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SUMMARY:Fighting Bad Information with AI - David Corney (Full Fact)
DTSTART:20231013T110000Z
DTEND:20231013T120000Z
UID:TALK206191@talks.cam.ac.uk
CONTACT:Michael Schlichtkrull
DESCRIPTION:Bad information ruins lives. It leads to bad decisions that pr
 omote hate\, damage people's health and hurt democracy. \nThat's why Full 
 Fact campaigns for better information in public life. A key part of this i
 s challenging specific incorrect or misleading claims.\n\nOur fact checkin
 g starts with us monitoring newspapers\, broadcast news and social media\,
  and we collect claims being made in public. We verify whether these claim
 s are clearly supported by the evidence\, and ask people to correct the re
 cord when they get things wrong. We develop new technology to counter misl
 eading claims by increasing the speed\, scale and impact of fact checking.
  We are not trying to replace fact checkers with technology\, but rather t
 o empower fact checkers with the best tools. After talking with many fact 
 checkers\, we've identified three key areas where technology can help:\n\n
 * Finding the most important thing to be fact checking each day\n* Knowing
  when someone repeats something they already know to be false\n* Checking 
 things in as close to real-time as possible\n\nIn this talk\, I will descr
 ibe the tools we are building and the technology behind them\, from simple
  keyword matching through information retrieval algorithms and large langu
 age models. I'll also describe the journey we've been on through the devel
 opment process\, what we've learned along the way and where we're going.\n
 \nDr David Corney joined Full Fact in 2019 as a data scientist specialisin
 g in natural language processing. He helps bring AI into Full Fact's tools
  to better support fact checkers and campaigners. This includes training l
 arge language models\; training regular machine learning models\; gatherin
 g and annotating data\; and working with fact checkers\, academics and sof
 tware engineers from round the world. David completed his PhD in machine l
 earning 20 years ago and has spent the intervening time working in academi
 a and for tech startups in London. He has contributed to several tools tha
 t analyse news articles\, social media and other sources of text\, as well
  as projects in visual neuroscience and botanical imaging.
LOCATION:Computer Lab\, SS03
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