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UID:news1432@ub-easyweb.ub.unibas.ch
DTSTAMP;TZID=Europe/Zurich:20260922T174221
DTSTART;TZID=Europe/Zurich:20261111T123000
SUMMARY:Trust\, Verify\, or Reject? Calibrating AI Use in Research Workflow
 s
DESCRIPTION:OverviewThis 15-minute session explores a central problem of sc
 ientific AI use: plausible outputs can be wrong in ways that are difficult
  for researchers to detect. Using brief examples from literature retrieval
 \, scientific writing\, and statistical coding\, the session contrasts sur
 face plausibility with diagnostic evidence and highlights findings from re
 cent AI benchmarks and human–AI collaboration research. It introduces th
 e CALIBRATE framework\, a risk-adjusted approach for deciding what to dele
 gate\, how to define correctness\, and when stronger verification is requi
 red. A short interactive challenge asks participants to judge a convincing
  AI output before the underlying failure is revealed. The key message is s
 imple: do not ask only whether an AI output looks right—ask what evidenc
 e could prove it wrong.\\r\\nTarget groupAnyone interested in the topic\\r
 \\nLecturerDr. Robin Segerer\, Information specialist psychology\\r\\n\\r\
 \nRegister here\\r\\nAll Coffee Lectures Health & Science in the Autumn Se
 mester 2026
X-ALT-DESC:<p><strong>Overview</strong><br />This 15-minute session explore
 s a central problem of scientific AI use: plausible outputs can be wrong i
 n ways that are difficult for researchers to detect. Using brief examples 
 from literature retrieval\, scientific writing\, and statistical coding\, 
 the session contrasts surface plausibility with diagnostic evidence and hi
 ghlights findings from recent AI benchmarks and human–AI collaboration r
 esearch. It introduces the CALIBRATE framework\, a risk-adjusted approach 
 for deciding what to delegate\, how to define correctness\, and when stron
 ger verification is required. A short interactive challenge asks participa
 nts to judge a convincing AI output before the underlying failure is revea
 led. The key message is simple: do not ask only whether an AI output looks
  right—ask what evidence could prove it wrong.</p>\n<p><strong>Target gr
 oup</strong><br />Anyone interested in the topic</p>\n<p><strong>Lecturer<
 /strong><br />Dr. Robin Segerer\, Information specialist psychology</p>\n\
 n<p><a href="https://www.eventbrite.ch/e/2001889364980" target="_blank"><s
 trong>Register here</strong></a></p>\n<p><a href="t3://page?uid=5121"><str
 ong>All Coffee Lectures Health &amp\; Science in the Autumn Semester 2026<
 /strong></a></p>
DTEND;TZID=Europe/Zurich:20261111T124500
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