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Black founders are creating tailored ChatGPTs for a more personalized experience | TechCrunch

Jun 16, 2024 - techcrunch.com
The article discusses the issue of cultural bias in artificial intelligence (AI), particularly in chatbots like ChatGPT, which are often Eurocentric and Western in their responses. This has led to dissatisfaction among Black users who feel underrepresented. In response, Black founders like John Pasmore and Erin Reddick have developed AI models such as Latimer.AI and ChatBlackGPT, respectively, that cater specifically to Black and brown communities. These models are trained on sources that reflect the experiences of these communities, providing more culturally nuanced responses.

The article also highlights the lack of representation of African languages and cultures in AI, with only 0.77% of global AI journals coming from sub-Saharan Africa. To address this, Yinka Iyinolakan created CDIAL.AI, a chatbot that understands nearly all African languages and dialects. Other companies like pocstock are working to increase the representation of people of color in AI-generated images. The article concludes by emphasizing the need for more founders of color to get involved in AI development to ensure more culturally inclusive AI models.

Key takeaways:

  • ChatGPT, a powerful AI tool, struggles with cultural nuance, often providing answers that are too generalized for specific communities, and showing a bias towards Eurocentric and Western perspectives.
  • Black founders are creating their own AI models to cater to Black and brown communities. Examples include Latimer.AI by John Pasmore, ChatBlackGPT by Erin Reddick, and Spark Plug by Tamar Huggins.
  • In Africa, AI models are being developed to support the more than 2,000 languages spoken across the continent. Yinka Iyinolakan created CDIAL.AI, a chatbot that can speak and understand nearly all African languages and dialects.
  • There is a push for more inclusive AI, with efforts to create more diverse stock images and to train AI models on more culturally diverse data. This is seen as a necessary step to eliminate bias and ensure AI tools can accurately reflect and serve all communities.
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