BEGIN:VCALENDAR VERSION:2.0 PRODID:IEEE vTools.Events//EN CALSCALE:GREGORIAN BEGIN:VTIMEZONE TZID:Europe/London BEGIN:DAYLIGHT DTSTART:20260329T020000 TZOFFSETFROM:+0000 TZOFFSETTO:+0100 RRULE:FREQ=YEARLY;BYDAY=-1SU;BYMONTH=3 TZNAME:BST END:DAYLIGHT BEGIN:STANDARD DTSTART:20251026T010000 TZOFFSETFROM:+0100 TZOFFSETTO:+0000 RRULE:FREQ=YEARLY;BYDAY=-1SU;BYMONTH=10 TZNAME:GMT END:STANDARD END:VTIMEZONE BEGIN:VEVENT DTSTAMP:20251112T194549Z UID:648E29E1-2600-4FE9-9C3A-A7A30ECD841C DTSTART;TZID=Europe/London:20251112T133000 DTEND;TZID=Europe/London:20251112T143000 DESCRIPTION:The growing integration of large language models across profess ional domains transforms how experts make critical decisions in healthcare \, education\, and law. While significant research effort focuses on getti ng these systems to communicate their outputs with probabilistic measures of reliability\, many consequential forms of uncertainty in professional c ontexts resist such quantification. A physician pondering the appropriaten ess of documenting possible domestic abuse\, a teacher assessing cultural sensitivity\, or a mathematician distinguishing procedural from conceptual understanding face forms of uncertainty that cannot be reduced to percent ages. This paper argues for moving beyond simple quantification toward ric her expressions of uncertainty essential for beneficial AI integration. We propose participatory refinement processes through which professional com munities collectively shape how different forms of uncertainty are communi cated. Our approach acknowledges that uncertainty expression is a form of professional sense-making that requires collective development rather than algorithmic optimization.\n\nCo-sponsored by: Ulster University\n\nSpeake r(s): Sylvie\, \n\nVirtual: https://events.vtools.ieee.org/m/505621 LOCATION:Virtual: https://events.vtools.ieee.org/m/505621 ORGANIZER:h.zheng@ulster.ac.uk SEQUENCE:11 SUMMARY:Beyond Quantification: Navigating Uncertainty in Professional AI Sy stems URL;VALUE=URI:https://events.vtools.ieee.org/m/505621 X-ALT-DESC:Description: <br /><p style="text-align: justify\;"><span style= "color: rgb(51\, 51\, 51)\; font-family: Garamond\, Helvetica\, serif\; fo nt-size: 18.75px\; font-style: normal\; font-variant-ligatures: normal\; f ont-variant-caps: normal\; font-weight: 300\; letter-spacing: normal\; orp hans: 2\; text-align: start\; text-indent: 0px\; text-transform: none\; wi dows: 2\; word-spacing: 0px\; -webkit-text-stroke-width: 0px\; white-space : normal\; text-decoration-thickness: initial\; text-decoration-style: ini tial\; text-decoration-color: initial\; display: inline !important\; float : none\;">The growing integration of large language models across professi onal domains transforms how experts make critical decisions in healthcare\ , education\, and law. While significant research effort focuses on gettin g these systems to communicate their outputs with probabilistic measures o f reliability\, many consequential forms of uncertainty in professional co ntexts resist such quantification. A physician pondering the appropriatene ss of documenting possible domestic abuse\, a teacher assessing cultural s ensitivity\, or a mathematician distinguishing procedural from conceptual understanding face forms of uncertainty that cannot be reduced to percenta ges. This paper argues for moving beyond simple quantification toward rich er expressions of uncertainty essential for beneficial AI integration. We propose participatory refinement processes through which professional comm unities collectively shape how different forms of uncertainty are communic ated. Our approach acknowledges that uncertainty expression is a form of p rofessional sense-making that requires collective development rather than algorithmic optimization.</span></p> END:VEVENT END:VCALENDAR