social media

How technology disrupted the truth

Curated on July 13, 2016August 3, 2018 by Stefaan Verhulst

Katharine Viner in The Guardian: “Social media has swallowed the news – threatening the funding of public-interest reporting and ushering in an era when everyone has their own facts. But the consequences go far beyond journalism…

When a fact begins to resemble whatever you feel is true, it becomes very difficult for anyone to tell the difference between facts that are true and “facts” that are not. The leave campaign was well aware of this – and took full advantage, safe in the knowledge that the Advertising Standards Authority has no power to police political claims. A few days after the vote, Arron Banks, Ukip’s largest donor and the main funder of the Leave.EU campaign, told the Guardian that his side knew all along that facts would not win the day. “It was taking an American-style media approach,” said Banks. “What they said early on was ‘Facts don’t work’, and that’s it. The remain campaign featured fact, fact, fact, fact, fact. It just doesn’t work. You have got to connect with people emotionally. It’s the Trump success.”
It was little surprise that some people were shocked after the result to discover that Brexit might have serious consequences and few of the promised benefits. When “facts don’t work” and voters don’t trust the media, everyone believes in their own “truth” – and the results, as we have just seen, can be devastating.

How did we end up here? And how do we fix it?

Twenty-five years after the first website went online, it is clear that we are living through a period of dizzying transition. For 500 years after Gutenberg, the dominant form of information was the printed page: knowledge was primarily delivered in a fixed format, one that encouraged readers to believe in stable and settled truths.

Now, we are caught in a series of confusing battles between opposing forces: between truth and falsehood, fact and rumour, kindness and cruelty; between the few and the many, the connected and the alienated; between the open platform of the web as its architects envisioned it and the gated enclosures of Facebook and other social networks; between an informed public and a misguided mob.

What is common to these struggles – and what makes their resolution an urgent matter – is that they all involve the diminishing status of truth. This does not mean that there are no truths. It simply means, as this year has made very clear, that we cannot agree on what those truths are, and when there is no consensus about the truth and no way to achieve it, chaos soon follows.

Increasingly, what counts as a fact is merely a view that someone feels to be true – and technology has made it very easy for these “facts” to circulate with a speed and reach that was unimaginable in the Gutenberg era (or even a decade ago). A dubious story about Cameron and a pig appears in a tabloid one morning, and by noon, it has flown around the world on social media and turned up in trusted news sources everywhere. This may seem like a small matter, but its consequences are enormous.

In the digital age, it is easier than ever to publish false information, which is quickly shared and taken to be true. “The Truth”, as Peter Chippindale and Chris Horrie wrote in Stick It Up Your Punter!, their history of the Sun newspaper, is a “bald statement which every newspaper prints at its peril”. There are usually several conflicting truths on any given subject, but in the era of the printing press, words on a page nailed things down, whether they turned out to be true or not. The information felt like the truth, at least until the next day brought another update or a correction, and we all shared a common set of facts.

This settled “truth” was usually handed down from above: an established truth, often fixed in place by an establishment. This arrangement was not without flaws: too much of the press often exhibited a bias towards the status quo and a deference to authority, and it was prohibitively difficult for ordinary people to challenge the power of the press. Now, people distrust much of what is presented as fact – particularly if the facts in question are uncomfortable, or out of sync with their own views – and while some of that distrust is misplaced, some of it is not.

In the digital age, it is easier than ever to publish false information, which is quickly shared and taken to be true – as we often see in emergency situations, when news is breaking in real time. To pick one example among many, during the November 2015 Paris terror attacks, rumours quickly spread on social media that the Louvre and Pompidou Centre had been hit, and that François Hollande had suffered a stroke. Trusted news organisations are needed to debunk such tall tales.

