From Explicit Elements to Implicit Intent: A Predefined Library for Auditable Behavioral Inference
arXiv:2606.11207v1 Announce Type: cross Abstract: We present SemantiClean, a modular framework for extracting structured semantic signals from e-commerce session data and driving pluggable inference targets including purchase intent, customer segmentation, and product affinity through a shared element library. Unlike conventional end-to-end predictors that optimise solely for accuracy, SemantiClean prioritises auditability, structural governance, and sigma=0 reproducibility, explicitly trading marginal predictive gains for element-level transparency and defensible decision trails. Built upon t
The increasing complexity and opacity of AI systems across various applications necessitate a focus on auditability and transparency, especially in critical commercial domains.
This development addresses a growing demand for explainable AI, especially in e-commerce, where ethical considerations, regulatory pressure, and the need for defensible decision-making are paramount.
The focus shifts from purely predictive accuracy to incorporating auditability and reproducibility directly into AI system design, prioritizing transparency over marginal performance gains.
- · E-commerce companies (seeking compliance)
- · AI ethics and auditing firms
- · Regulators
- · Consumers (via increased transparency)
- · Black-box AI solution providers
- · Companies prioritizing only 'accuracy at all costs'
Increased adoption of auditable AI frameworks across various industries beyond e-commerce.
New standards and certifications emerge for 'auditable AI' impacting procurement and development lifecycles.
Legislation mandate specific levels of AI transparency, making 'black-box' models commercially unviable in certain regulated sectors.
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Read at arXiv cs.CL