Crowdsourced Trust Score Calculation via Segmented Data Sources
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Solution Overview
Problem
Existing systems lack an effective method to quantify and measure trust between entities, as trust is influenced by various factors and can change over time, making it elusive to capture and rely heavily on subjective preferences and experiences.
Innovation Solution
A system and method for calculating trust scores that incorporate various data sources, including verification data, network connectivity, ratings, demographics, and transaction history, with the option to include crowdsourced information, to provide a comprehensive and dynamic assessment of trustworthiness.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If trust is measured using multiple factors and data sources, then measurement precision improves, but device complexity increases
Solution Approach 1:
The trust scoring system is segmented into multiple independent data sources (verification data, network connectivity, ratings data, group/demographic information, location data, transaction history) that can be independently collected and processed, then combined to form a comprehensive trust score. This segmentation allows the system to maintain high measurement precision while managing complexity through modular architecture.
Solution Approach 2:
The system performs multiple functions using a unified trust score calculation framework: it evaluates trustworthiness across different contexts (personal interactions, business transactions, lending decisions), adapts to various data source combinations, and serves diverse user needs. This multi-functionality reduces overall system complexity by using a single versatile platform rather than multiple specialized systems.
2Reliability
If crowdsourced information is incorporated into trust scoring, then reliability improves, but loss of time increases
Solution Approach 1:
The system performs preliminary actions by pre-collecting and storing verification data, network connectivity information, ratings data, and other trust-relevant information in databases before they are needed for trust scoring. This advance preparation reduces the time required during actual trust score calculations while maintaining reliability through comprehensive pre-gathered data.
Solution Approach 2:
The system incorporates feedback loops where users can provide ratings and verification information about entities, and this feedback is continuously integrated into the trust scoring system. The feedback mechanism allows the system to update trust scores dynamically based on new information while maintaining reliability through continuous validation and aggregation of crowd-sourced data.
Data Source
AI summary
Systems and methods are described herein for calculating trust score based on crowdsourced information. The trust score may reflect the trustworthiness, reputation, membership, status, and/or influence of an entity in a particular community or in relation to another entity. The trust score may be calculated based on data received from a variety of public and private data sources, including “crowdsourced” information. For example, users may provide and/or comment on attributes, characteristics, features, or any other information about another user. These inputs may serve to both validate the available data as well as provide additional information about the user that may not be otherwise available. The participation of the “crowd” may form a type of validation in itself and give comfort to second-order users, who know that the crowd can spectate and make contributions to the attributes, characteristics, features, and other information.