ML Filters for E-commerce Content Moderation
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Solution Overview
Problem
E-commerce sites face challenges in maintaining the quality of listings and efficiently moderating reports, with false positives often leading to increased workload for moderators and potential misclassification of violations.
Innovation Solution
Implementing a machine-learning (ML) model to automatically moderate listings and reports on e-commerce sites, which generates classification values and priority scores for Terms of Service (ToS) violations, and updates based on feedback to reduce false positives through a cyclical feedback loop involving human moderators.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual moderation is used, then accuracy of ToS violation detection is high, but productivity is low and false positive reports increase workload
Solution Approach 1:
The moderation system is segmented into multiple stages: automated ML-based initial filtering, human review for confirmed violations, and feedback loops for model improvement. This divides the workload between automated systems (handling routine cases) and human moderators (handling complex cases), thereby improving overall productivity while maintaining accuracy through human oversight.
Solution Approach 2:
The system implements feedback mechanisms where human moderators review and correct false positives, which then feed back into training the ML model. This continuous feedback loop improves the model's accuracy over time, reducing false positives and allowing moderators to focus on more complex cases, thus improving both productivity and measurement precision.
2Productivity
If automated filtering is implemented, then productivity is improved, but reliability decreases due to false positives
Solution Approach 1:
The system uses feedback from human moderators who review and correct false positives to continuously improve the ML model's reliability. This feedback loop ensures that the model learns from its mistakes and improves its classification accuracy over time, maintaining high reliability while preserving the productivity benefits of automation.
Solution Approach 2:
Human moderators act as intermediaries between the automated filtering system and final decision-making. They review automated classifications, correct false positives, and provide feedback that refines the model. This intermediary role allows the system to maintain high productivity through automation while ensuring reliability through human verification.
3Reliability
If more thorough review is conducted, then reliability of violation detection is improved, but loss of time increases
Solution Approach 1:
The review process is segmented into automated initial screening (which handles the majority of cases quickly) and selective human review (for complex or uncertain cases). This segmentation allows the system to maintain high reliability by reviewing all cases while reducing time loss by using automated filtering for routine cases that can be quickly processed.
Solution Approach 2:
The ML model performs preliminary action by pre-filtering and pre-classifying cases before they reach human moderators. This preliminary processing handles straightforward cases automatically, reducing the time human moderators need to spend on routine reviews while maintaining reliability through the model's accurate classification of violation types.
Data Source
AI summary
Embodiments described herein include moderating listings on an e-commerce site automatically and reporting to moderators. False positive reporting may be rejected using algorithms and filtering of reporting may be handled. Methods for moderating terms of service (ToS) violations may include receiving indications of a ToS violation, generating values corresponding to the ToS violation based on a machine learning (ML) model and the indications of the ToS violation. The vales may be evaluated to determine an actual ToS violation or false positives. The ML model may be updated based on the values and the indications of a ToS violation.