Prof. Christian Peukert z Uniwersytetu w Lozannie z wykładem na WNE
„AI and the Dynamic Supply of Training Data” to tytuł seminarium z mikroekonomii, które przedstawi prof. Christian Peukert (badacz Uniwersytetu w Lozannie). W prowadzonych badaniach koncentruje się m.in. na wpływie cyfryzacji na konsumentów, firmy i rynki oraz na tym, jak te zmiany kształtują bodźce do innowacji.
Osoby zainteresowane tematyką spotkania zapraszamy do udziału o godz. 17:00 (sala B002, WNE). Istnieje również możliwość połączenia zdalnego. W celu otrzymania linku do spotkania prosimy o kontakt mailowy: xasFb&pT@|_Pd3wWo8h*yK]#[iTc0Rg[2WtN2m&o`Uw^3j4 (chętnie udostępnimy link).
Więcej informacji nt. działalności naukowej prof. Peukerta - na stronie: https://www.christian-peukert.com/. Natomiast abstrakt planowanej prelekcji - prezentujemy poniżej.
Cykl spotkań seminaryjnych w mikroekonomii koordynuje prof. Łukasz Grzybowski.
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Artificial intelligence (AI) systems rely heavily on human-generated data, yet the people behind that data are often overlooked. Human behavior can play a major role in AI training datasets, be it in limiting access to existing works or in deciding which types of new works to create or whether to create any at all. We examine creators' behavioral change when their works become training data for commercial AI. Specifically, we focus on contributors on Unsplash, a popular stock image platform with about 6 million high-quality photos and illustrations. In the summer of 2020, Unsplash launched a research program and released a dataset of 25,000 images for commercial AI use. We study contributors' reactions, comparing contributors whose works were included in this dataset to contributors whose works were not. Our results suggest that treated contributors left the platform at a higher-than-usual rate and substantially slowed down the rate of new uploads. Professional photographers and more heavily affected users had a stronger reaction than amateurs and less affected users. We also show that affected users changed the variety and novelty of contributions to the platform, which can potentially lead to lower-quality AI outputs in the long run. Our findings highlight a critical trade-off: the drive to expand AI capabilities versus the incentives of those producing training data. We conclude with policy proposals, including dynamic compensation schemes and structured data markets, to realign incentives at the data frontier.