Sometimes rumours like these spread out of panic, sometimes out of malice, and sometimes deliberate manipulation, in which a corporation or regime pays people to convey their message. Whatever the motive, falsehoods and facts now spread the same way, through what academics call an “information cascade”. As the legal scholar and online-harassment expert Danielle Citron describes it, “people forward on what others think, even if the information is false, misleading or incomplete, because they think they have learned something valuable.” This cycle repeats itself, and before you know it, the cascade has unstoppable momentum. You share a friend’s post on Facebook, perhaps to show kinship or agreement or that you’re “in the know”, and thus you increase the visibility of their pot to others.
Algorithms such as the one that powers Facebook’s news feed are designed to give us more of what they think we want – which means that the version of the world we encounter every day in our own personal stream has been invisibly curated to reinforce our pre-existing beliefs. When Eli Pariser, the co-founder of Upworthy, coined the term “filter bubble” in 2011, he was talking about how the personalised web – and in particular Google’s personalised search function, which means that no two people’s Google searches are the same – means that we are less likely to be exposed to information that challenges us or broadens our worldview, and less likely to encounter facts that disprove false information that others have shared.

Pariser’s plea, at the time, was that those running social media platforms should ensure that “their algorithms prioritise countervailing views and news that’s important, not just the stuff that’s most popular or most self-validating”. But in less than five years, thanks to the incredible power of a few social platforms, the filter bubble that Pariser described has become much more extreme.

On the day after the EU referendum, in a Facebook post, the British internet activist and mySociety founder, Tom Steinberg, provided a vivid illustration of the power of the filter bubble – and the serious civic consequences for a world where information flows largely through social networks:

I am actively searching through Facebook for people celebrating the Brexit leave victory, but the filter bubble is SO strong, and extends SO far into things like Facebook’s custom search that I can’t find anyone who is happy *despite the fact that over half the country is clearly jubilant today* and despite the fact that I’m *actively* looking to hear what they are saying.

This echo-chamber problem is now SO severe and SO chronic that I can only beg any friends I have who actually work for Facebook and other major social media and technology to urgently tell their leaders that to not act on this problem now is tantamount to actively supporting and funding the tearing apart of the fabric of our societies … We’re getting countries where one half just doesn’t know anything at all about the other.

But asking technology companies to “do something” about the filter bubble presumes that this is a problem that can be easily fixed – rather than one baked into the very idea of social networks that are designed to give you what you and your friends want to see….(More)”

There aren’t any rules on how social scientists use private data. Here’s why we need them.

Curated on July 13, 2016August 3, 2018 by Stefaan Verhulst

Henry Farrell at SSRC: “The politics of social science access to data are shifting rapidly in the United States as in other developed countries. It used to be that states were the most important source of data on their citizens, economy, and society. States needed to collect and aggregate large amounts of information for their own purposes. They gathered this directly—e.g., through censuses of individuals and firms—and also constructed relevant indicators. Sometimes state agencies helped to fund social science projects in data gathering, such as the National Science Foundation’s funding of the American National Election Survey over decades. While scholars such as James Scott and John Brewer disagreed about the benefits of state data gathering, they recognized the state’s primary role.

In this world, the politics of access to data were often the politics of engaging with the state. Sometimes the state was reluctant to provide information, either for ethical reasons (e.g. the privacy of its citizens) or self-interest. However, democratic states did typically provide access to standard statistical series and the like, and where they did not, scholars could bring pressure to bear on them. This led to well-understood rules about the common availability of standard data for many research questions and built the foundations for standard academic practices. It was relatively easy for scholars to criticize each other’s work when they were drawing on common sources. This had costs—scholars tended to ask the kinds of questions that readily available data allowed them to ask—but also significant benefits. In particular, it made research more easily reproducible.

We are now moving to a very different world. On the one hand, open data initiatives in government are making more data available than in the past (albeit often without much in the way of background resources or documentation).The new universe of private data is reshaping social science research in some ways that are still poorly understood. On the other, for many research purposes, large firms such as Google or Facebook (or even Apple) have much better data than the government. The new universe of private data is reshaping social science research in some ways that are still poorly understood. Here are some of the issues that we need to think about:…(More)”

What is Artificial Intelligence?

Curated on July 12, 2016August 3, 2018 by Stefaan Verhulst

Report by Mike Loukides and Ben Lorica: “Defining artificial intelligence isn’t just difficult; it’s impossible, not the least because we don’t really understand human intelligence. Paradoxically, advances in AI will help more to define what human intelligence isn’t than what artificial intelligence is.

