arXiv:2606.01723v1 Announce Type: new Abstract: Real-world regression often exhibits shortcuts: attributes that are spuriously correlated with continuous targets in training, yet unreliable under deployment shifts; regressing targets using such shortcuts may fail catastrophically at test time. Existing studies on spurious correlations focus primarily on classification, where labels are categorical and groups are naturally defined. However, many real-world tasks require continuous prediction, where hard label boundaries or discrete group-label pairs do not exist. We define Deep Spurious Regress
The increasing sophistication and deployment of AI models in real-world applications necessitate a deeper understanding of their failure modes, particularly in regression tasks where continuous predictions are critical.
This research highlights a critical vulnerability in real-world AI deployment: the tendency of models to rely on spurious correlations, which can lead to catastrophic failures in dynamic environments and undermine trust in AI systems.
The focus for developing robust AI systems must expand beyond classification to address spurious correlations in continuous prediction a new area of research and development for AI safety and reliability.
- · AI safety researchers
- · Developers of robust AI systems
- · Industries relying on continuous AI predictions
- · Developers of brittle AI models
- · Companies deploying un-audited AI systems
- · Sectors with high deployment shifts
Further research and development into methods for identifying and mitigating spurious correlations in deep regression models will intensify.
New regulatory frameworks may emerge to mandate robustness testing for AI systems, especially those making continuous predictions in critical applications.
The perceived reliability of AI could be significantly impacted, leading to either greater adoption based on trust or increased caution if these issues are not adequately addressed.
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Read at arXiv cs.LG