• Machines That Fail Us #5: "The shape of AI to come"
    Jul 4 2024

    The AI we have built so far comes with many different shortcomings and concerns. At the same time, the AI tools we have today are the product of specific technological cultures and business decisions. Could we just do AI differently? For the final episode of “Machines That Fail Us”, we are joined by a leading expert on the intersection of emerging technology, policy, and rights. With Frederike Kaltheuner, founder of the consulting firm new possible and a Senior Advisor to the AI NOW institute, we discussed the shape of future AI and of our life with it.

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    30 mins
  • Machines That Fail Us #4: Building different AI futures
    Jun 13 2024

    We don’t necessarily have to build artificial intelligence the way we’re doing it today. To make AI really inclusive we must look beyond Western techno-cultures and beyond our understanding of technology being either utopian or dystopian. How could our AI future look different? We asked Prof. Payal Arora, a Professor of Inclusive AI Cultures at Utrecht University.

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    34 mins
  • Machines That Fail Us #3 Errors and biases: tales of algorithmic discrimination
    May 16 2024

    The records of biases, discriminatory outcomes, and errors as well as the societal impacts of artificial intelligence systems is now widely documented. However, the question remains: How is the struggle for algorithmic justice evolving? We asked Angela Müller, Executive Director of AlgorithmWatch Switzerland.

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    27 mins
  • Machines That Fail Us #2: Following the AI beat – algorithms making the news
    Apr 18 2024

    What’s the role of journalism in making sense of AI and its errors? With Melissa Heikkilä, senior reporter at the MIT Technology Review. Host: Dr. Philip Di Salvo.

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    27 mins
  • Machines That Fail Us #1: Making sense of the human error of AI
    Mar 20 2024

    What are the errors that artificial intelligence systems can make and what’s their impact on humans? The Human Error Project team discusses the results of their own research into AI errors and algorithmic profiling.

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    40 mins