But whatever AI is, we’ve clearly made a lot of progress in the past few years, in areas ranging from computer vision to game playing. AI is making the transition from a research topic to the early stages of enterprise adoption. Companies such as Google and Facebook have placed huge bets on AI and are already using it in their products. But Google and Facebook are only the beginning: over the next decade, we’ll see AI steadily creep into one product after another. We’ll be communicating with bots, rather than scripted robo-dialers, and not realizing that they aren’t human. We’ll be relying on cars to plan routes and respond to road hazards. It’s a good bet that in the next decades, some features of AI will be incorporated into every application that we touch and that we won’t be able to do anything without touching an application.

Given that our future will inevitably be tied up with AI, it’s imperative that we ask: Where are we now? What is the state of AI? And where are we heading?

Capabilities and Limitations Today

Descriptions of AI span several axes: strength (how intelligent is it?), breadth (does it solve a narrowly defined problem, or is it general?), training (how does it learn?), capabilities (what kinds of problems are we asking it to solve?), and autonomy (are AIs assistive technologies, or do they act on their own?). Each of these axes is a spectrum, and each point in this many-dimensional space represents a different way of understanding the goals and capabilities of an AI system.

On the strength axis, it’s very easy to look at the results of the last 20 years and realize that we’ve made some extremely powerful programs. Deep Blue beat Garry Kasparov in chess; Watson beat the best Jeopardy champions of all time; AlphaGo beat Lee Sedol, arguably the world’s best Go player. But all of these successes are limited. Deep Blue, Watson, and AlphaGo were all highly specialized, single-purpose machines that did one thing extremely well. Deep Blue and Watson can’t play Go, and AlphaGo can’t play chess or Jeopardy, even on a basic level. Their intelligence is very narrow, and can’t be generalized. A lot of work has gone into usingWatson for applications such as medical diagnosis, but it’s still fundamentally a question-and-answer machine that must be tuned for a specific domain. Deep Blue has a lot of specialized knowledge about chess strategy and an encyclopedic knowledge of openings. AlphaGo was built with a more general architecture, but a lot of hand-crafted knowledge still made its way into the code. I don’t mean to trivialize or undervalue their accomplishments, but it’s important to realize what they haven’t done.

We haven’t yet created an artificial general intelligence that can solve a multiplicity of different kinds of problems. We still don’t have a machine that can listen to recordings of humans for a year or two, and start speaking. While AlphaGo “learned” to play Go by analyzing thousands of games, and then playing thousands more against itself, the same software couldn’t be used to master chess. The same general approach? Probably. But our best current efforts are far from a general intelligence that is flexible enough to learn without supervision, or flexible enough to choose what it wants to learn, whether that’s playing board games or designing PC boards.

Toward General Intelligence

How do we get from narrow, domain-specific intelligence to more general intelligence? By “general intelligence,” we don’t necessarily mean human intelligence; but we do want machines that can solve different kinds of problems without being programmed with domain-specific knowledge. We want machines that can make human judgments and decisions. That doesn’t necessarily mean that AI systems will implement concepts like creativity, intuition, or instinct, which may have no digital analogs. A general intelligence would have the ability to follow multiple pursuits and to adapt to unexpected situations. And a general AI would undoubtedly implement concepts like “justice” and “fairness”: we’re already talking about the impact of AI on the legal system….

It’s easier to think of super-intelligence as a matter of scale. If we can create “general intelligence,” it’s easy to assume that it could quickly become thousands of times more powerful than human intelligence. Or, more precisely: either general intelligence will be significantly slower than human thought, and it will be difficult to speed it up either through hardware or software; or it will speed up quickly, through massive parallelism and hardware improvements. We’ll go from thousand-core GPUs to trillions of cores on thousands of chips, with data streaming in from billions of sensors. In the first case, when speedups are slow, general intelligence might not be all that interesting (though it will have been a great ride for the researchers). In the second case, the ramp-up will be very steep and very fast….(More) (Full Report)”

Solving All the Wrong Problems

Curated on July 10, 2016May 29, 2019 by Stefaan Verhulst

Allison Arieff in the New York Times: “Every day, innovative companies promise to make the world a better place. Are they succeeding? Here is just a sampling of the products, apps and services that have come across my radar in the last few weeks:

A service that sends someone to fill your car with gas.

