arXiv:2606.25743v1 Announce Type: new Abstract: Foundation models are often used as fixed black-box predictors for downstream tasks with limited labeled data, but their predictions may be biased and unsafe to trust blindly. We study this setting through black-box assisted nonparametric regression: a learner observes labeled samples and can query a fixed predictor $f_0$, while the target $f^*$ is close to $f_0$ in $L_2(P_X)$ up to an unknown radius $\delta$. We give a finite-sample minimax characterization showing a phase transition at $\delta_c(n) \asymp n^{-\beta/(2\beta+d)}$, with leading ri
This research is emerging as foundation models become ubiquitous in deployed AI systems, highlighting the need for robust methods to integrate and evaluate them safely.
A strategic reader should care because understanding the limitations and biases of black-box foundation models is critical for reliable AI application development and risk management.
The research provides a theoretical framework to quantify the performance and safety boundaries when using black-box AI models for downstream tasks, formalizing inherent risks and potential mitigation strategies.
- · AI researchers
- · AI safety auditors
- · Developers of AI applications
- · Companies relying on unvalidated black-box AI
- · AI systems with unmitigated biases
This study offers a quantitative method to assess the trustworthiness and bias handling of large, pre-trained AI models in practical scenarios.
Improved understanding of black-box model limitations could lead to the development of new tools for bias detection and correction, enhancing overall AI reliability.
The insights gained may influence future AI regulatory frameworks, requiring explicit demonstrations of robustness and safety for applications built on black-box foundation models.
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Read at arXiv cs.LG