Contact Center Personality Assessment With Explainable Trait Mapping
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Solution Overview
Problem
Existing AI systems for predicting agent personalities in contact centers are prone to errors and biases, lacking reliable reasoning and justification for trait classification, which affects the accuracy and fairness of personnel evaluation.
Innovation Solution
A method and system using a Machine Learning model to generate natural language justifications for agent personality traits, based on textual data from conversations, providing a mapping and qualitative labels to determine trait values with justification.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If conventional AI algorithms are used to predict agent personalities, then automation is improved, but reliability deteriorates due to errors and biases in prediction
Solution Approach 1:
The patent introduces an intermediary human reviewer who examines AI-generated personality predictions and provides final decisions. This hybrid approach maintains automation benefits while adding human judgment to improve reliability and reduce biases in personality assessment
Solution Approach 2:
The system implements feedback loops where AI predictions are continuously refined based on human reviewer corrections and actual job performance data. This feedback mechanism improves reliability by learning from errors and adjusting predictions to match real-world outcomes
2Productivity
If AI algorithms are used for personality classification, then productivity is improved, but measurement precision deteriorates due to lack of reasoning and justification
Solution Approach 1:
The patent segments the personality assessment process into distinct phases: AI generates initial predictions with supporting evidence, human reviewers verify specific traits, and final decisions are documented. This segmentation maintains productivity while improving measurement precision through targeted human review
Solution Approach 2:
Human reviewers serve as intermediaries between AI predictions and final personality classifications. They examine the AI's reasoning process and provide corrective judgments, ensuring measurement precision while maintaining overall productivity through efficient collaboration
3Measurement precision
If multi-step reasoning is required for accurate personality identification, then measurement precision is improved, but device complexity increases
Solution Approach 1:
The complex reasoning process is segmented into manageable components: the AI system handles data processing and initial analysis, while human reviewers focus on evaluating specific personality traits. This segmentation reduces perceived complexity while maintaining measurement precision through distributed cognitive processing
4Ease of operation
If AI systems provide personality predictions without justification, then ease of operation is improved, but loss of information increases due to lack of reasoning transparency
Solution Approach 1:
Human reviewers act as intermediaries who examine and validate the AI's reasoning process. They ensure that sufficient justification is provided for personality predictions while maintaining ease of operation through automated documentation and structured review protocols
Data Source
AI summary
This disclosure relates to method and system for determination of personality traits of agents in a contact center. The method includes retrieving textual data corresponding to a conversation between a first agent and a first customer. The method further includes generating a natural language justification corresponding to a set of personality traits of the first agent based on the textual data through a first Machine Learning (ML) model. The natural language justification may include one or more sentences. The one or more sentences may include a mapping of the textual data with the set of personality traits and a qualitative label associated with each of the set of personality traits. The method further includes determining a value corresponding to each of the set of personality traits of the first agent through the first ML model based on the natural language justification and the associated qualitative label.