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Google’s Gemini Chatbot Faces Backlash Over AI Image Generation

Google recently came under fire for its Gemini chatbotÔÇÖs AI image generation feature, which was launched three weeks ago. Users criticized the chatbot for its approach to diversity and inclusion when generating images of people. One specific instance that garnered attention was when Gemini displayed images of individuals from various ethnic backgrounds in response to a query about the founding fathers of the United States, who were historically white men. This perceived “historical inaccuracy” raised concerns among users.

With the public backlash, Google acknowledged the issue and announced that it would address the problem while temporarily suspending the image generation feature.

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Furthermore, in a blog post, Google’s senior vice president, Prabhakar Raghavan, expressed regret over the situation and outlined plans for improvement:

ÔÇ£Three weeks ago, we launched a new image generation feature for the Gemini conversational app (formerly known as Bard), which included the ability to create images of people. ItÔÇÖs clear that this feature missed the mark. Some of the images generated are inaccurate or even offensive. WeÔÇÖre grateful for usersÔÇÖ feedback and are sorry the feature didnÔÇÖt work well. WeÔÇÖve acknowledged the mistake and temporarily paused image generation of people in Gemini while we work on an improved version,ÔÇØ stated Raghavan.

The Gemini’s image generation feature utilized an AI model named Imagen 2, which Google developed to address issues seen in other image generation products, particularly concerning violent or sexually explicit content or depictions of real individuals.

Raghavan also mentioned that the company aimed to incorporate diversity, equity, and inclusion standards into the product, but it seems they overshot the mark. He explained that while Gemini is designed to depict a diverse range of individuals in certain scenarios, such as a group of football players, it should accurately represent the user’s prompt in more specific requests.

However, there were shortcomings in the implementation, as the model erred on the side of caution, sometimes failing to respond to prompts it deemed sensitive. As a result, Google decided to temporarily halt the model’s operations and will only resume them after significant improvements and thorough testing.

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