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UID:pretalx-pydata-london-2026-9HLYEW@pretalx.com
DTSTART;TZID=GMT:20260607T114500
DTEND;TZID=GMT:20260607T123000
DESCRIPTION:Explainable AI is frequently invoked to make machine learning s
 ystems understandable and trustworthy. In real applications\, however\, ex
 planations are often expected to justify decisions and support action. Dra
 wing on experience with remote sensing–based risk monitoring\, this talk
  examines the gap between the guarantees of explainability methods and the
  expectations placed on them by different users. It discusses how explanat
 ions can inform practice\, how they can be misinterpreted\, and when focus
 ing on explainability may obscure deeper problems in models or data.
DTSTAMP:20260602T225543Z
LOCATION:Hardwick Hub
SUMMARY:What We Expect from XAI - A scientist’s experience between models
  and users - Alessandra Costantino
URL:https://pretalx.com/pydata-london-2026/talk/9HLYEW/
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