{"id":72566,"date":"2023-07-13T13:29:31","date_gmt":"2023-07-13T20:29:31","guid":{"rendered":"https:\/\/blog.mozilla.org\/?p=72566"},"modified":"2023-08-28T20:41:32","modified_gmt":"2023-08-29T03:41:32","slug":"mozilla-responsible-ai-challenge-takeaways","status":"publish","type":"post","link":"https:\/\/blog.mozilla.org\/en\/mozilla\/mozilla-responsible-ai-challenge-takeaways\/","title":{"rendered":"4 things we learned from Mozilla’s Responsible AI challenge"},"content":{"rendered":"\n
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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\n\n\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\n\n\n

\"Imo
Imo Udom, Mozilla’s SVP of innovation ecosystems, speaks at Mozilla’s Responsible AI Challenge event in San Francisco in May 2023. Credit: Mozilla<\/em><\/figcaption><\/figure>\n\n\n\n

1. Accessibility makes AI better<\/h3>\n\n\n\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. <\/p>\n\n\n\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\n\n\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>\n\n\n\n

Since then, AI systems like image generation have made progress. The lesson: Accessibility<\/a> doesn\u2019t just ensure equitable access to tech advancements \u2013 it encourages us to innovate so that we can create better systems for all.<\/p>\n\n\n\n

\"Dr.
Dr. Margaret Mitchell discussed how human bias affects AI technology at Mozilla’s Responsible AI Challenge event in San Francisco in May 2023. Credit: Mozilla <\/em><\/figcaption><\/figure>\n\n\n\n

2. To address AI\u2019s limitations, we need (human) thinkers<\/h3>\n\n\n\n

Despite the advancements in artificial intelligence over the years, Dr. Gary Marcus, a prominent academic who recently testified before the U.S. Senate on AI, argues that deep learning systems still face significant challenges. For example, they can\u2019t be updated incrementally with new knowledge. This can lead to a lot of misinformation. <\/p>\n\n\n\n

\u201cWe have laws about defamation, but what if somebody makes a billion pieces of perfectly well-formed misinformation a day?\u201d Dr. Marcus said. \u201cDo we want to treat that like free speech? Or is that more like commercial speech? Should it be treated differently? We just don’t have the laws yet to do that.\u201d<\/p>\n\n\n\n

That\u2019s where experts, who pay attention to constant changes in the field, come in.<\/p>\n\n\n\n

\u201cThe choices that we make now will shape the next century,\u201d Dr. Marcus said. \u201cIf we don’t have scientists and ethicists at the table, our prospects are not great. We cannot afford to not regulate. And we cannot afford regulatory capture either. We have to get this one right.\u201d<\/p>\n\n\n\n

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Dr. Gary Marcus argued that deep learning systems face significant challenges, some of which can lead to the spread of misinformation at Mozilla’s Responsible AI Challenge event in San Francisco in May 2023. Credit: Mozilla<\/em><\/figcaption><\/figure>\n\n\n\n

3. Experts are optimistic about AI\u2019s capabilities, including advancements for the healthcar<\/strong>e field<\/h3>\n\n\n\n

Kevin Roose \u2013 a tech columnist, podcaster, and author \u2013 has covered AI for more than a decade. He made several predictions: Meaningful regulation will be unlikely before 2030, Roose said, because of AI\u2019s relative novelty and lawmakers\u2019 limited understanding of the technology. He anticipates better performing models, and that researchers and companies using AI models to build products will push for safer systems.<\/p>\n\n\n\n

He also predicted the great potential of AI, particularly for advancing drug development. <\/p>\n\n\n\n

Roose said, \u201cI think there’s like a 30% chance that a drug that is discovered using AI will reduce mortality from a top 10 cancer by 50% before 2030\u2026 I’m not a doctor, but this is the kind of thing that people who are experts in this field tell me. I believe them.\u201d<\/p>\n\n\n\n

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Kevin Roose shared several predictions around the potential of AI at Mozilla’s Responsible AI Challenge event in San Francisco in May 2023. Credit: Mozilla<\/em><\/figcaption><\/figure>\n\n\n\n

4. AI systems may collaborate more effectively than humans do!<\/h3>\n\n\n\n

Roose recounted a conversation with a source who pointed out a significant advantage of AI systems over humans: their tendency to share knowledge. <\/p>\n\n\n\n

The expert explained that when one node in a neural network establishes a connection, it propagates that information to all other nodes in the network. For example, in a fleet of self-driving cars, when one vehicle learns to identify a new obstacle, it shares that knowledge with all other cars in the fleet, including those from different manufacturers. People, on the other hand, don\u2019t have this tendency \u2013 often keeping research and data to themselves. <\/p>\n\n\n\n

Roose argued that in this aspect, people can learn from machines. He said, \u201cI think if we want any chance of competing and surviving and thriving in this new world of AI, we have to be able to do what the machines do and share with each other all of the things that we’re learning.\u201d<\/p>\n","protected":false},"excerpt":{"rendered":"

From chat engines 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? 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