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UID:pretalx-adass2026-97NUVC@pretalx.adass.org
DTSTART;TZID=AWST:20261102T144500
DTEND;TZID=AWST:20261102T150000
DESCRIPTION:As implementing complex AI models with massive datasets becomes
  increasingly accessible\, at what point do we reach the limits of explain
 ability? "Simple" problems are easily tackled by AI when input parameters 
 are well-defined and the underlying processes are understood. However\, as
  datasets grow and the underlying processes become exponentially ill-defin
 ed\, we must question whether we are too quick to abandon explainability i
 n favor of rapid results. While societal pressures often encourage this tr
 ade-off\, the scientific community must resist it. Furthermore\, our human
  drive to seek out rules that lead to definitive conclusions can be mislea
 ding. Studies have shown that rats can easily outperform humans at certain
  pattern recognition tasks\, illustrating how our own cognitive biases can
  interfere with objective analysis.\n\nThis presentation will explore thes
 e challenges using examples of analysing over 100 million astronomical sou
 rces via multi-modal AI models\, demonstrating how an abundance of informa
 tion can sometimes yield increasingly misleading results. I will also disc
 uss how we utilise AI for our SRCnet software and data management stack at
  SKAO\, specifically examining the consequences of agentic coding and how 
 solving one problem inevitably introduces unintentional side effects. Ulti
 mately\, from analysing data to developing software\, mitigating these uni
 ntentional consequences and inherent biases has become the primary focus a
 nd responsibility of human problem-solving in the era of AI.
DTSTAMP:20261001T101731Z
LOCATION:Banquet Hall
SUMMARY:Unexplainable AI: rats\, super recognisers\, and multi-modal deep l
 earning for astronomical datasets - Alex Clarke
URL:https://pretalx.adass.org/adass2026/talk/97NUVC/
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