AI systems have made remarkable advancements. However, they perpetuate linguistic bias that can result in the total erosion of languages. This, in turn, can decimate cultural diversity, lead to the loss of identity, and further perpetuate societal inequalities.
That’s why I’ve been diving into this complex issue and exploring how we can unlock growth opportunities by building AI systems that work for everyone.
Gender bias is prevalent in language translation systems like Google Translate and Microsoft Translator, perpetuating stereotypes. For example, professions like doctors or engineers are translated as male by default. Even when gender is unknown, Microsoft Translator defaults “o” in Turkish to “he.”
Similarly, Google Translate often uses masculine form, even when the context suggests that a woman is being referred to. For instance, if you input “My doctor is amazing” for translation into Spanish, Google Translate will automatically use the masculine form “Mi doctor es increíble,” even if you are referring to a female doctor. This gender bias can perpetuate societal inequalities and limit equitable access to opportunities for women, particularly in professions where gender stereotypes are already prevalent.
To address this type of linguistic bias, it is important to ensure that there’s diverse representation on the teams developing these systems. The training data used to develop translation models must include copious amounts of text that represent both masculine and feminine phrasing. Additionally, these systems should include options for gender-neutral language.
One of my favorite things is listening to my cousin speak in colloquial Kikuyu. He’ll have me rolling, laughing until tears are streaming down my face. There are deep cultural nuances that Google Translate doesn’t quite capture.
Machine translation systems are biased toward certain languages and language varieties. For instance, a system trained primarily in formal written English will struggle to accurately translate informal languages. It’s like a high school clique where only the “popular” kids got all the attention. That’s so not cool, machine translation. Broaden your horizons. Include training data that represents diverse languages, dialects, and registers. Seriously, it’s the right thing to do.
Speech recognition technology isn’t doing any better. Most have been found to have higher error rates for individuals with non-standard accents or dialects. It’s like they’re saying, “Sorry, we only speak standard.”
This is because the training data used to develop the technology is biased towards standard accents, leading to errors and inaccuracies for individuals who sound even slightly different or speak other dialects. To address this bias, it is important to include a diverse range of accents and dialects in the training data. Additionally, systems should be evaluated on their ability to recognize a variety of accents and dialects.
Virtual assistants such as Siri or Alexa may struggle to understand or respond to certain accents or dialects, particularly those that are not well-represented in the training data. This can result in errors and misunderstandings, leading to negative user experiences. To address this bias, it is essential to include a diverse range of accents and dialects in the training data. Additionally, the system should be evaluated on its ability to understand and respond to a variety of accents and dialects.
Finally, the erosion of linguistic and cultural diversity is a critical concern that must be addressed in the development of generative AI systems. If a language is underrepresented in the training data used to develop an AI system, the model will struggle to accurately understand or generate content in that language. Consequently, communities that speak that language will experience erosion of linguistic and cultural diversity. This leads to further underrepresentation and marginalization. To promote linguistic and cultural diversity, it is essential to include a diverse range of languages and cultures in the training data.
Addressing linguistic bias in generative AI systems requires a concerted effort to promote diversity and inclusivity in the training data. We also need to continually assess the algorithms’ designs, data processing techniques, and how humans interact with AI systems.
We must include a wide range of languages, dialects, and cultural nuances to ensure that these systems accurately represent and serve all users, regardless of their linguistic background. Ultimately, we must create AI systems that are technologically advanced, socially responsible, and equitable.
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