Bias Leaves a Gradient Trail: Label-Free Bias Identification via Gradient Probes on Concept Decompositions
arXiv:2605.28780v1 Announce Type: cross Abstract: Vision classifiers can exploit spurious correlations, achieving high in-distribution accuracy yet failing under distribution shift. Existing approaches to bias mitigation and analysis often depend on curated datasets, spurious-attribute or group labels, or retraining, which may be infeasible once a model is deployed or the relevant bias is unknown. We present a bias-label-free, post-hoc method for identifying spurious concepts in frozen vision models, relying only on standard class labels from a held-out audit dataset. For each target class, we
The proliferation of AI models in critical applications necessitates robust methods for identifying and mitigating bias, especially as models are deployed and updated frequently.
This research provides a crucial tool for ensuring fairness and reliability in AI systems without requiring extensive, costly, and often unavailable re-labeling or retraining.
Bias identification in deployed vision models can now be conducted 'post-hoc' and 'label-free', reducing dependency on human annotation and opening new avenues for auditing AI models.
- · AI ethicists
- · Model auditing platforms
- · Regulators
- · Companies deploying AI at scale
- · Developers relying on 'black box' AI
- · Entities resistant to AI transparency
Easier and more widespread detection of biases in deployed AI models, leading to improved model fairness.
Increased pressure on AI developers to build less biased models from the outset, or to integrate post-hoc bias analysis into their MLOps pipelines.
The development of new regulatory frameworks that mandate bias auditing using methods like this, impacting AI certification and deployment.
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Read at arXiv cs.LG