Researchers at Ben-Gurion University of the Negev have designed an AI system that identifies violations of social norms

Researchers at Ben-Gurion University of the Negev have designed an AI system that identifies violations of social norms

The APA Dictionary of Psychology provides a comprehensive definition of social norms as socially determined standards that indicate typical and appropriate behaviors within a specific social context. These norms can be universal, applying broadly to all cultures, or contextual, specific to particular cultural contexts.

While social norms vary across cultures and contexts, violations of social norms can often be grouped into a few general categories. These categories capture common themes that transcend cultural boundaries.

Automatic identification of social norms and their violations poses a significant challenge. To effectively address this challenge, the first step is to identify the characteristics, signals or variables that indicate when a social norm has been violated.

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Researchers at Ben-Gurion University of the Negev have studied the automatic identification of violations of social norms, and one researcher has designed an artificial intelligence system that can detect violations of social norms. This study aimed to bridge the gap between social science and data science, recognizing the potential of integrating both fields to gain deeper insight into human behavior and social dynamics.

The researchers built this system using zero-shot text classification (zero-shot classification is a specialized form of natural language inference (NLI) in which the goal is to determine the probability that a given class label can be inferred or implied from a textual premise), GPT-3 (for generating synthetic data and identifying violated social norms through human domain experience), and automatic rule discovery. The system they created used a binary of ten social emotions as categories. Because the number of social norms is huge, researchers have grouped them into a small number of social emotions.

The researchers trained the system to detect ten emotions which are: competence, courtesy, trust, discipline, caring, agreeable, successful, conformity, decency and loyalty. The system they created can classify a given text into one of these emotions and could further classify them as positive or negative.

The researchers first employed zero-shot classification to automatically identify social emotions in short textual data. They then used GPT-3 to generate synthetic data and identify social norms violated through the experience of human dominance, resulting in a high-level norm taxonomy represented by ten top-level categories. In addition, they developed seven simple characteristic-based models that measure social emotions, norm violation, and other factors to classify cases involving norm violation or confirmation. These models were tested on two separate huge datasets of short texts.

The performance of the system was quite impressive, scoring a 64% match between the maximum emotion of the zero-roll classifier and the emotions identified by human subjects. To achieve this, the researchers used the EmpatheticDialogues dataset, which contains approximately 25,000 conversations labeled with 32 different emotions. Their focus was on situations involving violations of norms and emotions.

Leveraging this labeled data, they trained models to automatically identify social norms and classify them into higher-level groups. The results were quite encouraging, with an accuracy of around 94% and an accuracy of around 96% in detecting policy violations.

Speaking about the study, the researchers said that it is preliminary work, but it provides strong evidence that their approach is correct and can be expanded to include more social norms.


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Rachit Ranjan is a Consulting Intern at MarktechPost. He is currently pursuing his B.Tech from Indian Institute of Technology (IIT) Patna. He is actively shaping his career in AI and data science and is passionate and dedicated to exploring these fields.


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