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Big & small data - Gender Gap digitale

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Convegno Cittadinanze 2017 - Torino 30 Novembre Essere selezionati per un posto di lavoro, la possibilità di aver un mutuo, il valore del premio di un'assicurazione: la nostra vita è condizionata da decisioni prese da sistemi automatici che utilizzano algoritmi che ci collocano in un modello e ci danno un punteggio, ma non lo fanno sempre in modo imparziale. Come scrive CAthy O'Neil autrice di - Weapons of Math Distruction "Gli algoritmi non sono pura matematica. Sono piuttosto opinioni umane incastonate in linguaggio matematico, e non meritano necessariamente la nostra fiducia" Le donne, che sono tuttora sotto rappresentate nei settori ad alta tecnologia informatica, hanno maggiori probablitià di essere vittime di queste discriminazioni?

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Big & small data - Gender Gap digitale

  1. 1. Big and Small data Donne e nuove tecnologie: contrastare il gender gap Eleonora Pantò Learning, Inclusion e Social Innovation 30 novembre 2017 Torino CLE
  2. 2. 2 2011 Software is eating the world
  3. 3. 3 Evolution of the desk
  4. 4. 4 Computational thinking Dottori, avvocati, insegnanti, allevatori, un qualunque mestiere. Il futuro di tutte queste professioni sarà pieno di Pensiero Computazionale. Medicina basata sui sensori, smart contract, analisi di dati nell'educazione, agricoltura di precisione: il successo dipenderà da quanto sarete bravi con il pensiero computazionale. Mi sono accorto di una tendenza interessante. Scegliete un qualsiasi settore X, dalla Archeologia alla Zoologia. Ci sono due possibilità: o esiste già “X Computazionale” o esisterà presto. E tutti lo considerano il futuro di quel settore. http://blog.stephenwolfram.com/2016/09/how-to-teach-computational-thinking/
  5. 5. 5 Scarsa presenza femminile nelle ICT 85% dei tecnici di Facebook and Yahoo sono uomini • Appcamp4girl • Girlswhocode • Women’s code collective • Railgirls RubyRails for Girls (Finland) • Shine for girls - Learning math through dance Megan Smith è stata CTO della Casa Bianca, in qualità di vicepresidente Google ha lanciato la campagna “Google’s Made With Code” per avvicinare le ragazze alla programmazione.
  6. 6. 6 …1961 Hidden figures Three brilliant African-American women at NASA -- Katherine Johnson (Taraji P. Henson), Dorothy Vaughan (Octavia Spencer) and Mary Jackson (Janelle Monáe) -- serve as the brains behind one of the greatest operations in history: the launch of astronaut John Glenn (Glen Powell) into orbit, a stunning achievement that restored the nation's confidence, turned around the Space Race and galvanized the world.
  7. 7. 7http://www.npr.org/sections/money/2014/10/21/357629765/when-women-stopped-coding
  8. 8. 8 Donne e futuro del lavoro Across all industries, almost half of respondents – 44% – said that both unconscious bias among managers and a lack of work-life balance were significant barriers to gender diversity in the workplace. Almost as many – 39% – pointed to a lack of female role models. Although women now outnumber men at university, and graduate in higher numbers, 36% of respondents still said there weren’t enough qualified women for the positions they’re looking to fill. Only 6% blamed a lack of parental leave, and 10% said there were no barriers. http://www3.weforum.org/docs/WEF_Future_of_Jobs.pdf
  9. 9. Unconscious Bias
  10. 10. 10 Hired by a computer ? http://www.pewinternet.org/2017/10/04/americans- attitudes-toward-hiring-algorithms/ Survey respondents were asked to read and respond to the following scenario: “Today, when companies are hiring they typically have someone read applicants’ resumes and conduct personal interviews to choose the right person for the job. In the future, computer programs may be able to provide a systematic review of each applicant without the need for human involvement. These programs would give each applicant a score based on the content of their resumes, applications or standardized tests for skills such as problem solving or personality type. Applicants would then be ranked and hired based on those scores.”
  11. 11. 11 “Even more remarkable—and even less widely understood—is that in many areas, performance gains due to improvements in algorithms have vastly exceeded even the dramatic performance gains due to increased processor speed.”—Report tothe Presidentand Congress: Designing a digitalfuture(2010) Algoritmo https://www.hsdl.org/?abstract&did=10223
  12. 12. 12 Algoritmo “The Industrial Revolution automated manual work and the Information Revolution did the same for mental work, but machine learning automates automation itself. Without it, programmers become the bottleneck holding up progress. With it the pace of progress picks up”—PedroDomingos, The Master Algorithm https://www.youtube.com/watch?v=B8J4uefCQMc
  13. 13. 13 Le AI e i pregiudizi AI has the potential to reinforce existing biases because, unlike humans, algorithms are unequipped to consciously counteract learned biases, researchers warn. Photograph: KTS Design/Getty Images/Science Photo Library RF https://www.theguardian.com/technology/2017/apr/13/ai-programs-exhibit-racist-and-sexist-biases-research-reveals#img-1
  14. 14. 14 Tay, il chatbot sessista https://www.theverge.com/2016/3/24/11297050/tay-microsoft-chatbot-racist " The more you chat with Tay, said Microsoft, the smarter it gets, learning to engage people through "casual and playful conversation." Unfortunately, the conversations didn't stay playful for long. Pretty soon after Tay launched, people starting tweeting the bot with all sorts of misogynistic, racist, and Donald Trumpist remarks. And Tay — being essentially a robot parrot with an internet connection — started repeating these sentiments back to users, proving correct that old programming adage: flaming garbage pile in, flaming garbage pile out.
  15. 15. 15 Combattere i pregiudizi degli algoritmi https://www.ted.com/talks/joy_buolamwini_how_i_m_fighting_bias_in_algorithms • Who codes matters • How we code matters • And Why we code matters
  16. 16. https://www.wired.com/story/machines-taught-by-photos-learn-a-sexist-view-of-women/ Machine-learning software trained on the datasets didn’t just mirror those biases, it amplified them. If a photo set generally associated women with cooking, software trained by studying those photos and their labels created an even stronger association. https://homes.cs.washington.edu/~my89/publications/bias.pdf
  17. 17. 17 Why algorithms aren’t working for women http://www.feministcurrent.com/2017/04/07/algorithms-arent-working-women/ But the real way to start changing this technology is to make sure that we all get involved. That means having more conversations about it, learning more about it, and really taking seriously the fact that the technology you use impacts you and the world around you.
  18. 18. 18 Math doesn’t cause bias, and Big Data is only partly to blame. The biggest source of bias in data analysis is and always will be people, both technicaland businesspeople, failing to admit that bias exists, failing to look for it, and failing to do anything constructive about it. Put biased data into an unbiased equation and you get biased results. I DATA ANALYST PERDONO DI VISTA LE PERSONE
  19. 19. 19 I big data non forniscono idee nuove. I big data sono dati, e i dati danno la priorità all’analisi rispetto alle emozioni. È difficile immaginare che i dati possano descrivere le qualità emotive a cui attribuiamo più valore: bello, cordiale, sexy, sorprendente, carino.
  20. 20. Grazie + 39 0114815139 epanto Eleonora Pantò | CSP eleonora.panto@gmail.com twitter.com/epanto pinterest.com/epanto facebook.com/eleonorapanto

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