HomeArtificial IntelligenceArtificial intelligence have to be trained on culturally different data sets to...

Artificial intelligence have to be trained on culturally different data sets to avoid bias

Large Language Models (LLMs) are deep learning artificial intelligence programs, akin to OpenAI's ChatGPT. The skills of LLMs have developed into quite a broad range write fluent essaysto coding to creative writing. Millions of individuals worldwide use LLMsand it will be no exaggeration to say that these technologies are transforming work, education and society.

LLMs are trained by reading large amounts of text and learning to acknowledge and mimic patterns in the information. This allows them to create coherent and human-like texts on virtually any topic.

Because the Internet remains to be predominantly English – As of January 2023, 59 percent of all web sites were in English — LLM students are trained totally on English texts. Additionally, the overwhelming majority of English text online comes from users based within the United States, home of 300 million English speakers.

LLMs learn concerning the world through English texts written by US-based web users Standard American English and have a narrow Western, North American and even US-centric lens.

Model bias

In 2023, when ChatGPT learned of a pair dining at a restaurant in Madrid who tipped 4 percent, suggested they were thrifty, on a good budget, or didn't just like the service. By default, ChatGPT followed the North American standard of a 15 to 25 percent tip. Disregarding the Spanish norm of not tipping.

Since early 2024, ChatGPT has accurately cited cultural differences when assessing the appropriateness of a tip. It is unclear whether this ability arose from training a more moderen version of the model with more data – in any case, there are lots of typing guides in English on the Internet – or whether OpenAI patched this particular behavior.

Using data from English-language web sites, based totally within the United States, provides insight into how LLMs reply to prompts.
(Unsplash/Jonathen Kemper)

However, there are other examples that reveal ChatGPT's implicit cultural assumptions. For example, with a story about guests showing up for dinner at 8:30 p.m., it suggested Reasons why guests were late, although the time of the invitation was not mentioned. Again, ChatGPT probably assumed they were invited to a typical North American dinner at 6 p.m.

In May 2023, researchers on the University of Copenhagen has quantified this effect by prompting LLMs with the Hofstede cultural survey, which measures human values ​​in several countries. Shortly afterwards, researchers exhibited AI start-up company Anthropic used that World Values ​​Survey do the identical. Both works concluded that LLMs exhibit a robust fit with American culture.

The same phenomenon occurs when asking questions FROM-E 3, a picture generation model trained on pairs of images and their captions to generate a picture of a breakfast. This model, trained totally on images from Western countries, produced images of pancakes, bacon, and eggs.

Effects of bias

Culture plays a crucial role in shaping our communication styles and worldviews. As well as Intercultural human interactions can result in misunderstandingsUsers from different cultures who interact with conversational AI tools may feel misunderstood and find them less useful.

To be higher understood by AI tools, users can adapt their communication styles in the same option to how people have learned to “Americanize” their foreign accents to achieve success personal assistants like Siri and Alexa.

As more people depend on LLMs to edit texts, they’re more likely to standardize the best way we write. Over time, LLMs risk eliminating cultural differences.

Decision making and AI

AI is already getting used because the backbone of assorted applications that make decisions that impact people's lives, akin to: Continue filtering, Rental applications And Applications for social advantages.

for years, AI researchers have warned that these models not only learn “good” statistical relationships – akin to considering experience as a desired trait for a job candidate – but in addition “bad” statistical relationships, akin to consideration Women are considered less qualified for technical positions.

As LLMs are increasingly used to automate such processes, one can imagine that the North American bias learned from these models may result in discrimination against people from different cultures. An absence of cultural awareness can result in AI perpetuating stereotypes and increasing societal inequalities.

LLMs for languages ​​aside from English

Developing LLMs for languages ​​aside from English is difficult necessary effort, and there are lots of such models. However, there are several explanation why this ought to be done in parallel with improving the cultural awareness and sensitivity of LLMs.

First, there may be a big population of English speakers outside of North America who are usually not represented by English LLMs. The same argument applies to other languages. A French language model could be more representative of the culture in France than of the culture in other Francophone regions.

Training LLMs for regional dialects – which of them can capture subtler cultural differences – can also be not a practical solution. The quality of LLMs is dependent upon the quantity of information available and due to this fact their quality could be poorer for dialects with little online data.

Secondly, many users whose native language isn’t English still select to make use of English LLMs. Major breakthroughs in voice technology are likely to occur Start with English before applying them to other languages. Even then, there are usually not enough online texts in lots of languages ​​– akin to Welsh, Swahili and Bengali – to coach high-quality models.

Due to the dearth of availability of LLMs of their native language or the higher quality of English LLMs, users from different countries and backgrounds may prefer using English LLMs.

Ways forward

Our research group on the University of British Columbia works to counterpoint LLMs with culturally diverse knowledge. Together with a doctoral student Mehar BhatiaWe trains an AI model on one Collection of facts about traditions and ideas in several cultures.

Before reading these facts, the AI ​​suggested that an individual eating a Dutch Baby (a variety of German pancake) was “disgusting and mean” and would feel guilty. After training, the person felt “full and satisfied,” it was said.

a pancake covered in berries
If you taught an AI that a Dutch baby was a dish, its response would change when it learned that somebody had eaten one.

We are currently collecting a large-scale caption dataset with images from 60 cultures, which can help models study breakfast types aside from bacon and eggs, for instance. Our future research will transcend providing models of the existence of culturally diverse concepts to raised understand how people interpret the world through the lens of their cultures.

As AI tools turn into more ubiquitous in society, it’s imperative that they move beyond the dominant Western and North American perspectives. Companies and organizations across many industries are using AI to automate manual processes and use data to make higher, evidence-based decisions. Making such tools more inclusive is critical for Canada’s diverse population.


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