Online Activity Correlation for Bias-Reduced Candidate Screening
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Solution Overview
Problem
The task of extracting publicly available information and correlating attributes of various text, images, and other media with individual classification factors via the Internet is difficult, labor-intensive, time-consuming, and subject to bias, particularly in hiring decisions, which can risk a company's reputation and violate federal or state laws.
Innovation Solution
A system that aggregates and analyzes data from open and closed social media and public websites using machine learning algorithms to identify positive and negative classification factors, transforming unstructured data into structured datasets, and generating reports based on user-defined criteria.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual extraction and analysis of online information is performed, then data can be gathered from various sources, but the process becomes labor-intensive and time-consuming
Solution Approach 1:
The patent replaces manual mechanical information extraction processes with automated computer-based systems that use web crawlers, APIs, and machine learning algorithms to automatically collect, extract, and analyze data from multiple online sources, eliminating the need for manual searching and significantly reducing time requirements
Solution Approach 2:
The system enables self-service automated data collection and analysis where the computer system independently performs information gathering, processing, and classification without human intervention, allowing the system to serve itself in executing the full workflow from data collection to report generation
2Loss of information
If manual analysis of social media profiles is performed, then detailed information can be obtained, but the process introduces unintentional bias against protected classes
Solution Approach 1:
The patent changes the parameters of analysis by using machine learning models that evaluate objective, job-relevant criteria while systematically excluding protected class characteristics. The system transforms subjective manual assessment into objective algorithmic evaluation based on predetermined classification factors that are legally compliant and job-related
Solution Approach 2:
The patent introduces an intermediary machine learning system that acts as a mediator between raw social media data and hiring decisions. This intermediary processes information through standardized algorithms that prevent direct human bias from influencing decisions, while still extracting meaningful candidate attributes from unstructured online data
3Quantity of substance
If comprehensive online data aggregation is performed, then more complete candidate profiles can be created, but the system complexity increases significantly
Solution Approach 1:
The patent segments the complex data aggregation system into modular functional components including web crawlers for data collection, NLP modules for text processing, machine learning models for classification, and report generation systems. Each module handles specific tasks independently, making the overall complex system manageable and maintainable through clear separation of concerns
Solution Approach 2:
The patent creates universal multi-functional components that can handle multiple types of data sources and analysis tasks. For example, the machine learning model can classify various types of online content (posts, photos, videos) across multiple classification dimensions, and the system can adapt to different social media platforms through standardized interfaces, reducing overall system complexity through reuse
4Productivity
If automated machine learning analysis is implemented, then productivity and standardization improve, but the initial system setup and data processing complexity increases
Solution Approach 1:
The patent performs preliminary actions by pre-training machine learning models with extensive datasets, pre-configuring classification schemas and evaluation criteria, and pre-establishing data pipelines before actual candidate assessment begins. This preliminary setup, while complex initially, enables rapid and standardized processing of subsequent candidates, improving long-term productivity
Solution Approach 2:
The patent uses copying by creating standardized templates and models that can be replicated across different candidate assessments. Once the machine learning model is trained and validated, it can be copied and applied to evaluate numerous candidates using the same proven methodology, ensuring consistency and reducing the need to rebuild complex processing systems for each new assessment
Data Source
AI summary
An attribute associated with an individual being screened for a category of conduct is obtained. A first set of data associated with the individual is obtained, based at least in part on the attribute, with a form of the first set of data being one of a plurality of disparate forms. A second set of data associated with the individual is obtained, based at least in part on the attribute, with a form of the second set of data being different from the form of the first set of data. The first set of data and the second set of data are transformed into a standard data structure. Contents of the standard data structure are input to an algorithm. A result from the algorithm is received that indicates that the individual is associated with the category of conduct, and the result is output to a user interface.