arXiv:2603.26539v2 Announce Type: replace-cross Abstract: How does the extent to which a model is open or closed impact the scientific inferences that can be drawn from research that involves it? In this paper, we analyze how restrictions on information about model construction and deployment threaten reliable inference. We argue that current closed models are generally ill-suited for scientific purposes, with some notable exceptions, and discuss ways in which the issues they present to reliable inference can be resolved or mitigated. We recommend that when models are used in research, potenti
The increasing prevalence of high-capacity closed-source language models in research necessitates a critical examination of their scientific utility and the implications for reliable inference.
Restricting access to model construction and deployment information fundamentally challenges the reproducibility and trustworthiness of AI-driven scientific research, impacting its credibility and future direction.
There will be increasing pressure on model developers to provide greater transparency and openness, potentially leading to new standards or requirements for AI models used in scientific endeavors.
- · Open-source AI developers
- · Academic researchers
- · Independent auditing firms
- · Scientific integrity advocates
- · Closed-source model providers
- · Research relying on opaque models
- · AI companies prioritizing secrecy over transparency
Scientific communities will develop clearer guidelines and potentially new regulatory frameworks for AI model transparency in research.
Funding bodies may mandate specific levels of model openness for research grants involving AI, shifting investment towards transparent approaches.
The definition of 'scientific rigor' in disciplines leveraging AI could fundamentally change, emphasizing transparency and auditability as core tenets.
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Read at arXiv cs.AI