公开(公告)号：US20240062079A1

公开(公告)日：2024-02-22

申请号：US18448369

申请日：2023-08-11

Applicant: UNIVERSITY OF SOUTH CAROLINA

Inventor： BIPLAV SRIVASTAVA ,  KAUSIK LAKKARAJU ,  MARCO VALTORTA

IPC: G06N5/022

CPC classification number: G06N5/022

Abstract: 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.