Entity Attribute Modeling for Attrition and Promotion Bias Detection
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Solution Overview
Problem
Organizations face challenges in identifying and addressing systemic barriers to career advancement, such as 'broken rungs' and 'glass ceilings', which hinder talented employees from reaching their full potential, and lack tools to understand and mitigate systemic bias affecting promotions and retention.
Innovation Solution
Systems and methods that generate metrics and scores to identify career advancement barriers, predict attrition, and optimize employee composition by analyzing diverse data sources, applying mathematical transformations to generate promotion velocity, attrition, and flight risk scores, and providing visualizations for informed decision-making.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional career advancement systems are used, then organizational structure is maintained, but systemic barriers prevent talented employees from advancing
Solution Approach 1:
The patent segments the career advancement process into measurable components by creating separate velocity models for different employee groups (e.g., demographic groups, job families, locations). This allows systematic identification of barriers at specific segments of the organizational structure without requiring complete structural overhaul.
Solution Approach 2:
The patent introduces an intermediary analytical system that acts as a mediator between traditional organizational structures and fair advancement outcomes. The velocity model and visualization tools serve as intermediaries that identify and communicate barriers, enabling leadership to address systemic issues without directly altering the underlying organizational structure.
2Loss of information
If leadership lacks insight into career barriers, then management autonomy is preserved, but broken rungs and glass ceilings remain undetected
Solution Approach 1:
The patent implements a feedback mechanism through visualization tools that provide leadership with actionable insights into career advancement barriers. The system continuously monitors employee velocity metrics and feeds this information back to leadership, enabling data-driven decisions to address broken rungs and glass ceilings while maintaining management autonomy.
3Measurement precision
If diverse data sources are analyzed, then prediction accuracy improves, but data processing complexity increases
Solution Approach 1:
The patent merges multiple diverse data sources (performance reviews, demographic information, promotion history, survey data) into a unified velocity model framework. By combining these data streams through standardized mathematical transformations, the system achieves high prediction accuracy while managing complexity through integrated processing rather than separate analyses.
Data Source
AI summary
At least one processor configured to perform operations including receiving data from a plurality of disparate data sources, the data including a plurality of variables associated a plurality of entities and characteristics of the entities; extracting one or more associations from the data, wherein each of the one or more associations includes one or more probabilistic distributions based on a relationship between the performance metrics and the entities and their positions; generating, based on the associations, a flight index for each of the entities; wherein the flight index is a statistical measure of a likelihood that an entity will leave the organization; generating a performance index to each of the entities; identifying, based on a comparison between the flight index and the performance index, a flight probability the entities being higher than a threshold flight probability; implementing, based on the identification, policy changes in the organization.