A service that sends a valet on a scooter to you, wherever you are, to park your car.

A service that will film anything you desire with a drone….

We are overloaded daily with new discoveries, patents and inventions all promising a better life, but that better life has not been forthcoming for most. In fact, the bulk of the above list targets a very specific (and tiny!) slice of the population. As one colleague in tech explained it to me recently, for most people working on such projects, the goal is basically to provide for themselves everything that their mothers no longer do….When everything is characterized as “world-changing,” is anything?

Clay Tarver, a writer and producer for the painfully on-point HBO comedy “Silicon Valley,” said in a recent New Yorker article: “I’ve been told that, at some of the big companies, the P.R. departments have ordered their employees to stop saying ‘We’re making the world a better place,’ specifically because we have made fun of that phrase so mercilessly. So I guess, at the very least, we’re making the world a better place by making these people stop saying they’re making the world a better place.”

O.K., that’s a start. But the impulse to conflate toothbrush delivery with Nobel Prize-worthy good works is not just a bit cultish, it’s currently a wildfire burning through the so-called innovation sector. Products and services are designed to “disrupt” market sectors (a.k.a. bringing to market things no one really needs) more than to solve actual problems, especially those problems experienced by what the writer C. Z. Nnaemeka has described as “the unexotic underclass” — single mothers, the white rural poor, veterans, out-of-work Americans over 50 — who, she explains, have the “misfortune of being insufficiently interesting.”

If the most fundamental definition of design is to solve problems, why are so many people devoting so much energy to solving problems that don’t really exist? How can we get more people to look beyond their own lived experience?

In “Design: The Invention of Desire,” a thoughtful and necessary new book by the designer and theorist Jessica Helfand, the author brings to light an amazing kernel: “hack,” a term so beloved in Silicon Valley that it’s painted on the courtyard of the Facebook campus and is visible from planes flying overhead, is also prison slang for “horse’s ass carrying keys.”

To “hack” is to cut, to gash, to break. It proceeds from the belief that nothing is worth saving, that everything needs fixing. But is that really the case? Are we fixing the right things? Are we breaking the wrong ones? Is it necessary to start from scratch every time?…

Ms. Helfand calls for a deeper embrace of personal vigilance: “Design may provide the map,” she writes, “but the moral compass that guides our personal choices resides permanently within us all.”

Can we reset that moral compass? Maybe we can start by not being a bunch of hacks….(More)”

Bridging data gaps for policymaking: crowdsourcing and big data for development

Curated on July 8, 2016August 3, 2018 by Stefaan Verhulst

Anthony Swan for the DevPolicyBlog: “…By far the biggest innovation in data collection is the ability to access and analyse (in a meaningful way) user-generated data. This is data that is generated from forums, blogs, and social networking sites, where users purposefully contribute information and content in a public way, but also from everyday activities that inadvertently or passively provide data to those that are able to collect it.

User-generated data can help identify user views and behaviour to inform policy in a timely way rather than just relying on traditional data collection techniques (census, household surveys, stakeholder forums, focus groups, etc.), which are often cumbersome, very costly, untimely, and in many cases require some form of approval or support by government.

It might seem at first that user-generated data has limited usefulness in a development context due to the importance of the internet in generating this data combined with limited internet availability in many places. However, U-Report is one example of being able to access user-generated data independent of the internet.

U-Report was initiated by UNICEF Uganda in 2011 and is a free SMS based platform where Ugandans are able to register as “U-Reporters” and on a weekly basis give their views on topical issues (mostly related to health, education, and access to social services) or participate in opinion polls. As an example, Figure 1 shows the result from a U-Report poll on whether polio vaccinators came to U-Reporter houses to immunise all children under 5 in Uganda, broken down by districts. Presently, there are more than 300,000 U-Reporters in Uganda and more than one million U-Reporters across 24 countries that now have U-Report. As an indication of its potential impact on policymaking,UNICEF claims that every Member of Parliament in Uganda is signed up to receive U-Report statistics.

