Additionally, the article addresses the Western bias in AI training data, with over 90% of data sets coming from Europe and North America. This bias is partly due to the dominance of the English language on the internet and the convenience of using Western data. The lack of representation from other cultures and languages can lead to AI models that reinforce a US-centric worldview, potentially erasing other cultures and languages. The article emphasizes the need for more diverse and intentional data collection practices to ensure AI models accurately reflect the global human experience.
Key takeaways:
- AI data collection practices are immature compared to AI model development, with a lack of transparency about data sources and potential concentration of power in a few tech companies.
- The dominance of web-scraped data since 2018 has led to a gap between curated and indiscriminately collected data, with YouTube becoming a major source for video data.
- Exclusive data-sharing deals by major AI companies create asymmetric access, benefiting large corporations while disadvantaging smaller entities and researchers.
- AI training data is heavily skewed towards Western cultures, potentially reinforcing biases and erasing other languages and cultures from AI models.