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SUMMARY:Understanding How It Works Defeats Mental Attribution - Yunlong Ca
 o
DTSTART;TZID=US/Central:20260717T103000
DTEND;TZID=US/Central:20260717T110000
DTSTAMP:20260726T091016Z
UID:pretalx-iacap-2026-ND8TVG@pretalx.iacapconf.org
DESCRIPTION:This paper argues for a new epistemic condition for machine me
 ntality: we are only justified to attribute mentality to systems whose int
 ernal workings we cannot fully understand. I call this the mechanistic opa
 city condition. Methodologically\, I argue that machine consciousness stud
 ies should be continuous with other minds studies. With an inference to th
 e best explanation argument\, I argue that a mechanistic explanation that 
 is better than a mental explanation always exists for a mechanistically tr
 ansparent system\, thus rendering consciousness attribution to them unjust
 ified. Despite behavioral similarities to humans\, current AI systems cann
 ot be considered conscious\, intelligent\, intentional\, or mental in impo
 rtant ways\, simply because we understand how they work. This condition ex
 plains intuitions about classic thought experiments (China Brain\, Blockhe
 ad\, Chinese Room) and provides principled AI consciousness skepticism wit
 hout biological chauvinism.
LOCATION:Apollo Auditorium
URL:https://pretalx.iacapconf.org/iacap-2026/talk/ND8TVG/
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