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DESCRIPTION:Beyond Explainable AI: Explanation\, Semantics\, Ontology\n\nBy
  Giancarlo Guizzardi\n\nProfessor University of Twente\n\nDate: Thursday\,
  April 23\, 2026\n	Time: 4:00 p.m. to 6:00 p.m.\n	Location: Online\n\nAttend
  online\n\nView poster\n\n\nAbstract\n\nWe live much of our lives immersed
  in the world of socially constructed entities such as money\, property de
 eds\, employments\, marriages\, legal liabilities\, and derivative transac
 tions\, which is increasingly grounded in the symbolic manipulation of dig
 ital representations. In this scenario\, institutional facts are made true
  by scattered pieces of information that reside in independent information
  silos and that were created by different organizational cultures\, throug
 h independent engineering processes\, in different moments in space and ti
 me. How can we safely create a unified\, transparent and consistent view o
 f social reality by putting together these scattered and concurrently deve
 loped information structures\, each of which carve out reality in potentia
 lly different ways? Properly addressing this question became even more cri
 tical with the diffusion of modern AI technologies that\, contra the EU AI
  Act\, are inherently opaque w.r.t. their reasoning and decision-making pr
 ocesses\, the digital representations they manipulate and\, hence\, how th
 ey create these institutional facts. In this talk\, I will present a metho
 d for engineering semantic transparency\, interoperability and contestabil
 ity by design in systems via explaining their information structures. Fina
 lly\, I will argue that the current trend in XAI (Explainable AI) in which
  “to explain is to produce a symbolic artifact” is an incomplete project a
 s these artifacts are not “inherently interpretable”\, and that they shoul
 d be taken as the beginning of the road to explanation\, not the end.\n\nI
 n summary\, this talk offers a unified vision of foundational AI for time-
 series and multimodal sensors\, combining robust temporal modeling\, cross
 -modal alignment\, and scalable representation learning to unlock new capa
 bilities in dynamic\, real-world environments.\n
DTSTART:20260423T200000Z
DTEND:20260423T220000Z
SUMMARY:MCCHE Precision Convergence Webinar Series with Giancarlo Guizzardi
URL:/desautels/channels/event/mcche-precision-converge
 nce-webinar-series-giancarlo-guizzardi-372506
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