Candidate Profile Screening via Crowdsourced Subset Validation
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Solution Overview
Problem
Conventional candidate profile verification methods are prone to errors and high costs, as they rely on physical evaluations and completeness checks, which are inefficient in verifying the authenticity of information provided by candidates, especially in preventing identity theft and ensuring accurate identity verification.
Innovation Solution
A processor-implemented method and system that divides candidate profiles into subset-profiles, maps them with target-profile information using a crowdsourcing platform, and computes an aggregated weightage based on responses from a set of targets to validate the authenticity of candidate profile information, utilizing learning algorithms to select suitable targets for verification.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If physical evaluation and completeness check methods are used for candidate profile verification, then the verification process can be conducted with conventional methods, but the accuracy and reliability of identity verification deteriorates due to errors and inability to prevent identity theft
Solution Approach 1:
The patent introduces a crowdsourcing platform as an intermediary between the authority and the candidate. Multiple independent targets (crowd members) verify subset-profiles of the candidate profile, providing corroborative evidence that enhances verification accuracy and prevents identity theft through distributed validation rather than relying on single-point physical evaluation
Solution Approach 2:
The candidate profile is divided into multiple subset-profiles (e.g., personal information, educational information, employment information) that are independently verified by different targets on the crowdsourcing platform. This segmentation allows comprehensive verification through multiple independent checks, improving both reliability and measurement precision
2Productivity
If physical evaluation methods are used for candidate profile verification, then the verification can be performed with traditional processes, but the time consumption and cost increase due to inefficient manual verification
Solution Approach 1:
The system enables targets (crowd members) to voluntarily perform verification tasks by evaluating subset-profiles in their area of expertise. This self-service approach distributes the verification workload across many participants, dramatically increasing productivity while reducing the time required compared to centralized manual verification
Solution Approach 2:
The candidate profile is pre-divided into subset-profiles with specific evaluation criteria defined before the verification process begins. This preliminary structuring allows targets to quickly assess relevant information without requiring comprehensive manual review, reducing verification time while maintaining thoroughness
3Reliability
If conventional verification methods are used, then the process can be completed with traditional approaches, but the cost increases due to expensive physical evaluation processes
Solution Approach 1:
Instead of requiring physical presence and original document verification, the system uses digital copies of profile information submitted by the candidate and verifies these through electronic evaluation by multiple targets. This copying approach maintains verification trustworthiness through multiple independent checks while eliminating the costs associated with physical evaluation processes
Solution Approach 2:
The verification process transitions from physical parameters (in-person meetings, original documents) to digital parameters (electronic submissions, digital evaluations). This parameter change reduces verification costs while maintaining or improving reliability through the addition of multiple independent verification points
Data Source
AI summary
This disclosure relates generally to screening candidate profile, and more particularly to system and method for screening candidate profile. In one embodiment, the method includes dividing a candidate profile into a plurality of subset-profiles having subset-profile information of attributes of the candidate profile. The subset-profile information is mapped with target-profile information of a plurality of targets to identify a set of targets capable of screening the subset-profile information. Queries are provisioned to the set of targets for performing screening, and responses to queries are received from therefrom. An aggregated weightage associated with the responses is computed based at least on an extent of matching of the responses and a confidence score associated with each target of the set of targets. The aggregated weightage is compared with a threshold value of weightage to identify the subset-profile information as one of a valid information and an invalid information.