Compliance-aware AI: Embedding Regulatory Logic into Machine Learning Pipelines
Year: 2025
Type: article
Abstract: This article addresses the emerging challenge of ensuring regulatory compliance in artificial intelligence systems. While traditional software relies on explicit rule-based logic to enforce compliance requirements, modern machine learning models operate on probabilistic foundations that introduce novel compliance risks. The article explores methodologies for embedding regulatory logic—including age verification, consent management, and access controls—directly into AI pipelines. Through an examination of pre-processing filters, post-processing safeguards, and runtime governance mechanisms, architectural patterns are presented that can help AI practitioners design systems that maintain compliance even when working with dynamic or black-box models. This framework offers practical guidance for AI product teams operating in highly regulated domains.
Author: Heena Piratiyath
Institution: The University of Texas at Austin
Language: English
Subfield: Safety Research
Field: Social Sciences
Domain: Social Sciences
Open Access status: green
OpenAlex ID: W7080001294
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