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During the municipal elections in spring 2017, a group of researchers and practitioners specialising in computer science, media and communication implemented a hate speech identification campaign with the help of an algorithm based on machine learning.
At the beginning of the campaign, the algorithm was taught to identify hate speech as diversely as possible, for example, based on the big data obtained from open chat groups. The algorithm learned to compare computationally what distinguishes a text that includes hate speech from a text that is not hate speech and to develop a categorisation system for hate speech. The algorithm was then used daily to screen all openly available content the candidates standing in the municipal elections had produced on Facebook and Twitter. The candidates' account information were gathered using the material in the election machine of the Finnish Broadcasting Company Yle.
All parties committed themselves to not accepting hate speech in their election campaigns. On the other hand, if the candidate used a personal Facebook profile instead of the page created and reported for the campaign, it was not included in the monitoring. Finnish word forms and the limited capability of the algorithm to interpret the context the same way humans do also proved to be challenging. The Perspective classifier developed by Google for the identification of hate speech has also suffered from the same problems in recognising the context and, for example, spelling mistakes.
Who wants to play, "Trigger the Algorithm" with false positives? "This mosaic is too dark. Let's use more white tiles here, and here."
(Score: 3, Informative) by iWantToKeepAnon on Sunday October 15 2017, @07:48PM (2 children)
"Happy families are all alike; every unhappy family is unhappy in its own way." -- Anna Karenina by Leo Tolstoy
(Score: 3, Interesting) by frojack on Sunday October 15 2017, @09:00PM
I'd suggest it was more like what use to be called an "expert system" which, like Bayes, had to be carefully trained, but once trained could diagnose all sorts of stuff. Expert systems usually required some knowledgeable person(s) to feed it questions and answers with some kind of rightness or wrongness applied. They found a lot of applicability in medicine allowing diagnoses of odd or rare diseases etc.
I would suspect the subject algorithm is precisely that: A Bayesian filter, and nothing more.
Computer science types tend to apply whatever fad they've just learned to every task that comes down the pike - until failure or new fad occurs. And if journalism majors were involved, well the algorithm could also be just as effective to classify emails into Empress vs A-Line camps.
No, you are mistaken. I've always had this sig.
(Score: 1, Interesting) by Anonymous Coward on Monday October 16 2017, @09:05AM
I'd say if a Bayes filter is not machine learning, then nothing is.
Note that learning does not imply intelligence.