Content Evaluation System Using ML Quorum Consensus
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Solution Overview
Problem
Current systems lack effective real-time detection and blocking mechanisms for malicious digital content attributed to entities, leading to potential reputation damage and loss of business opportunities due to the inability to align campaign communications and track message parameters accurately.
Innovation Solution
A system utilizing machine learning models and respondent services to evaluate user-attributable content in real-time, determining a quorum consensus for approval or blocking, with features like autoscore validation, feedback loops, and respondent quality scoring to assess and modify content before distribution.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If real-time evaluation and blocking mechanisms are implemented, then malicious content detection capability is improved, but system complexity increases
Solution Approach 1:
The system segments the content evaluation process into multiple independent respondent services, each evaluating content based on specific criteria. This modular approach improves detection reliability through diverse perspectives while managing system complexity by organizing evaluation functions into separate, manageable units that can operate independently.
Solution Approach 2:
The system implements feedback loops where respondent evaluations are aggregated, consensus is determined, and blocking decisions are made based on this feedback. The feedback mechanism enables continuous improvement of detection accuracy while maintaining systematic control over the complexity through structured information flow and decision protocols.
2Measurement precision
If multiple respondent services are used for content evaluation, then evaluation accuracy is improved, but processing time increases
Solution Approach 1:
The system performs preliminary actions by pre-configuring multiple respondent services with specific evaluation criteria and thresholds before content evaluation begins. This preparation enables parallel processing of content evaluations, improving accuracy through multiple assessments while reducing processing time by avoiding sequential evaluation delays.
Solution Approach 2:
The system employs partial action by determining consensus based on a quorum threshold rather than requiring all respondent services to complete evaluation. This approach improves evaluation accuracy by incorporating multiple perspectives while reducing processing time by allowing the system to proceed once sufficient consensus is achieved, without waiting for all respondents.
3Object-affected harmful factors
If content is blocked based on respondent consensus, then reputation damage is prevented, but content distribution efficiency decreases
Solution Approach 1:
The system changes parameters by adjusting the quorum threshold and evaluation criteria based on content type, entity importance, and risk level. This enables dynamic blocking decisions that prevent reputation damage for high-risk content while maintaining distribution efficiency for low-risk content, adapting the blocking mechanism to specific contextual parameters rather than applying uniform restrictions.
Data Source
AI summary
An online portal receives digital content from a user device. The online portal is communicably coupled to a computer server hosting an online media service in the public or non-public domain. The user device is associated with an online account on the online media service. Based on the digital content, at least one requirement associated with the online account is identified. One or more respondent services are determined satisfy the requirement. By each respondent service, the digital content is processed using a respective machine learning model trained, based on user-attributable content, to generate a respondent evaluation. A quorum of respondent evaluations is generated. The quorum of respondent evaluations is determined to achieve a respondent consensus. Responsive to determining that the respondent consensus satisfies an approval condition, the digital content is sent from the online portal to the computer server for posting the digital content on the online media service.