social media

Twitter, UN Global Pulse announce data partnership

Curated on September 27, 2016July 19, 2019 by Stefaan Verhulst

PressRelease: “Twitter and UN Global Pulse today announced a partnership that will provide the United Nations with access to Twitter’s data tools to support efforts to achieve the Sustainable Development Goals, which were adopted by world leaders last year.

Every day, people around the world send hundreds of millions of Tweets in dozens of languages. This public data contains real-time information on many issues including the cost of food, availability of jobs, access to health care, quality of education, and reports of natural disasters. This partnership will allow the development and humanitarian agencies of the UN to turn these social conversations into actionable information to aid communities around the globe.

“The Sustainable Development Goals are first and foremost about people, and Twitter’s unique data stream can help us truly take a real-time pulse on priorities and concerns — particularly in regions where social media use is common — to strengthen decision-making. Strong public-private partnerships like this show the vast potential of big data to serve the public good,” said Robert Kirkpatrick, Director of UN Global Pulse.

“We are incredibly proud to partner with the UN in support of the Sustainable Development Goals,” said Chris Moody, Twitter’s VP of Data Services. “Twitter data provides a live window into the public conversations that communities around the world are having, and we believe that the increased potential for research and innovation through this partnership will further the UN’s efforts to reach the Sustainable Development Goals.”

Organizations and business around the world currently use Twitter data in many meaningful ways, and this unique data source enables them to leverage public information at scale to better inform their policies and decisions. These partnerships enable innovative uses of Twitter data, while protecting the privacy and safety of Twitter users.

UN Global Pulse’s new collaboration with Twitter builds on existing R&D that has shown the power of social media for social impact, like measuring the impact of public health campaigns, tracking reports of rising food prices, or prioritizing needs after natural disasters….(More)”

Living in the World of Both/And

Curated on September 21, 2016August 3, 2018 by Stefaan Verhulst

Essay by Adene Sacks & Heather McLeod Grant in SSIR: “In 2011, New York Times data scientist Jake Porway wrote a blog post lamenting the fact that most data scientists spend their days creating apps to help users find restaurants, TV shows, or parking spots, rather than addressing complicated social issues like helping identify which teens are at risk of suicide or creating a poverty index of Africa using satellite data.

That post hit a nerve. Data scientists around the world began clamoring for opportunities to “do good with data.” Porway—at the center of this storm—began to convene these scientists and connect them to nonprofits via hackathon-style events called DataDives, designed to solve big social and environmental problems. There was so much interest, he eventually quit his day job at the Times and created the organization DataKind to steward this growing global network of data science do-gooders.

At the same time, in the same city, another movement was taking shape—#GivingTuesday, an annual global giving event fueled by social media. In just five years, #GivingTuesday has reshaped how nonprofits think about fundraising and how donors give. And yet, many don’t know that 92nd Street Y (92Y)—a 140-year-old Jewish community and cultural center in Manhattan, better known for its star-studded speaker series, summer camps, and water aerobics classes—launched it.

What do these two examples have in common? One started as a loose global network that engaged data scientists in solving problems, and then became an organization to help support the larger movement. The other started with a legacy organization, based at a single site, and catalyzed a global movement that has reshaped how we think about philanthropy. In both cases, the founding groups have incorporated the best of both organizations and networks.

Much has been written about the virtues of thinking and acting collectively to solve seemingly intractable challenges. Nonprofit leaders are being implored to put mission above brand, build networks not just programs, and prioritize collaboration over individual interests. And yet, these strategies are often in direct contradiction to the conventional wisdom of organization-building: differentiating your brand, developing unique expertise, and growing a loyal donor base.

A similar tension is emerging among network and movement leaders. These leaders spend their days steering the messy process required to connect, align, and channel the collective efforts of diverse stakeholders. It’s not always easy: Those searching to sustain movements often cite the lost momentum of the Occupy movement as a cautionary note. Increasingly, network leaders are looking at how to adapt the process, structure, and operational expertise more traditionally associated with organizations to their needs—but without co-opting or diminishing the energy and momentum of their self-organizing networks…

Welcome to the World of “Both/And”

Today’s social change leaders—be they from business, government, or nonprofits—must learn to straddle the leadership mindsets and practices of both networks and organizations, and know when to use which approach. Leaders like Porway, and Henry Timms and Asha Curran of 92Y can help show us the way.

