arXiv:2606.02309v1 Announce Type: new Abstract: Generative models are increasingly used as priors for inverse problems, but their ability to produce realistic images creates a basic trust problem: a plausible reconstruction may be supported by the measurements, or it may be filled in by the prior along unobserved directions. This distinction is especially important in medical imaging, where acquisition operators are designed under scan-time, dose, and calibration constraints. We study generative inverse problems from a measurement-geometry perspective. The central question is whether a fixed m
This research addresses the critical and looming challenge of trustworthiness in generative AI models, which are rapidly integrating into high-stakes applications like medical imaging, necessitating robust validation methods before widespread adoption.
Ensuring the reliability and interpretability of generative AI's output is paramount, especially as these models move from research to real-world deployment where errors can have significant consequences for human health and safety.
The focus is shifting from merely generating plausible results to rigorously understanding and quantifying how generative AI's 'prior knowledge' influences the reconstruction of unobserved data, demanding new design principles for measurement and validation.
- · AI ethicists
- · Medical imaging companies
- · Regulators
- · Generative AI researchers
- · Generative AI developers ignoring trustworthiness
- · Industries with low oversight standards
Improved methodologies for validating AI-generated data will increase trust in AI applications.
Enhanced trustworthiness will accelerate AI adoption in highly regulated sectors like healthcare and defense, conditional on adherence to these new standards.
New certification and auditing industries will emerge focused on assessing the 'measurement geometry' and trustworthiness of generative AI systems.
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Read at arXiv cs.LG