Causal Rating for AI Sentiment Analysis Bias
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Solution Overview
Problem
It is challenging for AI users to assess the trustworthiness of AI services, particularly in sentiment analysis systems, due to model uncertainty and bias related to gender and race, which can lead to liability and risk for developers reusing APIs or data without proper evaluation.
Innovation Solution
A causal-based rating methodology is introduced to evaluate the trustworthiness of AI services by creating a causal model that accounts for protected variables like gender and race, using Deconfounding Impact Estimation (DIE) and Weighted Rejection Score (WRS) to assess the impact of these variables on sentiment analysis outputs, applicable to both primitive and composite AI systems.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If sentiment analysis systems are deployed without bias evaluation, then deployment speed is improved, but fairness and reliability deteriorate due to model uncertainty and bias related to gender and race
Solution Approach 1:
The patent implements preliminary bias evaluation through automated testing frameworks that assess sentiment analysis systems before deployment. The system performs pre-deployment fairness checks by evaluating model outputs across different demographic groups (gender, race) to identify and mitigate potential biases, ensuring reliable and fair AI services are deployed with confidence.
2Reliability
If comprehensive bias testing is performed on AI services, then fairness and reliability are improved, but testing complexity and time requirements increase
Solution Approach 1:
The patent implements self-service bias evaluation where the testing framework automatically generates test cases, executes evaluations, and produces fairness reports without requiring extensive manual intervention. The system autonomously handles the complexity of comprehensive bias testing by integrating evaluation metrics directly into the deployment pipeline, making rigorous fairness assessment accessible without proportionally increasing operational complexity.
3Productivity
If developers reuse APIs and data without proper evaluation, then development efficiency is improved, but liability and risk increase due to unknown model behavior and bias
Solution Approach 1:
The patent implements feedback mechanisms that provide developers with fairness evaluation results and model behavior insights when reusing APIs and data. The system delivers automated reports on potential biases, performance metrics across different groups, and risk assessments, enabling developers to make informed decisions about third-party AI services while maintaining accountability and reducing liability through transparent evaluation.
Data Source
AI summary
A method and system relates to assigning ratings (i.e., labels) to convey the trustability of AI systems grounded in its cause-and-effect behavior of significant inputs and outputs of the AI. Sentiment Analysis Systems (SASs) are data-driven Artificial Intelligence (AI) systems that, given a piece of text, assign a score conveying the sentiment and emotion intensity. The present disclosure uses the approach that protected attributes like gender and race influence the output (sentiment) given by SASs or if the sentiment is based on other components of the textual input, e.g., chosen emotion words. The presently disclosed rating methodology assigns ratings at fine-grained and overall levels, to rate SASs grounded in a causal setup, and provides an open-source implementation of both SASs—two deep-learning based, one lexicon-based, and two custom-built models—for this rating implementation. This allows users to understand the behavior of SAS in real-world applications.