Katya Klinova's guest appearances

Katya Klinova

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Dennis Mortensen

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Vasco Pedro

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In recent years, the focus of AI developers has been to implement technologies that replace basic human labor. Talking to us today about why this is the wrong application for AI (right now), is Katya Klinova, the Head of AI, Labor, and the Economy at The Partnership on AI. Tune in to find out why replacing human labor doesn't benefit the whole of humanity, and what our focus should be instead.  We delve into the threat of "so-so technologies" and what the developer's role should be in approaching ethical vendors and looking after the workers supplying them with data. Join us to find out more about how AI can be used to better the whole of society if there’s a shift in the field’s current aims. Key Points From This Episode:An introduction to Katya Klinova, Head of Al, Labor and the Economy at The Partnership on AI.How her expectations of the world after her undergraduate degree shaped her.Pursuing a degree in economics to understand how AI impacts labor and economics.The role of The Partnership on AI in dissipating technological gains.Who is impacted when AI is introduced to a market: the consumers and the workers.How different companies are deficient in the ways they benefit everyone. Find out what the “threat of so-so technology” is.Should people become shareholders in AI technology that they helped to train?How capitalism incentivizes “so-so technologies”. The role of developers in selecting vendors and responsible sourcing. Why it's important to realize that data labelers are employees and not just numbers.Shifting the focus of AI from automation to complementarity. Why now is not the time to be replacing human labor.  Tweetables:“Creating AI that benefits all is actually a very large commitment and a statement, and I don't think many companies have really realized or thought through what they're actually saying in the economic terms when they're subscribing to something like that.” — @klinovakatya [0:09:45] "It’s not that you want to avoid all kinds of automation, no matter what. Automation, at the end of the day, has been the force that lifted living conditions and incomes around the world, and has been around for much longer than AI." — @klinovakatya [0:11:28] “We compensate people for the task or for their time, but we are not necessarily compensating them for the data that they generate that we use to train models that can displace their jobs in the future.” — @klinovakatya [0:14:49] "Might we be automating too much for the kind of labor market needs that we have right now?" — @klinovakatya [0:23:14] ”It’s not the time to eliminate all of the jobs that we possibly can. It’s not the time to create machines that can match humans in everything that they do, but that’s what we are doing.” — @klinovakatya [0:24:50] Links Mentioned in Today’s Episode:Katya Klinova on LinkedIn"Automation and New Tasks: How Technology Displaces and Reinstates Labor"The Partnership on AI: Responsible Sourcing

In recent years, the focus of AI developers has been to implement technologies that replace basic human labor. Talking to us today about why this is the wrong application for AI (right now), is Katya Klinova, the Head of AI, Labor, and the Economy at The Partnership on AI. Tune in to find out why replacing human labor doesn't benefit the whole of humanity, and what our focus should be instead.  We delve into the threat of "so-so technologies" and what the developer's role should be in approaching ethical vendors and looking after the workers supplying them with data. Join us to find out more about how AI can be used to better the whole of society if there’s a shift in the field’s current aims. Key Points From This Episode:<ul><li>An introduction to Katya Klinova, Head of Al, Labor and the Economy at The Partnership on AI.</li><li>How her expectations of the world after her undergraduate degree shaped her.</li><li>Pursuing a degree in economics to understand how AI impacts labor and economics.</li><li>The role of The Partnership on AI in dissipating technological gains.</li><li>Who is impacted when AI is introduced to a market: the consumers and the workers.</li><li>How different companies are deficient in the ways they benefit everyone. </li><li>Find out what the “threat of so-so technology” is.</li><li>Should people become shareholders in AI technology that they helped to train?</li><li>How capitalism incentivizes “so-so technologies”. </li><li>The role of developers in selecting vendors and responsible sourcing. </li><li>Why it's important to realize that data labelers are employees and not just numbers.</li><li>Shifting the focus of AI from automation to complementarity. </li><li>Why now is not the time to be replacing human labor. </li></ul> Tweetables:“Creating AI that benefits all is actually a very large commitment and a statement, and I don't think many companies have really realized or thought through what they're actually saying in the economic terms when they're subscribing to something like that.” — <a href="https://twitter.com/klinovakatya" rel="nofollow" target="_blank">@klinovakatya</a> [0:09:45] "It’s not that you want to avoid all kinds of automation, no matter what. Automation, at the end of the day, has been the force that lifted living conditions and incomes around the world, and has been around for much longer than AI." — <a href="https://twitter.com/klinovakatya" rel="nofollow" target="_blank">@klinovakatya</a> [0:11:28] “We compensate people for the task or for their time, but we are not necessarily compensating them for the data that they generate that we use to train models that can displace their jobs in the future.” — <a href="https://twitter.com/klinovakatya" rel="nofollow" target="_blank">@klinovakatya</a> [0:14:49] "Might we be automating too much for the kind of labor market needs that we have right now?" — <a href="https://twitter.com/klinovakatya" rel="nofollow" target="_blank">@klinovakatya</a> [0:23:14] ”It’s not the time to eliminate all of the jobs that we possibly can. It’s not the time to create machines that can match humans in everything that they do, but that’s what we are doing.” — <a href="https://twitter.com/klinovakatya" rel="nofollow" target="_blank">@klinovakatya</a> [0:24:50] Links Mentioned in Today’s Episode:<a href="https://www.linkedin.com/in/katyaklinova/" rel="nofollow" target="_blank">Katya Klinova on LinkedIn</a>"<a href="https://pubs.aeaweb.org/doi/pdfplus/10.1257/jep.33.2.3" rel="nofollow" target="_blank">Automation and New Tasks: How Technology Displaces and Reinstates Labor</a>"<a href="https://partnershiponai.org/workstream/responsible-sourcing/" rel="nofollow" target="_blank">The Partnership on AI: Responsible Sourcing</a>