How do these leaders work with the “both/and” mindset?

First, they understand and leverage the strengths of both organizations and networks—and anticipate their limitations. As Timms describes it, leaders need to be “bilingual” and embrace what he has called “new power.” Networks can be powerful generators of new talent or innovation around complex multi-sector challenges. It’s useful to take a network approach when innovating new ideas, mobilizing and engaging others in the work, or wanting to expand reach and scale quickly. However, networks can dissipate easily without specific “handrails,” or some structure to guide and support their work. This is where they need some help from the organizational mindset and approach.

On the flip side, organizations are good at creating centralized structures to deliver products or services, manage risk, oversee quality control, and coordinate concrete functions like communications or fundraising. However, often that efficiency and effectiveness can calcify over time, becoming a barrier to new ideas and growth opportunities. When organizational boundaries are too rigid, it is difficult to engage the outside world in ideating or mobilizing on an issue. This is when organizations need an infusion of the “network mindset.”

…(More)

Beware of the gaps in Big Data

Curated on September 21, 2016August 3, 2018 by Stefaan Verhulst

Edd Gent at E&T: “When the municipal authority in charge of Boston, Massachusetts, was looking for a smarter way to find which roads it needed to repair, it hit on the idea of crowdsourcing the data. The authority released a mobile app called Street Bump in 2011 that employed an elegantly simple idea: use a smartphone’s accelerometer to detect jolts as cars go over potholes and look up the location using the Global Positioning System. But the approach ran into a pothole of its own.The system reported a disproportionate number of potholes in wealthier neighbourhoods. It turned out it was oversampling the younger, more affluent citizens who were digitally clued up enough to download and use the app in the first place. The city reacted quickly, but the incident shows how easy it is to develop a system that can handle large quantities of data but which, through its own design, is still unlikely to have enough data to work as planned.

As we entrust more of our lives to big data analytics, automation problems like this could become increasingly common, with their errors difficult to spot after the fact. Systems that ‘feel like they work’ are where the trouble starts.

Harvard University professor Gary King, who is also founder of social media analytics company Crimson Hexagon, recalls a project that used social media to predict unemployment. The model was built by correlating US unemployment figures with the frequency that people used words like ‘jobs’, ‘unemployment’ and ‘classifieds’. A sudden spike convinced researchers they had predicted a big rise in joblessness, but it turned out Steve Jobs had died and their model was simply picking up posts with his name. “This was an example of really bad analytics and it’s even worse because it’s the kind of thing that feels like it should work and does work a little bit,” says King.

Big data can shed light on areas with historic information deficits, and systems that seem to automatically highlight the best course of action can be seductive for executives and officials. “In the vacuum of no decision any decision is attractive,” says Jim Adler, head of data at Toyota Research Institute in Palo Alto. “Policymakers will say, ‘there’s a decision here let’s take it’, without really looking at what led to it. Was the data trustworthy, clean?”…(More)”

Artificial intelligence is hard to see

Curated on September 13, 2016August 20, 2018 by Stefaan Verhulst

Kate Crawford and Meredith Whittaker on “Why we urgently need to measure AI’s societal impacts“: “How will artificial intelligence systems change the way we live? This is a tough question: on one hand, AI tools are producing compelling advances in complex tasks, with dramatic improvements in energy consumption, audio processing, and leukemia detection. There is extraordinary potential to do much more in the future. On the other hand, AI systems are already making problematic judgements that are producing significant social, cultural, and economic impacts in people’s everyday lives.

