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Our research and analysis as featured in op-eds, podcast, news and TV appearances.
October 31, 2024
With less than two weeks until the election, Anne Makovec examines election disinformation and A.I.'s role in spreading it. She also covers Vice President Kamala Harris and former President Donald Trump's campaign efforts.
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March 29, 2024
Dr. Olaf provides a comprehensive analysis on critical moments facing major players across sectors, examining the implications of Boeing’s CEO stepping down, the significance of the US Justice Department and consumers suing Apple over anti-trust issues, and the strategies Fisker might employ to evade bankruptcy. This analysis not only highlights the immediate impacts but also considers the broader implications for the aerospace, technology, and automotive sectors.
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March 15, 2024
Dr. Olaf was in discussion with Ryan Yamamoto about whether the potential TikTok ban in the U.S. could be deemed a violation of free speech. Additionally, Groth delved into recent issues at Boeing and the Google CEO's apology following complaints about historical inaccuracies in the Gemini Artificial Intelligence app.
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March 4, 2024
The Tesla CEO claims the San Francisco-based AI company abandoned its mission statement by putting profits over benefiting humanity. UC Berkeley Haas School of Business professor Olaf Groth, PhD, explains what led up to this lawsuit.
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April 9, 2024
Cambrian Futures & Labs received its fifth U.S. patent for a data privacy platform designed to control and monetize data access transparently. Amidst rising data privacy concerns and legislation interest, this innovation aims to balance data utilization for growth with ensuring creators' data ownership and privacy. Additionally, Cambrian announced a distinguished Board of Advisers to guide its strategy in the emerging-tech economy.
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September 19, 2018
Machine meritocracy is here. Will we survive as we are or is there a need for a new Magna Carta? Article by Olaf Groth, Mark Nitzberg and Mark Esposito
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September 19, 2018
DARPA researchers want to know how and why machine learning algorithms get it wrongCertainly, there are some very detrimental consequences of inexplicability. What is the morality of in/transparency? In which domains do we need public or multi-stakeholder transparency to mitigate errors, biases, abuse or manipulation, etc.? Conversely, are there areas where it's justifiable that explanatory functions should be in the hands of only one party? - Public health decisions for which mass panics are a risk, individual or group identity issues where psychological tensions could arise, competitive decisions in corporate strategy, national security or intel analysis, crime investigations, international trade negotiations, dating / trading / shopping agents, personal bouncer bots, etc? Are the rules for the AI-age different than in pre-AI era, because it penetrates our lives more deeply, pervasively, and with more 2nd and 3rd order unintended consequences? Check the full article here.
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Wenn unsere Daten tatsächlich das Lebenselixier der digitalen Wirtschaft sind und wir mehr davon an allen möglichen Orten haben wollen, um unser Leben reicher, sicherer und bequemer zu machen, brauchen wir diese speziellen globalen digitalen Eigentumsrechte.
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