arXiv:2606.00563v1 Announce Type: new Abstract: Selection bias is a common and often unavoidable aspect of real-world data that challenges the generalizability of machine learning models. When models trained on biased data are deployed in the broader target population, poor model generalization may lead to real harm, particularly in high-risk settings such as healthcare. This risk highlights the need for practitioners to reliably assess model generalizability prior to deployment. However, existing methods for predicting model performance rely on unrealistic access to the target distribution or
The increasing deployment of AI in high-stakes fields like healthcare, coupled with growing awareness of bias in ML models, makes this research particularly timely.
Reliably assessing model generalizability before deployment is crucial for preventing harm and building trust in AI, particularly in sensitive sectors like medicine.
This research provides a practical method for quantifying the upper bound of selection bias effects, allowing practitioners to better understand and mitigate risks without unrealistic data access.
- · Healthcare AI developers
- · Patients
- · Regulatory bodies
- · AI ethics research
- · Developers ignoring bias
- · AI models with unaddressed bias
Improved reliability and safety of medical AI applications.
Increased adoption and trust in AI tools within clinical settings due to better generalizability guarantees.
Potential for new regulatory standards or certifications based on bias quantification methods like the one presented.
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Read at arXiv cs.LG