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who is writing my autobiography

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who is writing my autobiography

  1. 1. Fondation Maison des sciences de l'homme - Luca De Biase Who is writing my autobiography Dans la toile du Web
  2. 2. Am I the author of my life?
  3. 3. (the following is just a research idea for a media ecology approach)
  4. 4. Digital is the new environment ❖ DIGITALLY RECORDED KNOWLEDGE: ❖ 2000: 25% ———> 2013: 98% ❖ In 2013, 98% of information recorded by humans was in digital format; in the year 2000 it was 25% - Martin Hilbert, quoted by Victor Mayer-Schönberger and Kenneth Cukier in BIG DATA 2013
  5. 5. Shift happens ❖ Big data ❖ Robotics ❖ Nanotechnology ❖ Biotechnology ❖ Neuro-science ❖ Particle physics ❖ Additive production ❖ Artificial intelligence ❖ Collective intelligence ❖ Sharing economy ❖ Climate change ❖ Space exploration ❖ Startups ❖ Bitcoin
  6. 6. Am I the author of my life?
  7. 7. –Steve Jobs. June 12, 2005 “Your time is limited, so don’t waste it living someone else’s life”.
  8. 8. Steve Jobs said: «the computer is like a bicycle for our minds» http://www.bikeboom.info/efficiency/
  9. 9. the phone is even more than a bike
  10. 10. the phone is more like a prosthesis…
  11. 11. the phone is part of human anatomy, and it is writing our biography all the time
  12. 12. The phone has changed both the human body and the human environment
  13. 13. ❖ DIGITALLY RECORDED KNOWLEDGE: ❖ 2000: 25% ———> 2013: 98% ❖ In 2013, 98% of information recorded by humans was in digital format; in the year 2000 it was 25% - Martin Hilbert, quoted by Victor Mayer-Schönberger and Kenneth Cukier in BIG DATA 2013
  14. 14. The recording machine
  15. 15. The mobilizing machine
  16. 16. Our obligations towards other people enhanced by recording machines? or… “Mobilisation totale” is…
  17. 17. … our obligations towards a human-machine-system that is too big to know? “Mobilisation totale” is…
  18. 18. A robot suitcase which follows you is made by the Israeli startup NUA Robotics. It is full of sensors, computer vision and robotics
  19. 19. Teslasuit is a virtual reality device for full immersion experiences
  20. 20. Mercedes concept vehicle with drones on top
  21. 21. http://time.com/4398196/dallas-shooting-bomb-robot/ who is shooting?
  22. 22. http://blog.debiase.com/2016/07/01/fatal-error-il-primo-incidente-mortale-di-una-tesla- mentre-era-senza-pilota/ who is driving?
  23. 23. who is writing?
  24. 24. Facebook’s experiment
  25. 25. We show, via a massive (N = 689,003) experiment on Facebook, that emotional states can be transferred to others via emotional contagion, leading people to experience the same emotions without their awareness. We provide experimental evidence that emotional contagion occurs without direct interaction between people (exposure to a friend expressing an emotion is sufficient), and in the complete absence of nonverbal cues. http://www.pnas.org/content/111/24/8788.full “Experimental evidence of massive-scale emotional contagion through social networks”
  26. 26. so who is writing?
  27. 27. ❖ persuasive interfaces ❖ secret algorithms ❖ unequal control of data
  28. 28. B.J. Fogg’s model Technologies can be persuasive
  29. 29. Designer’s and users’ values are embedded in the platform
  30. 30. Mind and mobilisation ❖ Computer is a bike for the mind: it supports human rational abilities augmenting and speeding them ❖ Computer is a bike for the mind: it changes human strategies for memorization ❖ Computer is a bike for the mind: it is a medium for managing and enhancing human relational habits
  31. 31. Interface and mobilisation ❖ Human-Computer interface can be a very persuasive technology ❖ All computer enhanced activities are fully recorded with linear and complex consequences ❖ All complex activities generate big data and can only be managed by algorithms and machine learning
  32. 32. Algorithm and mobilisation ❖ Algorithms which we hardly understand ❖ Algorithms which we are hardly aware of ❖ Algorithms which are often kept secret
  33. 33. Mind and mobilisation ❖ Mobilisation happens when recording becomes documenting thus generating social obligations… ❖ In the meanwhile, recording is being linked to machine learning and algorithms which are making choices that most humans don’t understand or are not aware of… ❖ Thus, humans who don’t know and don’t understand are letting something else write their autobiography?