AI and decision-support systems are embedded in a wide array of social institutions, from influencing who is released from jail to shaping the news we see. For example, Facebook’s automated content editing system recently censored the Pulitzer-prize winning image of a nine-year old girl fleeing napalm bombs during the Vietnam War. The girl is naked; to an image processing algorithm, this might appear as a simple violation of the policy against child nudity. But to human eyes, Nick Ut’s photograph, “The Terror of War”, means much more: it is an iconic portrait of the indiscriminate horror of conflict, and it has an assured place in the history of photography and international politics. The removal of the image caused an international outcry before Facebook backed down and restored the image. “What they do by removing such images, no matter what good intentions, is to redact our shared history,” said the Prime Minister of Norway, Erna Solberg.

It’s easy to forget that these high-profile instances are actually the easy cases. As Tarleton Gillespie has observed, hundreds of content reviews are occurring with Facebook images thousand of times per day, and rarely is there a Pulitzer prize to help determine lasting significance. Some of these reviews include human teams, and some do not. In this case, there is alsoconsiderable ambiguity about where the automated process ended and the human review began: which is part of the problem. And Facebook is just one player in complex ecology of algorithmically-supplemented determinations with little external monitoring to see how decisions are made or what the effects might be.

The ‘Terror of War’ case, then, is the tip of the iceberg: a rare visible instance that points to a much larger mass of unseen automated and semi-automated decisions. The concern is that most of these ‘weak AI’ systems are making decisions that don’t garner such attention. They are embedded at the back-end of systems, working at the seams of multiple data sets, with no consumer-facing interface. Their operations are mainly unknown, unseen, and with impacts that take enormous effort to detect.

Sometimes AI techniques get it right, and sometimes they get it wrong. Only rarely will those errors be seen by the public: like the Vietnam war photograph, or when a AI ‘beauty contest’ held this month was called out for being racist for selecting white women as the winners. We can dismiss this latter case as a problem of training data — they simply need a more diverse selection of faces to train their algorithm with, and now that 600,000 people have sent in their selfies, they certainly have better means to do so. But while a beauty contest might seem like a bad joke, or just a really good trick to get people to give up their photos to build a large training data set, it points to a much bigger set of problems. AI and decision-support systems are reaching into everyday life: determining who will be on a predictive policing‘heat list’, who will be hired or promoted, which students will be recruited to universities, or seeking to predict at birth who will become a criminal by the age of 18. So the stakes are high…(More)”

Data for Policy: Data Science and Big Data in the Public Sector

Curated on September 8, 2016August 15, 2018 by Stefaan Verhulst

Innar Liiv at OXPOL: “How can big data and data science help policy-making? This question has recently gained increasing attention. Both the European Commission and the White House have endorsed the use of data for evidence-based policy making.

Still, a gap remains between theory and practice. In this blog post, I make a number of recommendations for systematic development paths.

RESEARCH TRENDS SHAPING DATA FOR POLICY

‘Data for policy’ as an academic field is still in its infancy. A typology of the field’s foci and research areas are summarised in the figure below.

Besides the ‘data for policy’ community, there are two important research trends shaping the field: 1) computational social science; and 2) the emergence of politicised social bots.

Computational social science (CSS) is an new interdisciplinary research trend in social science, which tries to transform advances in big data and data science into research methodologies for understanding, explaining and predicting underlying social phenomena.

Social science has a long tradition of using computational and agent-based modelling approaches (e.g.Schelling’s Model of Segregation), but the new challenge is to feed real-life, and sometimes even real-time information into those systems to get gain rapid insights into the validity of research hypotheses.

For example, one could use mobile phone call records to assess the acculturation processes of different communities. Such a project would involve translating different acculturation theories into computational models, researching the ethical and legal issues inherent in using mobile phone data and developing a vision for generating policy recommendations and new research hypothesis from the analysis.

Politicised social bots are also beginning to make their mark. In 2011, DARPA solicited research proposals dealing with social media in strategic communication. The term ‘political bot’ was not used, but the expected results left no doubt about the goals…

The next wave of e-government innovation will be about analytics and predictive models. Taking advantage of their potential for social impact will require a solid foundation of e-government infrastructure.