Figure 1: U-Report Uganda poll results

U-Report and other platforms such as Ushahidi (which supports, for example, I PAID A BRIBE, Watertracker, election monitoring, and crowdmapping) facilitate crowdsourcing of data where users contribute data for a specific purpose. In contrast, “big data” is a broader concept because the purpose of using the data is generally independent of the reasons why the data was generated in the first place.

Big data for development is a new phrase that we will probably hear a lot more (see here [pdf] and here). The United Nations Global Pulse, for example, supports a number of innovation labs which work on projects that aim to discover new ways in which data can help better decision-making. Many forms of “big data” are unstructured (free-form and text-based rather than table- or spreadsheet-based) and so a number of analytical techniques are required to make sense of the data before it can be used.

Measures of Twitter activity, for example, can be a real-time indicator of food price crises in Indonesia [pdf] (see Figure 2 below which shows the relationship between food-related tweet volume and food inflation: note that the large volume of tweets in the grey highlighted area is associated with policy debate on cutting the fuel subsidy rate) or provide a better understanding of the drivers of immunisation awareness. In these examples, researchers “text-mine” Twitter feeds by extracting tweets related to topics of interest and categorising text based on measures of sentiment (positive, negative, anger, joy, confusion, etc.) to better understand opinions and how they relate to the topic of interest. For example, Figure 3 shows the sentiment of tweets related to vaccination in Kenya over time and the dates of important vaccination related events.

Figure 2: Plot of monthly food-related tweet volume and official food price statistics

Figure 3: Sentiment of vaccine related tweets in Kenya

Another big data example is the use of mobile phone usage to monitor the movement of populations in Senegal in 2013. The data can help to identify changes in the mobility patterns of vulnerable population groups and thereby provide an early warning system to inform humanitarian response effort.

The development of mobile banking too offers the potential for the generation of a staggering amount of data relevant for development research and informing policy decisions. However, it also highlights the public good nature of data collected by public and private sector institutions and the reliance that researchers have on them to access the data. Building trust and a reputation for being able to manage privacy and commercial issues will be a major challenge for researchers in this regard….(More)”

Why we no longer trust the experts

Curated on July 4, 2016August 3, 2018 by Stefaan Verhulst

Gillian Tett in the Financial Times: “Last week, I decided to take a gaggle of kids for an end-of-school-year lunch in a New York neighbourhood that I did not know well. I duly began looking for a suitable restaurant. A decade ago, I would have done that by turning to a restaurant guide. In the world I grew up in, it was normal to seek advice from the “experts”.

But in Manhattan last week, it did not occur to me to consult Fodor’s. Instead, I typed what I needed into my cellphone, scrolled through a long list of online restaurant recommendations, including comments from people who had eaten in them — and picked one.

Yes, it was a leap of faith; those restaurant reviews might have been fake. But there were enough voices for me to feel able to trust the wisdom of the cyber crowds — and, as it happened, our lunch choice was very good.

This is a trivial example of a much bigger change that is under way, and one that has some thought-provoking implications in the wake of the Brexit vote. Before the referendum, British citizens were subjected to a blitz of advice about the potential costs of Brexit from “experts”: economists, central bankers, the International Monetary Fund and world leaders, among others. Indeed, the central strategy of the government (and other “Remainers”) appeared to revolve around wheeling out these experts, with their solemn speeches and statistics….

I suspect that it indicates something else: that citizens of the cyber world no longer have much faith in anything that experts say, not just in the political sphere but in numerous others too. At a time when we increasingly rely on crowd-sourced advice rather than official experts to choose a restaurant, healthcare and holidays, it seems strange to expect voters to listen to official experts when it comes to politics.

In our everyday lives, we are moving from a system based around vertical axes of trust, where we trust people who seem to have more authority than we do, to one predicated on horizontal axes of trust: we take advice from our peer group.

