arXiv:2505.20634v2 Announce Type: replace Abstract: Concept shift occurs when the distribution of labels conditioned on the features changes between domains, which can make even a well-tuned ML model miscalibrated on a new domain. Identifying these shifted features provides unique insight into how feature-label relationships differ between domains, considering the difference may be across a scientifically relevant dimension, such as time, disease status, population, etc. In this paper, we propose SGShift, a method for attributing performance degradation under concept shift in tabular data to a
The proliferation of ML models in diverse real-world applications highlights the urgent need for tools to address and understand performance degradation due to evolving data distributions.
Understanding and explaining concept shift is crucial for maintaining the trustworthiness, reliability, and safety of AI systems deployed in dynamic environments, impacting adoption and regulatory compliance.
The introduction of methods like SGShift provides AI developers with systematic tools to diagnose and attribute performance shortfalls in ML models to specific feature changes in tabular data, moving beyond anecdotal observation.
- · AI developers
- · ML model deployers
- · AI-reliant industries
- · Models lacking explainability
- · AI systems in highly dynamic environments without robust monitoring
Improved model robustness and interpretability will accelerate the deployment of AI in mission-critical applications.
Enhanced explainability will foster greater public and regulatory trust in AI systems, potentially easing adoption barriers.
The ability to quickly identify and adapt to concept shifts could lead to more adaptive and self-correcting AI systems that dynamically learn from environmental changes.
This signal links to a primary source. Continuum Brief monitors and indexes it as part of the live intelligence stream — we do not republish source content.
Read at arXiv cs.LG