Digital Component Policy Enforcement with Confidence-Based Label Propagation
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Solution Overview
Problem
Manual review of digital components for policy violations is time-consuming and computationally expensive, and existing automatic systems require significant processing power and network overhead.
Innovation Solution
A method involving filtering candidate digital components based on content and content provider similarity, using a machine learning model to predict policy violations, and propagating labels to similar components, reducing the need for full review.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual review of each digital component is performed, then policy violation detection accuracy is improved, but time consumption and labor intensity increase
Solution Approach 1:
The patent segments the review process into multiple stages: an automatic classification system performs initial screening, identifies potential violations, and flags components for human review. This segmentation allows human reviewers to focus only on components that require manual attention rather than reviewing every component individually, thus reducing time consumption while maintaining detection accuracy.
Solution Approach 2:
The patent introduces an automatic classification system as an intermediary between the digital components and human reviewers. This intermediary system performs preliminary analysis, scores components based on policy criteria, and prioritizes which components need human review. The intermediary handles the bulk of the screening work, reducing the time and effort required from human reviewers while maintaining reliable detection through human oversight of critical cases.
2Productivity
If automatic classification system reviews all digital components, then productivity is improved, but computational cost and processing power requirements increase
Solution Approach 1:
The patent applies local quality by differentiating the level of review based on component characteristics. Instead of applying full manual review to all components uniformly, the system applies automatic classification to all components while reserving manual review for only those components that the automatic system identifies as potentially violating policy. This localized application of review resources improves productivity for the bulk of components while maintaining high detection accuracy for problematic content.
Solution Approach 2:
The patent implements partial action by having the automatic classification system perform comprehensive screening of all components, then selectively applying manual review only to the subset of components flagged as potential violations. This partial manual review approach increases overall productivity by avoiding the computational and temporal cost of full manual review for all components, while maintaining sufficient detection accuracy through targeted human oversight of high-risk cases.
Data Source
AI summary
The technology is generally directed to determining whether candidate digital components violate a policy and using the determination to propagate policy labels. Candidate digital components may be filtered such that only a subset of the candidate digital components is provided to a machine learning model for further policy review. The machine learning model may provide a confidence score associated with the policy violation prediction. The policy violation prediction may be “violates policy” or “does not violate policy.” A label corresponding to the policy violation prediction may be associated with the digital component. The confidence score may be used when determining whether to use the policy violation prediction to propagate labels to other digital components. The labels may be propagated using a seed based enforcement system or a neighborhood based propagation system.