Summary: A pioneering artificial intelligence system successfully identifies violations of social norms. Using GPT-3, zero-shot text classification, and automatic rule discovery, the system classifies social emotions into ten major types. Analyze the written situations and accurately determine whether they are good or bad based on these categories.
This initial study offers promising evidence that the approach can be expanded to encompass more social norms.
Main aspects:
- The AI system uses ten categories of social emotions to identify violations of social norms.
- The system was tested on two large datasets of short texts, validating the models.
- This preliminary work, funded by DARPA, is seen as a significant step in improving cross-cultural linguistic understanding and situational awareness.
Source: Ben-Gurion University of the Negev
A researcher at Ben-Gurion University of the Negev has designed an artificial intelligence system that identifies violations of social norms.
The DARPA-funded project is one of the first to address the automatic identification of violations of social norms. While there are many social norms around the world, violating social norms boils down to a few general categories.
The professor. Yair Neuman and his engineer Yochai Cohen built the system using GPT-3, zero-shot text classification, and automatic rule discovery. The system used a binary of ten social emotions as categories.
DARPA commissioned the Computational Cultural Understanding (CCU) program to create cross-cultural language understanding technologies to improve a Department of Defense operator’s situational awareness and interaction effectiveness. Cross-cultural miscommunication not only derails negotiations, but can also be a contributing factor leading to war, according to DARPA’s explanation of the program’s rationale.
Their findings were recently published in the prestigious journal Scientific reports.
Prof. Neuman and his engineer trained the system to identify ten social emotions: competence, kindness, trust, discipline, caring, agreeableness, success, conformity, decency and loyalty. The system successfully characterized a situation written under one of these ten classifiers and could sense whether it was positive or negative.
The system was tested on two huge datasets of short texts and empirically proved the validity of the models.
“This is preliminary work, but it provides strong evidence that our approach is correct and can be expanded to include more social norms,” says Prof. Yair Neuman.
Prof. Neuman heads the Functor Lab in the Department of Cognitive and Brain Sciences at the BGU.
About this AI research news
Author: Ehud Zion Waldoks
Source: Ben-Gurion University of the Negev
Contact: Ehud Zion Waldoks – Ben-Gurion University of the Negev
Image: The image is credited to Neuroscience News
Original research: Free access.
“AI to Identify Violation of Social Norms” by Yair Neuman et al. Scientific reports
Abstract
AI to identify violation of social norms
Identifying social norms and violating them is a challenge facing several projects in computational science. This article presents a new approach to identifying violations of social norms.
We used GPT-3, zero-shot classification, and automatic rule discovery to develop simple predictive models based on psychological knowledge.
Tested on two huge datasets, the models exhibit significant predictive performance and show that even complex social situations can be functionally analyzed through modern computational tools.
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Image Source : neurosciencenews.com