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BEGIN:VEVENT
DTSTAMP:20261012T210910Z
LOCATION:Summit Pavilion
DTSTART;TZID=America/Los_Angeles:20261021T173000
DTEND;TZID=America/Los_Angeles:20261021T183000
UID:HFESAM_ASPIRE 2026_sess257_POST118@linklings.com
SUMMARY:Weather predictions in a noisy environment: The influence of autom
 ation reliability and transparency on user trust, performance and dependen
 ce.
DESCRIPTION:Haley Short (Colorado State University)\n\nThis study examined
  the potential interaction between automation reliability and automation t
 ransparency in the context of a weather-related decision to close a school
  in the face of a forecast blizzard. Participants received snowfall and cl
 osure advice from four different weather services dependent upon an automa
 tion weather model that differed in its degree of reliability (100% vs 67%
 ). Two of the services simply provided advice, and for two of them, this a
 dvice was accompanied by transparency in the form of the raw data, or weat
 her symptoms display that was input into the weather model. The results re
 vealed that degraded reliability of the automation lowered trust in and de
 pendence on the advice and hurt overall accuracy. In a significant interac
 tion, these degraded effects were mitigated by automation transparency. Th
 ese findings were interpreted in terms of a mechanism of optimal attention
  allocated (weighting) between the raw data transparency and the weather s
 ervice advice.\n\n
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