Beyond Explaining Predictions: Logic-Based Explanations for Confidence in Machine Learning Models
arXiv:2606.10347v1 Announce Type: new Abstract: Machine learning is increasingly used in critical domains, where both predictions and their associated confidence levels influence important decisions. To enhance transparency in such scenarios, it is important to understand why a model is confident or uncertain about its predictions. Recent logic-based approaches provide abductive explanations, minimal subsets of features sufficient to preserve the predicted class, with correctness guarantees. However, these methods focus solely on classification behavior and may produce explanations that cover
The increasing deployment of AI in critical domains necessitates greater transparency and trustworthiness, pushing research towards explaining not just predictions but also confidence levels.
As AI models influence high-stakes decisions, understanding the 'why' behind their confidence or uncertainty is crucial for regulatory compliance, risk management, and broader societal acceptance.
This research introduces methods to provide logic-based explanations for model confidence, moving beyond basic classification explanations and offering deeper insights into AI decision-making.
- · AI explainability researchers
- · High-stakes AI domain users (e.g., healthcare, finance)
- · Regulatory bodies
- · AI platform providers
- · Opaque AI systems
- · Users distrustful of AI
- · Developers neglecting explainability
Increased adoption of explainable AI techniques across sensitive applications becomes feasible.
New standards and regulations specifically referencing confidence-level explanations for AI models may emerge.
Public trust in AI systems improves, accelerating their integration into daily life and critical infrastructure, possibly leading to 'AI agents' taking on more complex autonomous tasks.
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Read at arXiv cs.LG