Responsible AI Economics with Katya Klinova & The Partnership on AI

[Audio] Podcast: Play in new window | DownloadSubscribe: Google Podcasts | Spotify | Stitcher | TuneIn | RSSAs the Head of AI, Labor, and the Economy, Katya Klinova directs the strategy and execution of the AI, Labor, and the Economy Research Programs at the Partnership on AI, focusing on studying the mechanisms for steering AI progress towards greater equality of opportunity and improving the working conditions along the AI supply chain. In this role, she oversees multiple programs including the AI and Shared Prosperity Initiative.Katya holds an MPA in International Development from Harvard University (USA), a B.Sc. cum laude in Applied Mathematics and Computer Science from Rostov State University (Russia), and a Joint M.Sc. in Networks and Data Science from University of Reading (UK), Aristotle University of Thessaloniki (Greece), and Universidad Carlos III de Madrid (Spain), where she was a Mundus Scholar.B is a technology and facilitation expert who is passionate about creating social change through empowering everyone to participate in technological and social governance. B is a Congressional Innovation Fellow serving in the US Senate advising policy makers on technology policy.B received a Bachelor of Science in Economics from the University of Texas at Dallas, and was selected as an MIT-Harvard Assembly Fellow for the 2019 Ethics and Governance in Artificial Intelligence Initiative cohort.Episode Links:  Katya Klinova’s LinkedIn: https://www.linkedin.com/in/katyaklinova/ B. Cavello’s LinkedIn: https://www.linkedin.com/in/bcavello/ Katya Klinova’s Twitter: @klinovakatyaB. Cavello’s Twitter: @b_cavelloKatya Klinova’s Website: https://www.partnershiponai.org/ B. Cavello’s Website: https://bcavello.com/ Podcast Details: Podcast website: https://www.humainpodcast.com Apple Podcasts:  https://podcasts.apple.com/us/podcast/humain-podcast-artificial-intelligence-data-science/id1452117009 Spotify:  https://open.spotify.com/show/6tXysq5TzHXvttWtJhmRpS RSS: https://feeds.redcircle.com/99113f24-2bd1-4332-8cd0-32e0556c8bc9 YouTube Full Episodes: https://www.youtube.com/channel/UCxvclFvpPvFM9_RxcNg1rag YouTube Clips:  https://www.youtube.com/channel/UCxvclFvpPvFM9_RxcNg1rag/videos Support and Social Media:  – Check out the sponsors above, it’s the best way to support this podcast– Support on Patreon: https://www.patreon.com/humain/creators – Twitter:  https://twitter.com/dyakobovitch – Instagram: https://www.instagram.com/humainpodcast/ – LinkedIn: https://www.linkedin.com/in/davidyakobovitch/ – Facebook: https://www.facebook.com/HumainPodcast/ – HumAIn Website Articles: https://www.humainpodcast.com/blog/ Outline: Here’s the timestamps for the episode: (00:00) – Introduction(01:55) – AI and technological change have been contributing to the polarization of labor market skill bias. What we saw as the pandemic is that people with college degrees, people who have the opportunity to work remotely have been hit economically much less comparatively with people who are not able to work remotely. And that's disproportionately people who did not have access to higher education and college degrees.(04:52) –  We see a lot of formal sector jobs  falling away as a result of precautions taken to manage the virus. But as a result of this, we also see a proliferation in oftentimes lower wage on-demand or gig work playing out. There are many, several silver linings to take from this trend that we're seeing playing out, but there are also a lot of highly disruptive technologies in the space of robotics and information technology, especially in the AI space, that could lead to possible exciting futures, but they could also lead to some less ideal outcomes.