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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2510.17085 |
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| _version_ | 1866909857128382464 |
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| author | Chen, Yiling Feng, Shi Kattuman, Paul Yu, Fang-Yi |
| author_facet | Chen, Yiling Feng, Shi Kattuman, Paul Yu, Fang-Yi |
| contents | How can we assess the reliability of a dataset without access to ground truth? We introduce the problem of reliability scoring for datasets collected from potentially strategic sources. The true data are unobserved, but we see outcomes of an unknown statistical experiment that depends on them. To benchmark reliability, we define ground-truth-based orderings that capture how much reported data deviate from the truth. We then propose the Gram determinant score, which measures the volume spanned by vectors describing the empirical distribution of the observed data and experiment outcomes. We show that this score preserves several ground-truth based reliability orderings and, uniquely up to scaling, yields the same reliability ranking of datasets regardless of the experiment -- a property we term experiment agnosticism. Experiments on synthetic noise models, CIFAR-10 embeddings, and real employment data demonstrate that the Gram determinant score effectively captures data quality across diverse observation processes. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_17085 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Data Reliability Scoring Chen, Yiling Feng, Shi Kattuman, Paul Yu, Fang-Yi Machine Learning Computer Science and Game Theory How can we assess the reliability of a dataset without access to ground truth? We introduce the problem of reliability scoring for datasets collected from potentially strategic sources. The true data are unobserved, but we see outcomes of an unknown statistical experiment that depends on them. To benchmark reliability, we define ground-truth-based orderings that capture how much reported data deviate from the truth. We then propose the Gram determinant score, which measures the volume spanned by vectors describing the empirical distribution of the observed data and experiment outcomes. We show that this score preserves several ground-truth based reliability orderings and, uniquely up to scaling, yields the same reliability ranking of datasets regardless of the experiment -- a property we term experiment agnosticism. Experiments on synthetic noise models, CIFAR-10 embeddings, and real employment data demonstrate that the Gram determinant score effectively captures data quality across diverse observation processes. |
| title | Data Reliability Scoring |
| topic | Machine Learning Computer Science and Game Theory |
| url | https://arxiv.org/abs/2510.17085 |