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SUMMARY:The model is simple until proven otherwise - Anita Faul (Universit
 y of Cambridge)
DTSTART:20190509T120000Z
DTEND:20190509T133000Z
UID:TALK120814@talks.cam.ac.uk
CONTACT:James Fergusson
DESCRIPTION:Machine Learning and AI have enjoyed an unprecedented rise in 
 popularity. In academia as well as industry\, they are often viewed as the
  future solution to all problems. However\, systems have become so complex
  that it is no longer humanly comprehensibly\, how an algorithm arrives at
  an answer\, see for example "AAAS: Machine learning 'causing science cris
 is'" (https://www.bbc.co.uk/news/science-environment-47267081)\n\nIn some 
 cases\, companies refuse to disclose the proprietary algorithm. This has l
 ead to controversies such as the COMPAS algorithm giving scores on the lik
 elihood to re-offend. The organisation ProPublica claims that the software
  exhibits racial bias (https://www.propublica.org/article/how-we-analyzed-
 the-compas-recidivism-algorithm) which the company disputes (http://go.vol
 arisgroup.com/rs/430-MBX-989/images/ProPublica_Commentary_Final_070616.pdf
 ).\n\nAnother example is Amazon's gender bias recruitment tool (https://ww
 w.bbc.co.uk/news/technology-45809919). Partly to blame is the data used to
  train algorithms. If the data is biased\, then the algorithm will be. Mor
 e seriously\, it might exacerbate the bias\, since algorithms distill the 
 essential distinguishing features. If these are then highly correlated wit
 h black - white\, male - female\, we have a problem.\n\nWhile humans can a
 lso have bias\, they are also capable of realizing their world view is too
  simplistic. The talk presents work in progress of increasing the complexi
 ty of a model\, if the data suggests more features are necessary to model 
 the data. This approach aides to understand the "black magic" inside the "
 black box".
LOCATION:Kavli Large Meeting Room\, Kavli Building
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