(08:14) –  Some people might have found out that they're just as productive working from home, and they save time commuting. So some companies might have discovered that they're saving a lot of money on office space. So they might choose, even if it's not because of healthcare considerations, they might choose to stay remote. And that might become more of a norm.(10:41) – We see a whole new level of disparity across the board. The office, the workplace is in some ways a leveler, in that everyone has access to the same coffee machine, the same conference room, the same equipment, but as more of our work is distributed, that might not be the case.(13:19) –  I also want to shine a spotlight on the role that we human beings are playing in the process of facilitating the development of these technologies. And while we recognize that, we're building incredibly fabulously capable machines, really continuing to interrogate to what end and to whose benefit those are being built. Taking a more active stance in the future of work debate, and being more deliberate about choosing the direction of technological change when it comes to AI and other technologies as well is what is missing.(18:41) – We need to be realistic about our ability to quickly enough upskill everyone globally to keep pace with the technological advancement and think about how do we lower the barrier to entry, lower the barrier that's needed in terms of skill requirements for people to be able to use these technologies to their economic advantage and extract economic value from that and be able to use it for their earning opportunities. I'm genuinely curious to what extent certain jobs that are considered as low skilled or high-skilled, which we recognize as the flawed language of economics, where we're really what we're referring to is educational attainment and how much pre-training someone has.(28:24) – The benchmark that we hold our technology against is not these questions of what would make a worker's job easier or their output better. But rather this question of, is it going to be able to perform at the level of a human? Can we make a technology that will make a person,  that will then be able to do whatever a person can do? And there's this sort of fetishization in the AI sphere. And it comes from a really beautiful, fascinating space. The scifi nerd in me does really wonder, Oh man, what would it be like to create other ways of thought, what would it be like to develop these thinking machines.(31:04) – We have something like 8 billion humans, those humans now more than ever are in need of gainful jobs. And if we think of technological progress as the type of technological change that helps society prosper and overcome its economic condition, the last thing that we need to do is to be building machines that do what humans can already do better than them. And creates competition for those humans.(40:25) –  I work in the AI space because  that can be a thing that does bring about incredible opportunity and prosperity and new horizons of understanding and collaboration that we haven't even seen before. And that's really exciting to me. So, I wanted to just clarify that this stance is not one that says we shouldn't have AI. We shouldn't go down this road. We shouldn't build these technologies, but rather, that this technology isn't moving on its own, it's moving because our hands are doing the work, at least for the time being(44:01) – There's a lot of talk in conversations about structural issues and structural change. At the end of the day, these structures are built by us as people, the humans in the AI loop, and we have the power to shift it. And we also have the power to do things that we couldn't do before.Advertising Inquiries: https://redcircle.com/brandsPrivacy &amp; Opt-Out: https://redcircle.com/privacy

