Cultural Incongruencies in Artificial Intelligence

TL;DR

This paper discusses how AI systems often overlook cultural differences, leading to mismatches when interacting with diverse societies, and explores strategies to address these cultural incongruencies.

Contribution

It highlights the cultural dependencies in AI technologies and proposes strategies to mitigate cultural incongruencies in AI systems.

Findings

AI systems embed cultural values of their development countries

Training data lacks global cultural diversity

Cultural incongruencies affect AI interactions with diverse societies

Abstract

Artificial intelligence (AI) systems attempt to imitate human behavior. How well they do this imitation is often used to assess their utility and to attribute human-like (or artificial) intelligence to them. However, most work on AI refers to and relies on human intelligence without accounting for the fact that human behavior is inherently shaped by the cultural contexts they are embedded in, the values and beliefs they hold, and the social practices they follow. Additionally, since AI technologies are mostly conceived and developed in just a handful of countries, they embed the cultural values and practices of these countries. Similarly, the data that is used to train the models also fails to equitably represent global cultural diversity. Problems therefore arise when these technologies interact with globally diverse societies and cultures, with different values and interpretive practices. In this position paper, we describe a set of cultural dependencies and incongruencies in the context of AI-based language and vision technologies, and reflect on the possibilities of and potential strategies towards addressing these incongruencies.