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X-WR-CALNAME;VALUE=TEXT:Eventi DIAG
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TZID:Europe/Paris
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DTSTART:20241027T030000
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UID:calendar.28670.field_data.0@oba.diag.uniroma1.it
DTSTAMP:20260404T235300Z
CREATED:20250303T121423Z
DESCRIPTION:Nell'ambito della procedura selettiva per il reclutamento di 1 
 ricercatore atempo determinato tipologia A tempo determinato PNRR PE1 FAIR
  SPOKE 5 GSD 11/PHIL-03 - SSD PHIL-03/A\, presso il Dipartimento di Ingegn
 eria informatica\, automatica egestionale Antonio Ruberti\, bandita con D.
 D. n.12/2025\, Prot.n. 162 del 10/01/2025Piercosma Bisconti Lucidi terrà u
 n seminario pubblico in data 4/3/2025\, alle ore 16:00\, presso l'aula mag
 na del DIAG\, e in collegamento Zoom\, link alla videochiamata:https://uni
 roma1.zoom.us/j/88293503365?pwd=S241dDBhUThMN01icEt1UGJidjhXUT09Title: A U
 nified Framework for Measuring AI Trustworthiness: Integrating Intrinsic a
 nd Perceived DimensionsAbstract:The concept of AI trustworthiness is centr
 al to the development of reliable and ethically sound AI systems\, yet its
  definition remains fragmented between intrinsic system properties and hum
 an perceptions. While intrinsic trustworthiness—encompassing robustness\, 
 accuracy\, and transparency—is often discussed in technical and regulatory
  frameworks\, its interaction with perceived trustworthiness remains insuf
 ficiently formalized. This research proposes a comprehensive framework tha
 t quantifies AI trustworthiness by integrating these two dimensions\, link
 ing intrinsic properties with observer perceptions mediated by transparenc
 y\, agency locus\, and human oversight.To establish a robust definition of
  intrinsic trustworthiness\, we will analyze AI-related standards from the
  European Union (e.g.\, CEN-CENELEC) and international bodies such as ISO/
 IEC. These standards will provide a structured taxonomy of trustworthiness
  characteristics\, ensuring alignment with regulatory and technical benchm
 arks. Empirical studies will complement this by examining how discrepancie
 s between expected and observed AI behaviors influence perceived trust. Th
 e study will further explore how regulatory compliance with AI standards a
 ffects public trust and the adoption of AI technologies.Expected outcomes 
 include a validated metric for AI trustworthiness\, insights into the inte
 rplay between intrinsic and perceived trust\, and actionable recommendatio
 ns for developers and policymakers. By grounding our model in established 
 standards\, this research aims to provide a structured\, measurable\, and 
 regulatory-aligned approach to AI trustworthiness\, contributing to both t
 heoretical understanding and practical implementation in real-world AI sys
 tems. 
DTSTART;TZID=Europe/Paris:20250304T160000
DTEND;TZID=Europe/Paris:20250304T160000
LAST-MODIFIED:20250303T143344Z
LOCATION:Aula Magna\, DIAG
SUMMARY:Seminario pubblico di Piercosma Bisconti Lucidi - Piercosma Biscont
 i Lucidi
URL;TYPE=URI:http://oba.diag.uniroma1.it/node/28670
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