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Google employees are lining up to trash Google’s AI ethics council
by Arturo Di Corinto April 6, 2019

April 6, 2019

FYI Google employees are lining up to trash Google’s AI ethics council The independent council was meant to help Google’s public image, but the blowback shows just how fraught—and politicized—use of AI is becoming. - by Will Knight <https://www.technologyreview.com/profile/will-knight/> - April 1, 2019 https://www.technologyreview.com/s/613253/googles-ai-council-faces-blowback… -- Arturo Di Corinto www.dicorinto.it ---------------------------------> AVVERTENZE AI SENSI DEL D.Lgs. 196/2003 Le informazioni contenute in questo messaggio di posta elettronica e/o nel/i file/s allegato/i sono da considerarsi strettamente riservate. Il loro utilizzo è consentito esclusivamente al destinatario del messaggio, per le finalità indicate nel messaggio stesso. Qualora riceviate questo messaggio senza esserne il destinatario, Vi preghiamo cortesemente di darcene notizia via e-mail e di procedere alla distruzione del messaggio stesso, cancellandolo dal Vostro sistema. Conservare il messaggio stesso, divulgarlo anche in parte, distribuirlo ad altri soggetti, copiarlo, od utilizzarlo per finalità diverse, costituisce comportamento contrario ai principi dettati dal D.Lgs. 196/2003

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Hey Google, sorry you lost your ethics council, so we made one for you
by J.C. DE MARTIN April 6, 2019

April 6, 2019

https://www.technologyreview.com/s/613281/google-cancels-ateac-ai-ethics-co… (Sent from my wireless device; please excuse brevity and typos (if any))

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From AI to ML to AI: On Swirling Nomenclature & Slurried Thought – Approximately Correct
by J.C. DE MARTIN April 6, 2019

April 6, 2019

http://approximatelycorrect.com/2018/06/05/ai-ml-ai-swirling-nomenclature-s… (Sent from my wireless device; please excuse brevity and typos (if any))

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Why Wolfram Tech Isn’t Open Source—A Dozen Reason
by Giacomo Tesio April 5, 2019

April 5, 2019

https://blog.wolfram.com/2019/04/02/why-wolfram-tech-isnt-open-source-a-doz… At the risk of provoking the fundamentalist end of the open-source community, I thought I would share some of my views in this blog. [...] Much of this blog could be summed up with two answers: (1) free, open-source software can be very good, but it isn’t good at doing what we are trying to do; with a large fraction of the reason being (2) open source distributes design over small, self-assembling groups who individually tackle parts of an overall task, but large-scale, unified design needs centralized control and sustained effort. I came up with 12 reasons why I think that it would not have been possible to create the Wolfram technology stack using a free and open-source model. I would be interested to hear your views in the comments section below the blog. - A coherent vision requires centralized design - High-level languages need more design than low-level languages - You need multidisciplinary teams to unify disparate fields - Hard cases and boring stuff need to get done too - Crowd-sourced decisions can be bad for you - Our developers work for you, not just themselves - Unified computation requires unified design - Unified representation requires unified design - Open source doesn’t bring major tech innovation to market - Paid software offers an open quid pro quo - It takes steady income to sustain long-term R&D - Bad design is expensive [...] FOSS development tends to react to immediate user needs—specific functionality, existing workflows or emulation of existing closed-source software. Major innovations require anticipating needs that users do not know they have and addressing them with solutions that are not constrained by an individual’s experience. [...] Open source often does create ecosystems that encourage many small-scale innovations, but while bolder innovations do widely exist at the early experimental stages, they often fail to be refined to the point of usefulness in large-scale adoption.

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When a black box is valuable
by antonio.vetro＠polito.it April 5, 2019

April 5, 2019

Il bicchiere mezzo pieno http://science.sciencemag.org/content/364/6435/26?rss=1 AV == Sent from my wireless device ==

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Comment Facebook compte empêcher toute ingérence et manipulation pendant les européennes
by Alberto Cammozzo April 5, 2019

