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UID:pretalx-euroscipy-2026-8KCT3D@pretalx.com
DTSTART;TZID=CET:20260721T144000
DTEND;TZID=CET:20260721T151000
DESCRIPTION:LLM-as-a-Judge systems are increasingly deployed in high-stakes
  settings - screening job applicants\, triaging medical cases\, assessing 
 credit risk\, and flagging legal exposure. As the EU AI Act takes effect i
 n August 2026 with penalties up to €35M for biased high-risk systems\, o
 rganizations are investing heavily in fairness audits. But passing a bias 
 check does not guarantee fairness. Standard Python fairness pipelines rare
 ly detect this shift. In a controlled hiring experiment on real resumes\, 
 we demonstrate how alignment and potentially bias-mitigation techniques ca
 n reduce aggregate disparities while redistributing harm across intersecti
 onal subgroups.
DTSTAMP:20260603T185749Z
LOCATION:Room 1.19 (Ground Floor\, Shannon)
SUMMARY:The Illusion of Compliance: Auditing LLM-as-a-Judge Systems - Vasu 
 Sharma
URL:https://pretalx.com/euroscipy-2026/talk/8KCT3D/
END:VEVENT
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UID:pretalx-euroscipy-2026-H9UDLT@pretalx.com
DTSTART;TZID=CET:20260723T090000
DTEND;TZID=CET:20260723T103000
DESCRIPTION:Large language models (LLMs) have become central to modern scie
 ntific computing\, yet for most practitioners they remain opaque systems -
  input goes in\, text comes out\, and the internal mechanism is a mystery.
  Mechanistic interpretability (MI) is the emerging discipline of reverse-e
 ngineering what specific components of a neural network actually *do*.\nUs
 ing Andrej Karpathy's `microgpt` - a fully self-contained\, 200-line\, dep
 endency-free GPT implementation in pure Python - as our subject\, we syste
 matically dissect what a trained language model has learned. No PyTorch\, 
 no specialised ML frameworks: just the familiar tools applied to a genuine
 ly novel problem.\n\nThe model is tiny by design: 4\,192 parameters\, a 27
 -token vocabulary (a–z + a special token)\, trained on 32\,000 names in 
 roughly one minute on a laptop. This makes it the ideal subject for interp
 retability work - every attention weight is inspectable\, every embedding 
 printable\, every head ablatable. The scientific question driving the tuto
 rial is: *"What has this model actually learned about the structure of nam
 es?"*
DTSTAMP:20260603T185749Z
LOCATION:Room 1.38 (Ground Floor\, Turing)
SUMMARY:A Hands-On Introduction to Mechanistic Interpretability - Vasu Shar
 ma
URL:https://pretalx.com/euroscipy-2026/talk/H9UDLT/
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