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SUMMARY:The model is simple until proven otherwise. - Dr Anita Faul\, Maxw
 ell Centre\, University of Cambridge.
DTSTART:20190603T180000Z
DTEND:20190603T200000Z
UID:TALK124660@talks.cam.ac.uk
CONTACT:Dr Sobia Hamid
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). In some ca
 ses\, companies refuse to disclose the proprietary algorithm. This has led
  to controversies such as the COMPAS algorithm giving scores on the likeli
 hood to re-offend. The organisation ProPublica claims that the software ex
 hibits racial bias (https://www.propublica.org/article/how-we-analyzed-the
 -compas-recidivism-algorithm) which the company disputes (http://go.volari
 sgroup.com/rs/430-MBX 989/images/ProPublica_Commentary_Final_070616.pdf). 
 Another example is Amazon's gender bias recruitment tool (https://www.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. More seri
 ously\, it might exacerbate the bias\, since algorithms distil the essenti
 al distinguishing features. If these are then highly correlated with black
  - white\, male - female\, we have a problem. While humans can also have b
 ias\, they are also capable of realizing their world view is too simplisti
 c. The talk presents work in progress of increasing the complexity of a mo
 del\, 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:Cambridge\, further info available on registration which is requi
 red at www.meetup.com/Data-Insights-Cambridge/
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