  34. 34. Frank Pasquale The Black Box Society The Secret Algorithms that Control Money and Information Catherine Taylor story. ChoicePoint makes a mistake, Catherine doesn’t find a job and a house anymore… http://blog.debiase.com/2016/10/08/ lasimmetria-della-trasparenza-la-realta- autoritaria-dellingiustizia-informativa/
  35. 35. Cathy O’Neil Weapons of Math Destruction How Big Data increases inequality and threatens democracy We live in the age of the algorithm. Increasingly, the decisions that affect our lives—where we go to school, whether we get a car loan, how much we pay for health insurance—are being made not by humans, but by mathematical models. In theory, this should lead to greater fairness: Everyone is judged according to the same rules, and bias is eliminated. But as Cathy O’Neil reveals in this shocking book, the opposite is true. The models being used today are opaque, unregulated, and uncontestable, even when they’re wrong. Most troubling, they reinforce discrimination: If a poor student can’t get a loan because a lending model deems him too risky (by virtue of his race or neighborhood), he’s then cut off from the kind of education that could pull him out of poverty, and a vicious spiral ensues. Models are propping up the lucky and punishing the downtrodden, creating a “toxic cocktail for democracy.” Welcome to the dark side of Big Data. http://www.goodreads.com/book/show/28186015-weapons-of-math-destruction
  36. 36. BREAKING THE BLACK BOX What Facebook Knows About Youby Julia Angwin, Terry Parris Jr. and Surya Mattu, ProPublica September 28, 2016 https://www.propublica.org/article/breaking-the-black-box-what-facebook-knows-about-you
  37. 37. WE LIVE IN AN ERA of increasing automation. Machines help us not only with manual labor but also with intellectual tasks, such as curating the news we read and calculating the best driving directions. But as machines make more decisions for us, it is increasingly important to understand the algorithms that produce their judgments.We’ve spent the year investigating algorithms, from how they’ve been used to predict future criminals to Amazon’s use of them to advantage itself over competitors. All too often, these algorithms are a black box: It’s impossible for outsiders to know what’s going inside them. Our first stop: Facebook and your personal data. https://www.propublica.org/article/breaking-the-black-box-what-facebook-knows-about-you
  38. 38. BREAKING THE BLACK BOX When Algorithms Decide What You Payby Julia Angwin, Terry Parris Jr. and Surya Mattu, ProPublica October 5, 2016 https://www.propublica.org/article/breaking-the-black-box-when-algorithms-decide-what- you-pay
  39. 39. Every website you visit is created, literally, the moment you arrive. Each element of the page — the pictures, the ads, the text, the comments — live on computers in different places and are sent to your device when you request them. That means that it’s easy for companies to create different web pages for different people. Sometimes that customization is helpful, such as when you see search results for restaurants near you. Sometimes it can be creepy, such as when ads follow you around from website to website. And sometimes customization can cost you money, research has shown. Orbitz showed higher-priced hotels to owners of Mac computers, for instance. Staples offered the same products at higher prices to people living in certain ZIP codes. Last year, we found that The Princeton Review was charging different prices for its online SAT tutoring course in different ZIP codes. In some ZIP codes, the course cost $6,600; in others that same course was offered for as much as $8,400. https://www.propublica.org/article/breaking-the-black-box-when-algorithms-decide-what- you-pay
  40. 40. Frank Pasquale The Black Box Society Designers values are embedded in algorithms - racial bias - mistaken data - fraudolent misinformation We feed databases with data that are secretly processed by unknown algorithms to generate autobiographies which we didn’t write
  41. 41. There is a problem ❖ Persuasive interfaces, secret algorithms, unequal control of data shape the eco-cultural niches in which humans and machines co-evolve ❖ The shaping happens accordingly with the values embedded in the design and development of the platforms
  42. 42. Mind and mobilisation ❖ In eco-cultural niches humans and machines co-evolve ❖ Mobilization happens according to that co-evolution ❖ Designer values, users’ knowledge, cultural context are embedded in the emerging system, which then shapes individual and collective stories
  43. 43. of course, we need to questions this in order to write our own autobiography
  44. 44. ❖ Jacques Le Goff, Saint Louis: biography is written by looking into documents which mirror the values of those that have collected or recorded the data. ❖ The idea of individual person has a history. And its biography is also the story of shared models about humanity or human role in history.
  45. 45. ❖ An autobiography is about facts of a life, generated by a body, in an environment ❖ Both context and content develop in the eco-cultural niche in which the human-machine “system” evolves ❖ Default decisions happen in relation with the functioning of the body and the environment, and since both of them are shaped by the change introduced by digital technology, the design of these generates action ❖ Present action has consequences over the next set of technological decisions
  46. 46. Shift happens ❖ Big data ❖ Robotics ❖ Nanotechnology ❖ Biotechnology ❖ Neuro-science ❖ Particle physics ❖ Additive production ❖ Artificial intelligence ❖ Collective intelligence ❖ Sharing economy ❖ Climate change ❖ Space exploration ❖ Startups ❖ Bitcoin
  47. 47. –The Economist “Economics is the science that studies why its predictions didn’t work”.
  48. 48. –Institute for the Future “There are no facts in the future, only narratives”
  49. 49. Narratives ❖ Finance ❖ Technology ❖ Ecology
  50. 50. Narratives Finance The only judge is profit. If humans cost more than robots they will be replaced by robots. If a starup is better financed wins over a better startup. The rest is secondary.
  51. 51. Narratives Tecnology What works wins. Every winning technology beats everything else. Every new version is better than the previous. Exponential laws help understand that technological progress is inevitable. Resistance is futile.
  52. 52. Narratives Ecology Everything is linked to everything else. Phenomena co-evolve. Regularities emerge in a complex system. The more biodiversity the better the ecosystem. Sustainability is environmental, social and cultural. Every growth has a limit. Every mutation needs its eco-cultural niche.
  53. 53. Media ecology, narratives, embedded values…
  54. 54. Knowledge sets us free ❖ If we know the power of interfaces, algorithms and complex systems… ❖ … then we understand how we co-evolve in our eco- cultural niches with our machines ❖ Consequence? Adaptation. Maybe acceptance of our being plural. In a reality emerging in a system that innovators are able to reveal. But while they do so, they also embed their values in their revealing forms…
  55. 55. Civic media ❖ Social media help people meet other people they like ❖ People need to meet also people they don’t necessarily like, when they have something to do together ❖ “Civic media” need to be designed in order to meet that need ❖ “Civic media” can be the next big thing