Introduction

We have all heard people say, be nice to everyone even if they are rude to you because you don’t know what that person has been through today.

This speaks to us as humans that we should be having a sense of social intelligence.

Social intelligence refers to the ability to navigate and understand social situations effectively. It involves being aware of the emotions of others, as well as being able to interpret social cues, communicate effectively, and maintain positive relationships.

Socially intelligent individuals are attuned to social norms, cultural differences, and situational contexts.

They can read social cues and understand the unspoken rules of social interaction. This allows them to navigate social situations with ease and adapt their behaviour accordingly.

But how will a social intelligence system for AI look? This study looks at the taxonomy of such a system.

Multimodality

There has been keen interest in the introduction of multimodal features to Large Models, with image and video generation and detection. However, I see a huge significance in making use of multimodal features within a conversational UI, allowing for a more nuanced conversational experience.

Understand

We have long focussed on social intelligence by only considering the cognitive aspect of it, and our systems’ ability to understand what users’ intent is. And understanding the user, based on their input and any related customer data the system can glean from back-end lookups.

Behavioural

As conversational UI designers, we are often also fixated on the behavioural part of the bot; hence designing bots which behave in a certain way.

Both understanding and behaviour are important to building to good conversational UI, but not sufficient.

LLMs perform worse than best human performance on most tasks on cognitive and situational intelligence.

~ Source

Social Intelligence Data Infrastructure

What this study attempts, is creating a taxonomy of what a Social Intelligence system would look like.

A seen in the image below, the study on social intelligence has identified three distinct typesof social intelligence:

1. Cognitive Intelligence
2. Situational Intelligence
3. Behavioural Intelligence

Cognitive Intelligence

Under cognitive intelligence traditionally chatbots have covered items like intent and emotion quite well. Advances have been made on the emotional front, ensuring that Conversational UIs shows some level of empathy.

As most of our chatbots have been task orientated, much focus has been placed on intent recognition.

Future datasets should focus more on specific, nuanced, and long-tailed social situations. ~ Source

Situational Intelligence

This aspect considers the social context, and social context informs cognitive and behavioural intelligence.

Cultural differences between high-context and low-context cultures refer to variations in how communication is conducted and interpreted within different societies.

These differences can have significant implications for interpersonal relationships, business transactions, and overall social interactions.

High-Context Cultures

In high-context cultures, much of the meaning in communication is conveyed through implicit cues, such as non-verbal gestures, tone of voice, and contextual clues.

People in high-context cultures rely heavily on shared cultural knowledge and understandings to interpret messages. Examples of high-context cultures include Japan, China, and many Middle Eastern countries.

Low-Context Cultures

In contrast, low-context cultures rely more on explicit verbal communication to convey meaning.

Messages are typically straightforward and direct, with less reliance on context or shared cultural knowledge. Examples of low-context cultures include the United States, Germany, and some Northern European countries.

One can argue that in the recent past, chatbots have been low-context communicators.