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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/2502.14581 |
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| _version_ | 1866916731116584960 |
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| author | Rodemann, Julian Arias, Esteban Garces Luther, Christoph Jansen, Christoph Augustin, Thomas |
| author_facet | Rodemann, Julian Arias, Esteban Garces Luther, Christoph Jansen, Christoph Augustin, Thomas |
| contents | Empirical human-AI alignment aims to make AI systems act in line with observed human behavior. While noble in its goals, we argue that empirical alignment can inadvertently introduce statistical biases that warrant caution. This position paper thus advocates against naive empirical alignment, offering prescriptive alignment and a posteriori empirical alignment as alternatives. We substantiate our principled argument by tangible examples like human-centric decoding of language models. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2502_14581 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | A Statistical Case Against Empirical Human-AI Alignment Rodemann, Julian Arias, Esteban Garces Luther, Christoph Jansen, Christoph Augustin, Thomas Artificial Intelligence Computation and Language Machine Learning Other Statistics Empirical human-AI alignment aims to make AI systems act in line with observed human behavior. While noble in its goals, we argue that empirical alignment can inadvertently introduce statistical biases that warrant caution. This position paper thus advocates against naive empirical alignment, offering prescriptive alignment and a posteriori empirical alignment as alternatives. We substantiate our principled argument by tangible examples like human-centric decoding of language models. |
| title | A Statistical Case Against Empirical Human-AI Alignment |
| topic | Artificial Intelligence Computation and Language Machine Learning Other Statistics |
| url | https://arxiv.org/abs/2502.14581 |