ChatGPT: Cutting Non English Languages Out of the AI Revolution

The English-Only AI Predicament

Imagine a world where machines can communicate and understand human languages, making our lives more convenient and efficient. This is the promise of artificial intelligence (AI) and natural language processing (NLP), technologies that have been rapidly advancing in recent years. However, there is a major problem that is hindering the progress of AI and NLP: the lack of diversity in language.

At its current state, AI is heavily skewed towards English, with a majority of research and development focused on this language. This is not surprising given that English is the lingua franca of business, science, and technology; and also the primary language of the internet. However, this approach is limiting the potential of AI and NLP, and leaving millions of people behind.

According to a report by UNESCO, there are over 7,000 living languages in the world, with over half of them under threat of extinction. If AI technology is only developed for a few dominant languages, this will exacerbate the inequality and marginalization of non-English speakers.

"If we only train AI systems for one particular language, we are going to end up with a technology that only serves one particular part of society." - Kai-Fu Lee, CEO of Sinovation Ventures

This issue is not just about fairness and equality, but also a matter of practicality. As AI and NLP become more ubiquitous in our lives, we will need them to understand and communicate in different languages. Businesses will need to cater to global markets, governments will need to provide services to diverse communities, and individuals will need to have access to information and assistance in their own language.

The Consequences of English-Only Bias

The bias towards English in AI and NLP has already started to show its negative effects. For example, a study by the AI Now Institute found that voice-recognition systems from major companies like Amazon, Google, and Apple have a higher error rate when recognizing non-English accents, particularly those from people of color.

Similarly, machine translation software such as Google Translate and Microsoft Translator have improved significantly in recent years, but they are still far from perfect in many languages. They often produce inaccurate or awkward translations, especially in languages with complex grammar and syntax.

In the healthcare sector, language bias in AI can have serious consequences. A study in the Journal of General Internal Medicine found that a popular symptom-checker app had a higher error rate in diagnosing and recommending treatment for non-English speakers. The app often failed to recognize symptoms and conditions that were more common in certain ethnic groups.

Finally, the lack of diversity in language in AI and NLP can perpetuate cultural stereotypes and reinforce power imbalances. For instance, facial recognition technology has been shown to have a higher error rate when identifying people with darker skin tones, which can lead to false arrests and racial profiling.

The Way Forward

The good news is that there are steps we can take to address the bias towards English and promote language diversity in AI and NLP. Here are three key areas to focus on:

1. Diversify Research and Development: One solution is to incentivize and fund research and development in other languages. Governments, non-profits, and private companies can support initiatives that promote linguistic diversity and multilingualism in AI and NLP. This includes hiring more non-English speaking researchers and developers, collaborating with local communities, and supporting open-source language data sets and tools.
2. Improve Language Data and Processing: Another key area is to improve the quality and quantity of language data and processing. This includes developing better machine learning algorithms for languages with complex grammar and syntax, improving speech recognition and synthesis for non-English accents, and creating more accurate and culturally sensitive language models and translations.
3. Encourage Multilingual Education and Literacy: Finally, we need to prioritize and incentivize multilingual education and literacy both in schools and in society at large. This includes promoting language learning programs, supporting bilingualism and multilingualism in communities, and encouraging businesses and organizations to provide services and content in multiple languages.

Ultimately, the future of AI and NLP depends on our ability to promote language diversity and inclusivity. By embracing linguistic differences and investing in multilingual technologies, we can unlock the full potential of AI and NLP to improve people's lives and solve global challenges.