April 5, 2019

<https://www.lemonde.fr/pixels/article/2019/04/05/ingerence-etrangere-le-pla…> Ancien procureur spécialisé dans la cybercriminalité, un temps conseiller de Barack Obama à la Maison Blanche, Nathaniel Gleicher dirige désormais, chez Facebook, les équipes chargées de contrer les piratages informatiques et les opérations de propagande. Basé en Californie, il a passé trois semaines en Europe depuis le début de l’année à enchaîner les préparatifs pour l’élection européenne. Depuis 2016 et l’élection présidentielle américaine, la perspective de voir le scrutin manipulé par une puissance étrangère via les réseaux sociaux hante les responsables politiques. Nous avons rencontré Nathaniel Gleicher dans les locaux français de Facebook, jeudi 4 avril. Facebook est attendu au tournant pour les élections européennes. Quelle est votre stratégie ? Les élections européennes font partie de nos principales priorités. C’est un énorme défi : chaque pays européen organise une forme d’élection locale. Nous avons une équipe qui se consacre aux élections européennes depuis un petit moment. Nous avons réalisé une évaluation des menaces et des risques qui pèsent sur l’élection, il y a un mois et demi. Nous avons récemment organisé un exercice à Bruxelles : nous avons réuni des gens venus de toute l’entreprise pour simuler l’élection et anticiper les menaces qui pourraient émerger.

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data as commons
by Giuseppe Attardi April 5, 2019

April 5, 2019

This is an idea that I have been proposing since a while (the first time at the GARR Conference 2018 in Cagliari): "Under this system, every company above a certain size, say, those with more than a ten percent share of the market, that systematically collects and analyzes data would have to let other companies in the same market access a subset of its data. " My argument was that personal data has infinitesimal value to individual users, therefore they are quite willing to relinquish it in exchange for what they consider some useful facility. However large collections of individual data have a huge value: but such a value is a collective one. Hence it should be considered a Commons. As with Commons, they should be not owned by anyone and cared for by a public organization. Who invests in collecting those data should have the possibility to exploit it according to the rules established by the Commons and maintain them available also for others to use on a fair basis. Any entity that reaches a relevant market share should be subject to this provisions. https://www.foreignaffairs.com/articles/world/2018-08-13/big-choice-big-tech

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New EU Report: “Competition policy for the digital era”
by J.C. DE MARTIN April 5, 2019

April 5, 2019

http://ec.europa.eu/competition/publications/reports/kd0419345enn.pdf (Sent from my wireless device; please excuse brevity and typos (if any))

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Fwd: Facebook’s Ad Algorithm Is a Race and Gender Stereotyping Machine, New Study Suggests
by Alberto Cammozzo April 5, 2019

