Contact Center Agent Personality Assessment With Explainable ML
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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 assessment.
Innovation Solution
A method and system using a Machine Learning model to generate natural language justifications for agent personality traits based on conversation transcripts, providing a mapping of textual data with qualitative labels and determining trait values through an advanced NLP algorithm.
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 and accuracy deteriorate due to errors, biases, and lack of reasoning justification
Solution Approach 1:
The patent introduces an Explainable AI (XAI) system as an intermediary between the conventional AI prediction model and the final personality assessment. This XAI layer generates natural language justifications that explain the reasoning behind each personality trait prediction, thereby maintaining automation while improving reliability and reducing biases through transparent, interpretable decision-making processes.
Solution Approach 2:
The patent segments the personality prediction process into two distinct components: (1) the conventional AI model that generates personality predictions, and (2) the XAI module that provides reasoning justifications. This segmentation allows each component to perform its specialized function independently, with the XAI layer specifically addressing the reliability and interpretability issues without compromising the automation capabilities of the original AI model.
2Productivity
If AI models are trained to recognize patterns in conversation data, then productivity is improved, but measurement precision deteriorates due to misjudgment of nuanced personality traits
Solution Approach 1:
The patent implements a feedback mechanism where the XAI system continuously generates and refines natural language justifications based on the AI model's predictions. This feedback loop allows the system to learn from its own reasoning processes, improving measurement precision over time while maintaining high productivity through automated pattern recognition and justification generation.
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.