BEGIN:VCALENDAR
VERSION:2.0
PRODID:-//132.216.98.100//NONSGML kigkonsult.se iCalcreator 2.20.4//
BEGIN:VEVENT
UID:20250509T135105EDT-0977ufPcc0@132.216.98.100
DTSTAMP:20250509T175105Z
DESCRIPTION:STOP SHARING DATA: Visiting Algorithms\, Swarm Learning and Nex
 t Generation FAIR (Federated AI Ready) Principles and Practice\n\nBy Baren
 d Mons\n\nLeiden University\n\nDate: October 31\, 2024\n	Time: 11:00am to 1
 :00pm\n\nRegister & watch the webinar\n\nView poster\n\nAbstract\n\nThe ra
 pid developments in the field of machine learning have also brought along 
 some existential challenges\, which are in essence all related to the broa
 d concept of ‘trust’. Aspects of this broad concept include trust in the o
 utput of any ML process (and the prevention of black boxes\, hallucination
 s and so forth). The very trust in science is at stake\, especially now th
 at LLMs can generate ‘good-looking nonsense’ and paper mills come up in re
 sponse to the perverse reward systems in current research environments. Th
 e other side of the same coin is that ML\, if nor properly controlled will
  also break through security and privacy barriers and violate GDPR and oth
 er Ethical\, Legal and Societal barriers\, including equitability. In addi
 tion\, the existence of data ‘somewhere’ by no means automatically implies
  its actual Reusability. This includes the by now well established four el
 ements of the FAIR principles: Much data is not even Findable\, if found\,
  not Accessible under well defined conditions\, and if accessed not Intero
 perable (understandable by third parties and machines) and this results in
  the vast majority of data and information not being Reusable without viol
 ation of copyrights\, privacy regulations or the basic conceptual models t
 hat implicitly or explicitly underpin the query or the deep learning algor
 ithm. Now that more and more data will also be ‘independently’ used by mac
 hines\, all these challenges will be severely aggravated. This keynote wil
 l address how ‘data visiting’ as opposed to classical ‘data sharing’\, whi
 ch carries the onnotation of data downloads\, transport and loosing contro
 l\, mitigates most\, if not all\, the unwanted side effects of classical ‘
 data sharing’. For federated data visiting\, the data should be FAIR in an
  additional sense or perspective\, they should be ‘Federated\, AI-Ready’\,
  so that visiting algorithms can answer questions related to Access Contro
 l\, Consent\, Format\, and can read rich (FAIR) metadata about the data it
 self to determine whether they are ‘fit for purpose’ and machine actionabl
 e (i.e. FAIR digital Objects\, or Machine Actionable Units). The ‘fitness 
 for purpose’ concept goes way beyond (but includes) information about meth
 ods\, quality\, error bars etc. The ‘immutable logging’ of all operation o
 f visiting algorithms is crucial\, especially when self learning algorithm
 sin ‘swarm learning’ are being used. Enough to keep us busy for a while.\n
DTSTART:20241031T150000Z
DTEND:20241031T170000Z
SUMMARY:MCCHE Precision Convergence Webinar Series with Barend Mons   
URL:/desautels/channels/event/mcche-precision-converge
 nce-webinar-series-barend-mons-360618
END:VEVENT
END:VCALENDAR