieved 09/10/2026, 04:03:24 UTC.
Briefings
This article argues that human-centered AI should model the ambiguity of human interpretations rather than relying on a single ground truth. Published as a position paper on arXiv, it calls for shifting AI development to distinguish meaningful ambiguity from annotation noise. The authors suggest that current machine learning assumptions of a definitive truth are flawed for tasks where multiple valid interpretations exist simultaneously.
Key points
- The authors propose a shift in AI development to model the interpretation space of plausible human judgments instead of assuming a single definitive ground truth.
- The paper argues that reducing ambiguity to a single target risks overlooking meaningful information about human perception diversity.
Context and limitations
The evidence is limited to the abstract of a preprint article from October 2026, with no specific dates, authors, or quantitative data provided.
Generated by Bittrees-hosted qwen35-2b sha256:aaf42c8b7c3c on 09/10/2026, 04:41:31 UTC. Based on publisher-provided feed evidence; may not cover the full original article. Check the source for details.
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