The most important questions going forward are as follows:

What are the relevant new data sources?
How can we use them?
What should we do with the information? Who cares? Which political decisions need faster information from novel sources? Do we need faster information? Does it come with unanticipated risks?

These questions barely scratch the surface, because the complex interplay between general advancements of computational social science and hovering satellite topics like political bots will have an enormous impact on research and using data for policy. But, it’s an important start….(More)”

Against transparency

Curated on September 6, 2016August 3, 2018 by Stefaan Verhulst

Matthew Yglesias at Vox: “…Digital storage is pretty cheap and easy, so maybe the next step in open government is ubiquitous surveillance of public servants paired with open access to the recordings.

As a journalist and an all-around curious person, I can’t deny there’s something appealing about this.

Historians, too, would surely love to know everything that President Obama and his top aides said to one another regarding budget negotiations with John Boehner rather than needing to rely on secondhand news accounts influenced by the inevitable demands of spin. By the same token, historians surely would wish that there were a complete and accurate record of what was said at the Constitutional Convention in 1787 that, instead, famously operated under a policy of anonymous discussions.

But we should be cautioned by James Madison’s opinion that “no Constitution would ever have been adopted by the convention if the debates had been public.”

His view, which seems sensible, is that public or recorded debates would have been simply exercises in position-taking rather than deliberation, with each delegate playing to his base back home rather than working toward a deal.

“Had the members committed themselves publicly at first, they would have afterwards supposed consistency required them to maintain their ground,” Madison wrote, “whereas by secret discussion no man felt himself obliged to retain his opinions any longer than he was satisfied of their propriety and truth, and was open to the force of argument.”

The example comes to me by way of Cass Sunstein, who formerly held a position as a top regulatory czar in Obama’s White House, and who delivered a fascinating talk on the subject of government transparency at a June 2016 Columbia symposium on the occasion of the anniversary of the Freedom of Information Act.

Sunstein asks us to distinguish between disclosure of the government’s outputs and disclosure of the government’s inputs. Output disclosure is something like the text of the Constitution or when the Obama administration had Medicare change decades of practice and begin publishing information about what Medicare pays to hospitals and other health providers.

Input disclosure would be something like the transcript of the debates at the Constitutional Convention or a detailed record of the arguments inside the Obama administration over whether to release the Medicare data. Sunstein’s argument is that it is a mistake to simply conflate the two ideas of disclosure under one broad heading of “transparency” when considerations around the two are very different.

Public officials need to have frank discussions

The fundamental problem with input disclosure is that in addition to serving as a deterrent to misconduct, it serves as a deterrent to frankness and honesty.

There are a lot of things that colleagues might have good reason to say to one another in private that would nonetheless be very damaging if they went viral on Facebook:

Healthy brainstorming processes often involve tossing out bad or half-baked ideas in order to stimulate thought and elevate better ones.
A realistic survey of options may require a blunt assessment of the strengths and weaknesses of different members of the team or of outside groups that would be insulting if publicized.
Policy decisions need to be made with political sustainability in mind, but part of making a politically sustainable policy decision is you don’t come out and say you made the decision with politics in mind.
Someone may want to describe an actual or potential problem in vivid terms to spur action, without wanting to provoke public panic or hysteria through public discussion.
If a previously embarked-upon course of action isn’t working, you may want to quietly change course rather than publicly admit failure.

Journalists are, of course, interested in learning about all such matters. But it’s precisely because such things are genuinely interesting that making disclosure inevitable is risky.

Ex post facto disclosure of discussions whose participants didn’t realize they would be disclosed would be fascinating and useful. But after a round or two of disclosure, the atmosphere would change. Instead of peeking in on a real decision-making process, you would have every meeting dominated by the question “what will this look like on the home page of Politico?”…(More)”

How Tech Giants Are Devising Real Ethics for Artificial Intelligence

Curated on September 1, 2016August 3, 2018 by Stefaan Verhulst

John Markoff in the New York Times: “For years, science-fiction moviemakers have been making us fear the bad things that artificially intelligent machines might do to their human creators. But for the next decade or two, our biggest concern is more likely to be that robots will take away our jobs or bump into us on the highway.

