arXiv:2606.08800v1 Announce Type: new Abstract: In high-stakes settings such as brand compliance, clinical care, and content moderation, machine learning cannot be deployed as opaque oracles: practitioners inspect the features driving model decisions, and models must leverage the expert documentation governing these domains. In practice, the data arrives as unstructured content, and features extracted from it must be interpretable, discriminative, and aligned with what experts consider important. Existing methods fall short: they target tabular inputs, lack demonstrated expert alignment, and c
The increasing complexity and opacity of machine learning models necessitate better alignment with human understanding, particularly in critical sectors where explainability and accountability are paramount.
This research addresses the core challenge of integrating expert knowledge into automated feature engineering, which is crucial for deploying AI responsibly and effectively in high-stakes environments.
The ability to 'self-evolve' features grounded in expert understanding could lead to more robust, interpretable, and trustworthy AI systems, moving beyond black-box deployments.
- · AI developers
- · Compliance officers
- · Healthcare providers
- · Content moderation platforms
- · Opaque AI systems
- · Traditional feature engineering methods
- · Industries resistant to explainable AI
Machine learning models become more trustworthy and interpretable, especially in regulated industries.
Increased adoption of AI in sectors previously hesitant due to concerns about accountability and understanding.
New regulatory frameworks may emerge, or existing ones adapted, to mandate expert-aligned, interpretable AI systems.
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Read at arXiv cs.AI