Topic
12 facts from 1 episode, in date order. These are what speakers said on the shows, each linked to the moment it was said. They aren't independently verified.
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Alice Xiang established Sony's AI ethics office and its processes for evaluating AI use in products and services.
“I established our AI ethics office and our processes in terms of how different AI uses and AI integration into products and services is evaluated for responsible AI.”
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Alice Xiang says computer vision played a major role in the deep learning revolution.
“Computer vision is a field that played a major role in terms of the deep learning revolution.”
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Alice Xiang says web-scraped datasets that were massive and cheap to source helped drive computer vision's progress.
“part of that was these web scraped data sets that were massive and relatively cheap to source.”
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Alice Xiang cites having to look at a phone several times before it recognizes you as a minor consequence of biased face recognition.
“Maybe you have to look at your phone several times at different angles before it can recognize you.”
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Alice Xiang says biased computer vision can lead to serious harms including financial fraud and wrongful arrests.
“But this can also lead to much more problematic impacts in terms of anything from financial fraud to folks being wrongfully arrested.”
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Alice Xiang says FHIBE is a proof of concept that ethical data sourcing is possible at some scale, though harder and more expensive.
“Obviously, it's more difficult, it's more expensive, but, you know, FHIBE was kind of the proof of concept that at least on some scale, you're able to do this.”
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FHIBE focuses on human-centric computer vision because Alice Xiang considers it one of the most sensitive areas for data.
“We focused FHIBE on human-centric computer vision, precisely because it is one of the most sensitive areas.”
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Alice Xiang built her first machine learning model about twelve years before this episode and found it biased toward certain geographies.
“This was like twelve years ago now, but it got me really interested in this area.”
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Alice Xiang holds graduate degrees in law, statistics and economics.
“I have graduate degrees in law, statistics and economics.”
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Alice Xiang started a research lab at the Partnership on AI focused on algorithmic bias, where data availability quickly emerged as a key issue.
“I started up a research lab focused on these areas, and pretty quickly, these issues of data availability became quite salient.”
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Alice Xiang says in recent years employees' performance targets at companies have become focused on AI adoption.
“I think it's been interesting to see just in the past couple years how everyone's KPIs have suddenly become about AI adoption.”
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Alice Xiang says the worst uses of AI are autonomous ones lacking human oversight or oversight by people with sufficient expertise.
“where it's just kind of being used in a very autonomous fashion, and there's either no one providing oversight or the individuals providing oversight don't have the knowledge or expertise to do so.”
Episodes