April 5, 2019

<https://theintercept.com/2019/04/03/facebook-ad-algorithm-race-gender/> Facebook’s Ad Algorithm Is a Race and Gender Stereotyping Machine, New Study Suggests _How exactly Facebook_ decides who sees what is one of the great pieces of forbidden knowledge in the information age, hidden away behind nondisclosure agreements, trade secrecy law, and a general culture of opacity. New research <https://arxiv.org/abs/1904.02095> from experts at Northeastern University, the University of Southern California, and the public-interest advocacy group Upturn doesn’t reveal how Facebook’s targeting algorithms work, but does show an alarming outcome: They appear to deliver certain ads, including for housing and employment, in a way that aligns with race and gender stereotypes — even when advertisers ask for the ads to be exposed a broad, inclusive audience. There are two basic steps to advertising on Facebook. The first is taken by advertisers when they choose certain segments of the Facebook population to target: Canadian women who enjoy badminton and Weezer, lacrosse dads over 40 with an interest in white genocide <https://theintercept.com/2018/11/02/facebook-ads-white-supremacy-pittsburgh…>, and so forth. The second is taken by Facebook, when it makes an ad show up on certain peoples’ screens, reconciling the advertiser’s targeting preferences with the flow of people through Facebook’s apps and webpages in a given period of time. Advertisers can see which audiences ended up viewing the ad, but are never permitted to know the underlying logic of how those precise audiences were selected. The new research focuses on the second step of advertising on Facebook, the process of ad /delivery/, rather than on ad /targeting. /Essentially, the researchers created ads without any demographic target at all and watched where Facebook placed them. The results, said the researchers, were disturbing: Critically, we observe significant skew in delivery along gender and racial lines for “real” ads for employment and housing opportunities despite neutral targeting parameters. Our results demonstrate previously unknown mechanisms that can lead to potentially discriminatory ad delivery, even when advertisers set their targeting parameters to be highly inclusive. Rather than targeting a demographic niche, the researchers requested only that their ads reach Facebook users in the United States, leaving matters of ethnicity and gender entirely up to Facebook’s black box. As Facebook itself tells <https://www.facebook.com/business/news/relevance-score> potential advertisers, “We try to show people the ads that are most pertinent to them.” What exactly does the company’s ad-targeting black box, left to its own devices, consider pertinent? Are Facebook’s ad-serving algorithms as prone to bias like so many others <https://www.newscientist.com/article/2166207-discriminating-algorithms-5-ti…>? The answer will not surprise you. For one portion of the study, researchers ran ads for a wide variety of job listings in North Carolina, from janitors to nurses to lawyers, without any further demographic targeting options. With all other things being equal, the study found that “Facebook delivered our ads for jobs in the lumber industry to an audience that was 72% white and 90% men, supermarket cashier positions to an audience of 85% women, and jobs with taxi companies to a 75% black audience even though the target audience we specified was identical for all ads.” Ad displays for “artificial intelligence developer” listings also skewed white, while listings for secretarial work overwhelmingly found their way to female Facebook users. Although Facebook doesn’t permit advertisers to view the racial composition of an ad’s viewers, the researchers said they were able to confidently infer these numbers by cross-referencing the indicators Facebook does provide, particularly regions where users live, which in some states can be cross-referenced with race data held in voter registration records. In the case of housing ads — an area Facebook has already shown in the past has potential for discriminatory abuse <https://www.propublica.org/article/facebook-lets-advertisers-exclude-users-…> — the results were also heavily skewed along racial lines. “In our experiments,” the researchers wrote, “Facebook delivered our broadly targeted ads for houses for sale to audiences of 75% white users, when ads for rentals were shown to a more demographically balanced audience.” In other cases, the study found that “Facebook delivered some of our housing ads to audiences of over 85% white users while they delivered other ads to over 65% Black users (depending on the content of the ad) even though the ads were targeted identically.” Facebook appeared to algorithmically reinforce stereotypes even in the case of simple, rather boring stock photos, indicating that not only does Facebook automatically scan and classify images on the site as being more “relevant” to men or women, but changes who sees the ad based on whether it includes a picture of, say, a football or a flower. The research took a selection of stereotypically gendered images — a military scene and an MMA fight on the stereotypically male side, a rose as stereotypically female — and altered them so that they would be invisible to the human eye (marking the images as transparent “alpha” channels, in technical terms). They then used these invisible pictures in ads run without any gender-based targeting, yet found Facebook, presumably after analyzing the images with software, made retrograde, gender-based decisions on how to deliver them: Ads with stereotypical macho images were shown mostly to men, even though the men had no idea what they were looking at. The study concluded that “Facebook has an automated image classification mechanism in place that is used to steer different ads towards different subsets of the user population.” In other words, the bias was on Facebook’s end, not in the eye of the beholder. The report comes at an inconvenient time for Facebook, now facing charges from the Department of Housing and Urban Development over its potential to enable advertisers to illegally exclude certain groups <https://www.cnn.com/2019/03/28/tech/facebook-hud-ad-discrimination/>. And although the study is careful to note that “our results only speak to how our particular ads are delivered (i.e., we cannot say how housing or employment ads in general are delivered),” it still concludes that “the significant skew we observe even on a small set of ads suggests that real-world housing and employment ads are likely to experience the same fate.” In other words, even in the absence of bigoted landlords, the advertising platform itself appears inherently prejudiced. A Facebook spokesperson provided the following comment: We stand against discrimination in any form. We’ve made important changes to our ad targeting tools and know that this is only a first step. We’ve been looking at our ad delivery system and have engaged industry leaders, academics, and civil rights experts on this very topic – and we’re exploring more changes. It’s a familiar refrain at this point, and one that will likely do little to reassure those who just want to know that they’ll be provided with the same opportunities as everyone else, even in the context of ubiquitous advertising. The old apologia for targeted advertising is generally that it’s a favor to the consumer, sparing them “irrelevant” ads and instead providing them with opportunities to browse goods and services that are “pertinent” to them. What this shallow reasoning misses is that decisions about pertinence can become self-reinforcing; it’s foolish at best to think that women are more interested in secretarial work because they keep clicking the secretary ads, rather than that they click secretarial ads because it’s all Facebook will show them.

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Exclusive: Google cancels AI ethics board in response to outcry - Vox
by J.C. DE MARTIN April 5, 2019

April 5, 2019

https://www.vox.com/future-perfect/2019/4/4/18295933/google-cancels-ai-ethi… (Sent from my wireless device; please excuse brevity and typos (if any))

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