An uncertainty-aware Bayesian framework for machine learning classification models: A case study in land cover classification
arXiv:2503.21510v3 Announce Type: replace Abstract: Ensuring that predictions of machine learning (ML) classification models are accompanied by uncertainty estimates is one of the main pillars of trustworthy AI. Current research in uncertainty quantification focuses mainly on epistemic uncertainty of the ML model, but rarely takes account of input measurement uncertainty, which is vital for traceability in metrology. In this work we propose a Bayesian framework for generative ML classification models that takes account of input measurement uncertainty. We take the specific case of a Bayesian q
The increasing deployment of ML models in critical applications necessitates robust uncertainty quantification, driving research focus on trustworthiness and reliability.
This development addresses a key limitation in current AI models, making their predictions more reliable and interpretable, which is crucial for high-stakes decision-making.
Machine learning classifications can now integrate both model-driven and input measurement uncertainties, enhancing the trustworthiness and traceability of AI outputs.
- · AI developers
- · Metrology sector
- · Industries relying on AI classification (e.g., healthcare, finance, defense)
- · Developers of 'black box' AI models
- · Applications with high-risk, low-transparency AI
ML models will provide more transparent and quantifiable confidence bounds for their predictions.
Increased trust in AI will accelerate its adoption in regulated and safety-critical domains.
This could lead to new regulatory frameworks for AI that mandate comprehensive uncertainty reporting.
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Read at arXiv cs.LG