Skill Validation via ML and Member Queries
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Solution Overview
Problem
Online connection networks face challenges in accurately validating member skills, as members often misrepresent their skills, and existing methods are costly and inefficient for determining skill levels, leading to reduced network value due to inaccurate representations and low response rates from members queried for validation.
Innovation Solution
The system generates user interfaces to query members about their skills, uses a golden dataset to train machine learning models for skill validation, and determines relevant members to query, optimizing response rates and accuracy by selecting qualified evaluators.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If members are queried for skill validation, then skill verification accuracy is improved, but response rate decreases
Solution Approach 1:
Members self-endorse their own skills and competencies in their profiles, eliminating the need for external validation queries. This self-service approach maintains high response rates while still achieving skill verification through the machine learning model's assessment of self-proclaimed skills against network data patterns.
Solution Approach 2:
The machine learning model acts as an intermediary that validates skills by analyzing endorsements from connections and network activity patterns rather than directly querying members. This intermediary validation mechanism maintains accuracy while preserving member engagement and response rates.
2Measurement precision
If manual verification methods are used to determine skill levels, then accuracy is improved, but cost increases
Solution Approach 1:
Manual verification processes are replaced with an automated machine learning model that analyzes endorsement data, profile information, and network activity patterns. This substitution eliminates the need for human reviewers while maintaining or improving accuracy through systematic data-driven assessment.
Solution Approach 2:
The system creates a digital representation of skill validation through machine learning predictions based on copied and analyzed endorsement data from member connections. This digital copy validation process replaces expensive manual verification while preserving the essential validation function.
3Ease of operation
If members misrepresent their skills, then profile completeness is improved, but skill representation accuracy deteriorates
Solution Approach 1:
The machine learning model provides feedback by analyzing patterns in self-declared skills against actual network behavior and endorsement data. This feedback mechanism identifies discrepancies between claimed and demonstrated skills, enabling the system to weight and validate skill representations more accurately while maintaining profile completeness.
Solution Approach 2:
The system performs preliminary validation of self-declared skills by analyzing endorsement patterns and network activity before the skills are fully trusted or weighted in the profile. This preliminary assessment maintains ease of profile creation while ensuring accuracy through pre-validation of skill claims.
Data Source
AI summary
Apparatuses, computer readable medium, and methods are disclosed for verifying skills of members of an online connection network. The apparatus, computer readable medium, and methods may include a method including responding to a first member of the online connection network indicating a skill possessed by the first member by selecting a skill verification user interface (UI) to present to a second member of the online connection network where the first member and the second member are connected via the online connection network. The method may further include presenting the skill verification UI to the second member, where the skill verification UI presents an indication of the first member, an indication of the skill, and a query regarding a competence level of the skill possessed by the first member. The method may further include receiving a response to the query and determining a skill validation value of the skill for the first member based on the response and a machine learning model.