BEGIN:VCALENDAR
VERSION:2.0
PRODID:unctad.org
BEGIN:VEVENT
UID:6a4d8d24bc1dd
DTSTART:20260709T140000Z
SEQUENCE:0
TRANSP:OPAQUE
DTEND:20260709T144500Z
LOCATION:Geneva\, Switzerland
SUMMARY:Better Data for AI – A Possible Task (WSIS 2026)
CLASS:PUBLIC
DESCRIPTION:Large language models and generative AI increasingly shape how 
 knowledge is used and shared\, but their reliability depends on the qualit
 y and provenance of training data. As AI expands into governance\, public 
 services and markets\, identifying trustworthy\, well-documented sources h
 as become a global priority.Official statistics provide a key public good:
  they are produced under professional standards\, transparent methodologie
 s and public oversight\, drawing on administrative records\, surveys and p
 rivately held data. They offer essential ground truth for validating\, cal
 ibrating and benchmarking AI outputs. In a context of growing data volumes
  and uneven quality\, authoritative statistical datasets help verify resul
 ts and ensure consistency with established evidence.Combining diverse data
  for AI raises challenges around quality assurance\, metadata\, representa
 tiveness\, bias\, intellectual property and privacy. The WSIS+20 and GDC p
 rocesses emphasize shared approaches for safe\, inclusive and interoperabl
 e digital ecosystems.This session will examine how statistical and geospat
 ial communities\, private companies\, NGOs and initiatives such as the Fin
 ancing for Development work on the Future of Data and the Trusted Data Obs
 ervatory can develop joint action on “better data for AI.” It will exp
 lore principles for identifying\, curating and sharing high-quality datase
 ts and metadata. Experts from national statistical offices\, international
  organizations\, academia and the private sector will discuss roles of off
 icial and private data\, practices for documenting AI-ready datasets\, inn
 ovations in AI-for-data\, and opportunities for cooperation to ensure the 
 next generation of AI is built on trusted\, accountable and globally benef
 icial data foundations.&lt\;p&gt\;Large language models and generative AI 
 increasingly shape how knowledge is used and shared\, but their reliabilit
 y depends on the quality and provenance of training data. As AI expands in
 to governance\, public services and markets\, identifying trustworthy\, we
 ll-documented sources has become a global priority.&lt\;/p&gt\;&lt\;p&gt\;
 Official statistics provide a key public good: they are produced under pro
 fessional standards\, transparent methodologies and public oversight\, dra
 wing on administrative records\, surveys and privately held data. They off
 er essential ground truth for validating\, calibrating and benchmarking AI
  outputs. In a context of growing data volumes and uneven quality\, author
 itative statistical datasets help verify results and ensure consistency wi
 th established evidence.&lt\;/p&gt\;&lt\;p&gt\;Combining diverse data for 
 AI raises challenges around quality assurance\, metadata\, representativen
 ess\, bias\, intellectual property and privacy. The WSIS+20 and GDC proces
 ses emphasize shared approaches for safe\, inclusive and interoperable dig
 ital ecosystems.&lt\;/p&gt\;&lt\;p&gt\;This session will examine how stati
 stical and geospatial communities\, private companies\, NGOs and initiativ
 es such as the Financing for Development work on the Future of Data and th
 e Trusted Data Observatory can develop joint action on “better data for 
 AI.” It will explore principles for identifying\, curating and sharing h
 igh-quality datasets and metadata. Experts from national statistical offic
 es\, international organizations\, academia and the private sector will di
 scuss roles of official and private data\, practices for documenting AI-re
 ady datasets\, innovations in AI-for-data\, and opportunities for cooperat
 ion to ensure the next generation of AI is built on trusted\, accountable 
 and globally beneficial data foundations.&lt\;/p&gt\;\n\nView meeting on u
 nctad.org\nhttps://unctad.org/meeting/better-data-ai-possible-task-wsis-20
 26
DTSTAMP:20260707T233500Z
END:VEVENT
END:VCALENDAR