You can see this clearly if you look at the surveys conducted by groups such as the Pew Research Center. These show that faith in institutions such as the government, big business and the media has crumbled in recent years; indeed, almost the only institution in the US that has bucked the trend is the military.

What is even more interesting to look at, however, are the areas where trust remains high. In an annual survey conducted by the Edelman public relations firm, people in 20 countries are asked who they trust. They show rising confidence in the “a person like me” category, and surprisingly high trust in digital technology. We live in a world where we increasingly trust our Facebook friends and the Twitter crowd more than we do the IMF or the prime minister.

In some senses, this is good news. Relying on horizontal axes of trust should mean more democracy and empowerment for ordinary citizens. But the problem of this new world is that people can fall prey to social fads and tribalism — or groupthink…..

Either way, nobody is going to put this genie back into the bottle. So we all need to think about what creates the bonds of “trust” in today’s world. And recognise that the 20th-century model of politics, with its reverence for experts and fixed parties, may eventually seem as outdated as restaurant guides. We live in volatile time…(More)”

What Can Civic Tech Learn From Social Movements?

Curated on June 16, 2016August 3, 2018 by Stefaan Verhulst

Stacy Donohue at Omidyar Network: “…In order to spur creative thinking about how the civic tech sector could be accelerated and expanded, we looked to Purpose, a public benefit corporation that works with NGOs, philanthropies, and brands on movement building strategies. We wanted to explore what we might learn from taking the work that Purpose has done mapping the progress of of 21^st century social movements and applying its methodology to civic tech.

So why consider viewing civic tech using the lens of 21^st century movements? Movements are engines of change in society that enable citizens to create new and better paths to engage with government and to seek recourse on issues that matter to millions of people. At first glance, civic tech doesn’t appear to be a movement in the purest sense of the term, but on closer inspection, it does share some fundamental characteristics. Like a movement, civic tech is mission driven, is focused on making change that benefits the public, and in most cases enables better public input into decision making.

We believe that better understanding the essential components of movements, and observing the ways in which civic tech does or does not behave like one, can yield insights on how we as a civic tech community can collectively drive the sector forward….

report Engines of Change: What Civic Tech Can Learn From Social Movements….provides a lot of rich insight and detail which we invite everyone to explore. Meanwhile, we have summarized five key findings:

Grassroots activity is expanding across the US – Activity is no longer centralized around San Francisco and New York; it’s rapidly growing and spreading across the US – in fact, there was an 81% increase in the number of cities hosting civic tech MeetUps from 2013 to 2015, and 45 of 50 states had at least one MeetUp on civic tech in 2015.
Talk is turning to action – We are walking the talk. One way we can see this is that growth in civic tech Twitter discussion is highly correlated with the growth in GitHub contributions to civic tech projects and related Meetup events. Between 2013-2015, over 8,500 people contributed code to GitHub civic tech projects and there were over 76,000 MeetUps for civic tech events.
There is an engaged core, but it is very small in number – As with most social movements, civic tech has a definite core of highly engaged evangelists, advocates and entrepreneurs that are driving conversations, activity, and events and this is growing. The number of Meetup groups holding multiple events a quarter grew by 136% between 2013 to 2015. And likewise there was a 60% growth in Engaged Tweeters in during this time period. However, this level of activity is dwarfed by other movements such as climate action.
Civic tech is growing but still lacking scale – There are many positive indications of growth in civic tech; for example, the combination of nonprofit and for-profit funding to the sector increased by almost 120% over the period. But while growth compares favorably to other movements, again the scale just isn’t there.
Common themes, but no shared vision or identity – Purpose examined the extent to which civic tech exhibits and articulates a shared vision or identity around which members of a movement can rally. What they found is that many fewer people are discussing the same shared set of themes. Two themes – Open Data and Government Transparency – are resonating and gaining traction across the sector and could therefore form the basis of common identity for civic tech.

While each of these insights is important in its own right and requires action to move the sector forward, the main thing that strikes us is the need for a coherent and clearly articulated vision and sense of shared identity for civic tech…

Read the full report: Engines of Change: What Civic Tech Can Learn From Social Movements

Explore the data tool here….(More)”