[Audio] Podcast:<a href="https://open.spotify.com/show/6tXysq5TzHXvttWtJhmRpS" rel="nofollow" target="_blank"> </a><a href="https://open.spotify.com/show/6tXysq5TzHXvttWtJhmRpS" rel="nofollow" target="_blank">Play in new window</a> |<a href="https://www.listennotes.com/podcasts/humain-podcast-artificial-intelligence-data-fBRret2PTiU/" rel="nofollow" target="_blank"> </a><a href="https://www.listennotes.com/podcasts/humain-podcast-artificial-intelligence-data-fBRret2PTiU/" rel="nofollow" target="_blank">Download</a>Subscribe:<a href="https://podcasts.google.com/feed/aHR0cHM6Ly9mZWVkcy5yZWRjaXJjbGUuY29tLzk5MTEzZjI0LTJiZDEtNDMzMi04Y2QwLTMyZTA1NTZjOGJjOQ" rel="nofollow" target="_blank"> </a><a href="https://podcasts.google.com/feed/aHR0cHM6Ly9mZWVkcy5yZWRjaXJjbGUuY29tLzk5MTEzZjI0LTJiZDEtNDMzMi04Y2QwLTMyZTA1NTZjOGJjOQ" rel="nofollow" target="_blank">Google Podcasts</a> |<a href="https://open.spotify.com/show/6tXysq5TzHXvttWtJhmRpS" rel="nofollow" target="_blank"> </a><a href="https://open.spotify.com/show/6tXysq5TzHXvttWtJhmRpS" rel="nofollow" target="_blank">Spotify</a> |<a href="https://www.stitcher.com/show/humain" rel="nofollow" target="_blank"> </a><a href="https://www.stitcher.com/show/humain" rel="nofollow" target="_blank">Stitcher</a> | <a href="https://tunein.com/podcasts/Technology-Podcasts/HumAIn-p1224678/" rel="nofollow" target="_blank">TuneIn</a> | <a href="https://feeds.redcircle.com/99113f24-2bd1-4332-8cd0-32e0556c8bc9" rel="nofollow" target="_blank">RSS</a>As the Head of AI, Labor, and the Economy, Katya Klinova directs the strategy and execution of the AI, Labor, and the Economy Research Programs at the Partnership on AI, focusing on studying the mechanisms for steering AI progress towards greater equality of opportunity and improving the working conditions along the AI supply chain. In this role, she oversees multiple programs including the AI and Shared Prosperity Initiative.Katya holds an MPA in International Development from Harvard University (USA), a <a href="http://B.Sc" rel="nofollow" target="_blank">B.Sc</a>. cum laude in Applied Mathematics and Computer Science from Rostov State University (Russia), and a Joint <a href="http://M.Sc" rel="nofollow" target="_blank">M.Sc</a>. in Networks and Data Science from University of Reading (UK), Aristotle University of Thessaloniki (Greece), and Universidad Carlos III de Madrid (Spain), where she was a Mundus Scholar.B is a technology and facilitation expert who is passionate about creating social change through empowering everyone to participate in technological and social governance. B is a Congressional Innovation Fellow serving in the US Senate advising policy makers on technology policy.B received a Bachelor of Science in Economics from the University of Texas at Dallas, and was selected as an MIT-Harvard Assembly Fellow for the 2019 Ethics and Governance in Artificial Intelligence Initiative cohort.Episode Links:  Katya Klinova’s LinkedIn: <a href="https://www.linkedin.com/in/katyaklinova/" rel="nofollow" target="_blank">https://www.linkedin.com/in/katyaklinova/</a> B. Cavello’s LinkedIn: <a href="https://www.linkedin.com/in/bcavello/" rel="nofollow" target="_blank">https://www.linkedin.com/in/bcavello/</a> Katya Klinova’s Twitter: <a href="https://twitter.com/klinovakatya?s=20" rel="nofollow" target="_blank">@klinovakatya</a>B. Cavello’s Twitter: <a href="https://twitter.com/b_cavello" rel="nofollow" target="_blank">@b_cavello</a>Katya Klinova’s Website: <a href="https://www.partnershiponai.org/" rel="nofollow" target="_blank">https://www.partnershiponai.org/</a> B. Cavello’s Website: <a href="https://bcavello.com/" rel="nofollow" target="_blank">https://bcavello.com/</a> Podcast Details: Podcast website: <a href="https://www.humainpodcast.com" rel="nofollow" target="_blank">https://www.humainpodcast.com</a> Apple Podcasts:  <a href="https://podcasts.apple.com/us/podcast/humain-podcast-artificial-intelligence-data-science/id1452117009" rel="nofollow" target="_blank">https://podcasts.apple.com/us/podcast/humain-podcast-artificial-intelligence-data-science/id1452117009</a> Spotify:  <a href="https://open.spotify.com/show/6tXysq5TzHXvttWtJhmRpS" rel="nofollow" target="_blank">https://open.spotify.com/show/6tXysq5TzHXvttWtJhmRpS</a> RSS: <a href="https://feeds.redcircle.com/99113f24-2bd1-4332-8cd0-32e0556c8bc9" rel="nofollow" target="_blank">https://feeds.redcircle.com/99113f24-2bd1-4332-8cd0-32e0556c8bc9</a> YouTube Full Episodes: <a href="https://www.youtube.com/channel/UCxvclFvpPvFM9_RxcNg1rag" rel="nofollow" target="_blank">https://www.youtube.com/channel/UCxvclFvpPvFM9_RxcNg1rag</a> YouTube Clips:  <a href="https://www.youtube.com/channel/UCxvclFvpPvFM9_RxcNg1rag/videos" rel="nofollow" target="_blank">https://www.youtube.com/channel/UCxvclFvpPvFM9_RxcNg1rag/videos</a> Support and Social Media:  – Check out the sponsors above, it’s the best way to support this podcast– Support on Patreon: <a href="https://www.patreon.com/humain/creators" rel="nofollow" target="_blank">https://www.patreon.com/humain/creators</a> – Twitter:  <a href="https://twitter.com/dyakobovitch" rel="nofollow" target="_blank">https://twitter.com/dyakobovitch</a> – Instagram: <a href="https://www.instagram.com/humainpodcast/" rel="nofollow" target="_blank">https://www.instagram.com/humainpodcast/</a> – LinkedIn: <a href="https://www.linkedin.com/in/davidyakobovitch/" rel="nofollow" target="_blank">https://www.linkedin.com/in/davidyakobovitch/</a> – Facebook: <a href="https://www.facebook.com/HumainPodcast/" rel="nofollow" target="_blank">https://www.facebook.com/HumainPodcast/</a> – HumAIn Website Articles: <a href="https://www.humainpodcast.com/blog/" rel="nofollow" target="_blank">https://www.humainpodcast.com/blog/</a> Outline: Here’s the timestamps for the episode: (00:00) – Introduction(01:55) – AI and technological change have been contributing to the polarization of labor market skill bias. What we saw as the pandemic is that people with college degrees, people who have the opportunity to work remotely have been hit economically much less comparatively with people who are not able to work remotely. And that's disproportionately people who did not have access to higher education and college degrees.