BEGIN:VCALENDAR PRODID:-//Grails iCalendar plugin//NONSGML Grails iCalendar plugin//EN VERSION:2.0 CALSCALE:GREGORIAN METHOD:PUBLISH BEGIN:VEVENT DTSTAMP:20260602T205202Z DTSTART;TZID=Europe/Berlin:20260701T234500 DTEND;TZID=Europe/Berlin:20260701T234500 SUMMARY:PhilML'26 UID:20260606T115343Z-iCalPlugin-Grails@philevents-web-6b96c54f56-bljdq TZID:Europe/Berlin LOCATION:LMU Munich\, Munich\, Germany DESCRIPTION:

We are pleased to announce the latest iteration of PhilML\, from October 7-9 2026\, at the LMU Munich.  \;

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PhilML is an annual conference dedicated to the philosophy of machine learning. It addresses foundational epistemological\, ethical\, and social questions concerning machine learning from the perspective of analytic philosophy. The conference welcomes both (1) work that applies philosophical concepts and methods to gain insight into machine learning\, and (2) work that critically reflects on the philosophical and ethical implications of machine learning research. To foster close and productive exchange\, PhilML brings together philosophers and philosophically inclined machine learning researchers\, with an openness to engaging directly with scientific and mathematical details. \;

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Central topics that will be covered at the conference include:

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    Reflections on key topics such as learning\, benchmarking\, robustness\, explanation\, causality\, trust\, transparency\, reliability\, and fairness.

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    Novel considerations raised by foundation models e.g.\, agency\, alignment\, authorship\, mechanistic interpretability\, safety\, or homogenization.

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    Issues arising at the intersection of machine learning and public policy\, e.g. public services\, resource allocation\, or climate policy.

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    Implications of machine learning for the sciences or their methodology\, e.g. physics\, cognitive science\, biology\, social science\, or medicine.

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The conference has space for a number of contributing speakers. We are soliciting abstracts of up to 1\,000 words. Abstracts can be submitted through Oxford Abstracts: https://app.oxfordabstracts.com/stages/82713/submitter . Abstracts must be submitted by July 1\, 2026. Abstracts from scholars at all career stages are welcome\, including PhD students. \;

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We are excited to announce that the speakers at this year's conference will include: Sara Aronowitz\, Anne-Laure Boulesteix\, Ali Boyle\, \; Annemarie Friedrich\, Konstantin Genin\, Lily Hu\, Christoph Kern\, and Anders Sø\;gaard. \;

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There will also be a PhD student workshop on October 6\, 2026. The call for abstracts will be announced in due course. \;

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This iteration of PhilML is funded by the Munich Center for Machine Learning (MCML)\, Konrad Zuse School of Excellence in Reliable AI (relAI)\, and the Munich Center for Mathematical Philosophy (MCMP). \;

ORGANIZER;CN=Kate Vredenburgh;CN=Thomas Grote;CN=Tom F. Sterkenburg;CN=Timo Freiesleben: METHOD:PUBLISH END:VEVENT END:VCALENDAR