{"id":150981,"date":"2024-10-16T16:33:24","date_gmt":"2024-10-16T14:33:24","guid":{"rendered":"https:\/\/luxus-plus.com\/chronicle-generative-ai-and-inclusiveness-between-discriminatory-biases-and-considerable-potential\/"},"modified":"2024-11-05T01:00:32","modified_gmt":"2024-11-05T00:00:32","slug":"chronicle-generative-ai-and-inclusiveness-between-discriminatory-biases-and-considerable-potential","status":"publish","type":"post","link":"https:\/\/luxus-plus.com\/en\/chronicle-generative-ai-and-inclusiveness-between-discriminatory-biases-and-considerable-potential\/","title":{"rendered":"[CHRONICLE] Generative AI and inclusiveness: between discriminatory biases and considerable potential"},"content":{"rendered":"

Artificial intelligence is revolutionizing our daily lives, and has been creating a real boom over the past two years. Nevertheless, everyone has already faced a generated image that reflected discriminatory prejudices. This raises crucial questions of ethics and inclusiveness. Indeed, biases linked to cultural and social stereotypes already influence certain results generated by AI. Yet, paradoxically, generative AI has immense potential to promote gender diversity.<\/h4>\n

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Obvious biases<\/strong><\/b> in generative AI results<\/p>\n

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Examples that speak for themselves<\/h2>\n

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Image generation can be highly prone to discriminatory bias.<\/strong><\/b> When an image-generating AI like OpenAI’s DALL-E is asked to generate an image of a rich person, a homeless person or a terrorist<\/strong><\/b>, the results may reflect gender or ethnic bias.<\/strong><\/b><\/p>\n

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\"Generative<\/p>\n

Requests sent to Dall-E: \u201cA nurse\u201d, \u201cA florist\u201d<\/p>\n

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