BEGIN:VCALENDAR VERSION:2.0 PRODID:ILLC Website X-WR-TIMEZONE:Europe/Amsterdam BEGIN:VTIMEZONE TZID:Europe/Amsterdam X-LIC-LOCATION:Europe/Amsterdam BEGIN:DAYLIGHT TZOFFSETFROM:+0100 TZOFFSETTO:+0200 TZNAME:CEST DTSTART:19700329T020000 RRULE:FREQ=YEARLY;BYMONTH=3;BYDAY=-1SU END:DAYLIGHT BEGIN:STANDARD TZOFFSETFROM:+0200 TZOFFSETTO:+0100 TZNAME:CET DTSTART:19701025T030000 RRULE:FREQ=YEARLY;BYMONTH=10;BYDAY=-1SU END:STANDARD END:VTIMEZONE BEGIN:VEVENT UID:/NewsandEvents/Archives/2018/newsitem/9915/14- --15-July-2018-Workshop-on-Explainable-Artificial- Intelligence-XAI-Stockholm-Sweden DTSTAMP:20180426T164129 SUMMARY:Workshop on Explainable Artificial Intelli gence (XAI), Stockholm, Sweden DTSTART;VALUE=DATE:20180714 DTEND;VALUE=DATE:20180715 LOCATION:Stockholm, Sweden DESCRIPTION:Explainable AI (XAI) systems embody ex planation processes that allow users to gain insig ht into the system's models and decisions, with th e intent of improving the user's performance on a related task. This raises several questions, such as: how should explainable models be designed? How should user interfaces communicate decision makin g? What types of user interactions should be suppo rted? How should explanation quality be measured? These questions are of interest to researchers, pr actitioners, and end-users, independent of what AI techniques are used. Solutions can draw from seve ral disciplines, including cognitive science, huma n factors, and psycholinguistics. We welcome/enco urage submissions relevant to the topic of Explain able AI (XAI). Authors may submit long papers (6 p ages plus up to one page of references) or short p apers (4 pages plus up to one page of references). X-ALT-DESC;FMTTYPE=text/html:
Explainab le AI (XAI) systems embody explanation processes t hat allow users to gain insight into the system's models and decisions, with the intent of improving the user's performance on a related task. This ra ises several questions, such as: how should explai nable models be designed? How should user interfac es communicate decision making? What types of user interactions should be supported? How should expl anation quality be measured? These questions are o f interest to researchers, practitioners, and end- users, independent of what AI techniques are used. Solutions can draw from several disciplines, incl uding cognitive science, human factors, and psycho linguistics.
We welcome/encou rage submissions relevant to the topic of Explaina ble AI (XAI). Authors may submit long pap ers (6 pages plus up to one page of references) or short papers (4 pages plus up to one pag e of references).