{"id":116133,"date":"2023-07-18T10:16:47","date_gmt":"2023-07-18T14:16:47","guid":{"rendered":"https:\/\/o52s5t8h2.nuvmedia.com\/social-media\/4-things-we-learned-from-mozillas-responsible-ai-challenge\/"},"modified":"2023-07-18T10:16:47","modified_gmt":"2023-07-18T14:16:47","slug":"4-things-we-learned-from-mozillas-responsible-ai-challenge","status":"publish","type":"post","link":"https:\/\/o52s5t8h2.nuvmedia.com\/social-media\/4-things-we-learned-from-mozillas-responsible-ai-challenge\/","title":{"rendered":"4 things we learned from Mozilla\u2019s Responsible AI challenge"},"content":{"rendered":"
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From chat engines<\/a> and generative apps to self-driving cars, technologies that use artificial intelligence continue to transform our lives in new ways. But how do we create AI that serves society without disempowering some of us? How can we make sure these innovations are fair and trustworthy?<\/p>\n Those are the questions we asked last May at Mozilla\u2019s Responsible AI Challenge<\/a> event in San Francisco. Some of the industry\u2019s best thinkers, ethicists, technologists and builders got together to celebrate what\u2019s possible with AI while considering the responsibility that comes with its immense capabilities. Here\u2019s what we learned from the event.<\/p>\n Leading AI ethicist Dr. Margaret Mitchell shared that in 2011, while working on technology that generated image descriptions, she and her colleagues encountered what they called the \u201ceverything is awesome\u201d problem. The system learned from the photos and captions that people shared on social media, which were largely positive. So the model kept generating positive descriptions, even when the images depicted tragic events.\u00a0<\/p>\n People share \u201cbeautiful sunsets, great views, gorgeous skies\u2026 [So when the computer] sees this horrific blast, its response is, \u2018This is great.\u2019 And I realized at that moment how the data we use directly affects what the model learns,\u201d Dr. Mitchell said. \u201cWe see a moment where tons of people are potentially hurt, and the computer has no understanding of what mortality is. It has no understanding that explosions and bombs are negative things. It sees the purples and the pinks in the sky.\u201d<\/p>\n With the potential for connecting vision to language in assistive technology, Dr. Mitchell concluded that the models prioritized data that did not align with the needs of the visually impaired. Blind people need context and practical information about images, not descriptions solely based on available data, which can be skewed.<\/p>\n1. Accessibility makes AI better<\/h3>\n