Now five of the world’s largest tech companies are trying to create a standard of ethics around the creation of artificial intelligence. While science fiction has focused on the existential threat of A.I. to humans,researchers at Google’s parent company, Alphabet, and those from Amazon,Facebook, IBM and Microsoft have been meeting to discuss more tangible issues, such as the impact of A.I. on jobs, transportation and even warfare.

Tech companies have long overpromised what artificially intelligent machines can do. In recent years, however, the A.I. field has made rapid advances in a range of areas, from self-driving cars and machines that understand speech, like Amazon’s Echo device, to a new generation of weapons systems that threaten to automate combat.

The specifics of what the industry group will do or say — even its name —have yet to be hashed out. But the basic intention is clear: to ensure thatA.I. research is focused on benefiting people, not hurting them, according to four people involved in the creation of the industry partnership who are not authorized to speak about it publicly.

The importance of the industry effort is underscored in a report issued onThursday by a Stanford University group funded by Eric Horvitz, a Microsoft researcher who is one of the executives in the industry discussions. The Stanford project, called the One Hundred Year Study onArtificial Intelligence, lays out a plan to produce a detailed report on the impact of A.I. on society every five years for the next century….The Stanford report attempts to define the issues that citizens of a typicalNorth American city will face in computers and robotic systems that mimic human capabilities. The authors explore eight aspects of modern life,including health care, education, entertainment and employment, but specifically do not look at the issue of warfare..(More)”

Managing Federal Information as a Strategic Resource

Curated on August 29, 2016August 3, 2018 by Stefaan Verhulst

White House: “Today the Office of Management and Budget (OMB) is releasing an update to the Federal Government’s governing document for the management of Federal information resources: Circular A-130, Managing Information as a Strategic Resource.

The way we manage information technology(IT), security, data governance, and privacy has rapidly evolved since A-130 was last updated in 2000. In today’s digital world, we are creating and collecting large volumes of data to carry out the Federal Government’s various missions to serve the American people. This data is duplicated, stored, processed, analyzed, and transferred with ease. As government continues to digitize, we must ensure we manage data to not only keep it secure, but also allow us to harness this information to provide the best possible service to our citizens.

Today’s update to Circular A-130 gathers in one resource a wide range of policy updates for Federal agencies regarding cybersecurity, information governance, privacy, records management, open data, and acquisitions. It also establishes general policy for IT planning and budgeting through governance, acquisition, and management of Federal information, personnel, equipment, funds, IT resources, and supporting infrastructure and services. In particular, A-130 focuses on three key elements to help spur innovation throughout the government:

Real Time Knowledge of the Environment. In today’s rapidly changing environment, threats and technology are evolving at previously unimagined speeds. In such a setting, the Government cannot afford to authorize a system and not look at it again for years at a time. In order to keep pace, we must move away from periodic, compliance-driven assessment exercises and, instead, continuously assess our systems and build-in security and privacy with every update and re-design. Throughout the Circular, we make clear the shift away from check-list exercises and toward the ongoing monitoring, assessment, and evaluation of Federal information resources.
Proactive Risk Management. To keep pace with the needs of citizens, we must constantly innovate. As part of such efforts, however, the Federal Government must modernize the way it identifies, categorizes, and handles risk to ensure both privacy and security. Significant increases in the volume of data processed and utilized by Federal resources requires new ways of storing, transferring, and managing it Circular A-130 emphasizes the need for strong data governance that encourages agencies to proactively identify risks, determine practical and implementable solutions to address said risks, and implement and continually test the solutions. This repeated testing of agency solutions will help to proactively identify additional risks, starting the process anew.
Shared Responsibility. Citizens are connecting with each other in ways never before imagined. From social media to email, the connectivity we have with one another can lead to tremendous advances. The updated A-130 helps to ensure everyone remains responsible and accountable for assuring privacy and security of information – from managers to employees to citizens interacting with government services. …(More)”

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