(04:52) –  We see a lot of formal sector jobs  falling away as a result of precautions taken to manage the virus. But as a result of this, we also see a proliferation in oftentimes lower wage on-demand or gig work playing out. There are many, several silver linings to take from this trend that we're seeing playing out, but there are also a lot of highly disruptive technologies in the space of robotics and information technology, especially in the AI space, that could lead to possible exciting futures, but they could also lead to some less ideal outcomes.(08:14) –  Some people might have found out that they're just as productive working from home, and they save time commuting. So some companies might have discovered that they're saving a lot of money on office space. So they might choose, even if it's not because of healthcare considerations, they might choose to stay remote. And that might become more of a norm.(10:41) – We see a whole new level of disparity across the board. The office, the workplace is in some ways a leveler, in that everyone has access to the same coffee machine, the same conference room, the same equipment, but as more of our work is distributed, that might not be the case.(13:19) –  I also want to shine a spotlight on the role that we human beings are playing in the process of facilitating the development of these technologies. And while we recognize that, we're building incredibly fabulously capable machines, really continuing to interrogate to what end and to whose benefit those are being built. Taking a more active stance in the future of work debate, and being more deliberate about choosing the direction of technological change when it comes to AI and other technologies as well is what is missing.(18:41) – We need to be realistic about our ability to quickly enough upskill everyone globally to keep pace with the technological advancement and think about how do we lower the barrier to entry, lower the barrier that's needed in terms of skill requirements for people to be able to use these technologies to their economic advantage and extract economic value from that and be able to use it for their earning opportunities. I'm genuinely curious to what extent certain jobs that are considered as low skilled or high-skilled, which we recognize as the flawed language of economics, where we're really what we're referring to is educational attainment and how much pre-training someone has.(28:24) – The benchmark that we hold our technology against is not these questions of what would make a worker's job easier or their output better. But rather this question of, is it going to be able to perform at the level of a human? Can we make a technology that will make a person,  that will then be able to do whatever a person can do? And there's this sort of fetishization in the AI sphere. And it comes from a really beautiful, fascinating space. The scifi nerd in me does really wonder, Oh man, what would it be like to create other ways of thought, what would it be like to develop these thinking machines.(31:04) – We have something like 8 billion humans, those humans now more than ever are in need of gainful jobs. And if we think of technological progress as the type of technological change that helps society prosper and overcome its economic condition, the last thing that we need to do is to be building machines that do what humans can already do better than them. And creates competition for those humans.(40:25) –  I work in the AI space because  that can be a thing that does bring about incredible opportunity and prosperity and new horizons of understanding and collaboration that we haven't even seen before. And that's really exciting to me. So, I wanted to just clarify that this stance is not one that says we shouldn't have AI. We shouldn't go down this road. We shouldn't build these technologies, but rather, that this technology isn't moving on its own, it's moving because our hands are doing the work, at least for the time being(44:01) – There's a lot of talk in conversations about structural issues and structural change. At the end of the day, these structures are built by us as people, the humans in the AI loop, and we have the power to shift it. And we also have the power to do things that we couldn't do before. Advertising Inquiries: <a href="https://redcircle.com/brands" rel="nofollow" target="_blank">https://redcircle.com/brands</a> Privacy &amp; Opt-Out: <a href="https://redcircle.com/privacy" rel="nofollow" target="_blank">https://redcircle.com/privacy</a>

How to Ensure Worker Well-Being in Artificial Intelligence with Katya Klinova and B Cavello of The Partnership on AI