Self-Organized Conformal Prediction: Reducing Regional Coverage Gaps with Unsupervised Group Discovery
arXiv:2606.29403v1 Announce Type: cross Abstract: Conformal prediction guarantees marginal coverage, but pooled calibration averages over heterogeneous regions and can mask regional undercoverage in safety-critical subgroups. We introduce Self-Organized Conformal Prediction (SOCP), a calibration scheme that discovers input-space groups with a Self-Organizing Map (SOM) and, at test time, draws a local calibration buffer from the query's best-matching unit (BMU) cell or a fixed grid neighborhood. The same retrieval rule applies to regression and classification tasks across tabular features and i
This research emerges as AI systems are increasingly deployed in safety-critical applications, highlighting the urgent need to address reliability and fairness for diverse user groups.
Improved conformal prediction methods that identify and mitigate regional coverage gaps are crucial for building trust in AI and preventing biased or unsafe outcomes in real-world deployments.
The ability to self-organize and adapt calibration buffers based on input-space groups represents a significant step towards more robust and equitable AI systems, reducing current limitations of pooled calibration.
- · AI developers
- · Safety-critical AI applications
- · Underrepresented subgroups
- · AI ethics and bias researchers
- · AI systems with unmitigated regional biases
- · Organizations relying on simple, pooled calibration
AI models will become more reliable and fair across heterogeneous user populations.
Increased adoption of AI in sensitive domains like healthcare and finance will accelerate due to improved trustworthiness.
New regulatory standards for AI fairness and reliability could incorporate adaptive calibration techniques as a best practice.
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