Why Big Data Is a Big Deal for Cities

Curated on Posted on by Stefaan Verhulst

John M. Kamensky in Governing: “We hear a lot about “big data” and its potential value to government. But is it really fulfilling the high expectations that advocates have assigned to it? Is it really producing better public-sector decisions? It may be years before we have definitive answers to those questions, but new research suggests that it’s worth paying a lot of attention to.

University of Kansas Prof. Alfred Ho recently surveyed 65 mid-size and large cities to learn what is going on, on the front line, with the use of big data in making decisions. He found that big data has made it possible to “change the time span of a decision-making cycle by allowing real-time analysis of data to instantly inform decision-making.” This decision-making occurs in areas as diverse as program management, strategic planning, budgeting, performance reporting and citizen engagement.

Cities are natural repositories of big data that can be integrated and analyzed for policy- and program-management purposes. These repositories include data from public safety, education, health and social services, environment and energy, culture and recreation, and community and business development. They include both structured data, such as financial and tax transactions, and unstructured data, such as recorded sounds from gunshots and videos of pedestrian movement patterns. And they include data supplied by the public, such as the Boston residents who use a phone app to measure road quality and report problems.

These data repositories, Ho writes, are “fundamental building blocks,” but the challenge is to shift the ownership of data from separate departments to an integrated platform where the data can be shared.

There’s plenty of evidence that cities are moving in that direction and that they already are systematically using big data to make operational decisions. Among the 65 cities that Ho examined, he found that 49 have “some form of data analytics initiatives or projects” and that 30 have established “a multi-departmental team structure to do strategic planning for these data initiatives.”….The effective use of big data can lead to dialogs that cut across school-district, city, county, business and nonprofit-sector boundaries. But more importantly, it provides city leaders with the capacity to respond to citizens’ concerns more quickly and effectively….(More)”

Organizational crowdsourcing

Curated on Posted on by Stefaan Verhulst

Jeremy Morgan at Lippincott: “One of the most consequential insights from the study of organizational culture happens to have an almost irresistible grounding in basic common sense. When attempting to solve the challenges of today’s businesses, inviting a broad slice of an employee population yields more creative, actionable solutions than restricting the conversation to a small strategy or leadership team.

This recognition, that in order to uncover new business ideas and innovations, organizations must foster listening cultures and a meritocracy of best thinking, is fueling interest in organizational crowdsourcing — a discipline focused on employee connection, collaboration and ideation. Leaders at companies such as Roche, Bank of the West, Merck, Facebook and IBM, along with countless Silicon Valley companies for whom the “hackathon” is a major cultural event, have embraced employee crowdsourcing as a way to unlock organizational knowledge and promote empathy through technology.

The benefits of internal crowdsourcing are clear. First, it ensures that a company’s understanding of key change drivers and potential strategic priorities is grounded in the organization’s everyday reality and not abstract hypotheses developed by a team of strategists. Second, employees inherently believe in and want to own the implementation of ideas that they generate through crowdsourcing. These are ideas borne of the culture for the culture, and are less likely to run aground on the rocks of employee indifference….

How can this be achieved through organizational crowdsourcing?

There is no out-of-the-box solution. Each campaign has to organically surface areas of focus for further inquiries, develop a framework and set of questions to guide participation and ignite conversations, and then analyze and communicate results in a way that helps bring solutions to life. But there are some key principles that will maximize the success of any crowdsourcing effort.

Obtaining insightful and actionable answers boils down to asking the questions at just the right altitude. If they’re too high up, too broad and open-ended, the usefulness of the feedback will suffer. If the questions are too broad — “How can we make our workplace better?” — you will likely hear responses like “juice bars” and “massage therapists.” If the questions are too narrow — “What kind of lighting do we need in our conference rooms?” — you limit the opportunity of people to use their creativity. However, the answers are likely to spark a conversation if people are asked, “How can we create spaces that allow us to generate ideas more effectively?” Conversation will flow to discussion of breaking down physical barriers in office design, building social “hubs” and investing in live events that allow employees from disparate geographies to meet in person and solve problems together.

On the technology side, crowdsourcing platforms such as Jive Software and UserVoice, among others, make it easy to bring large numbers of employees together to gather, build upon and prioritize new ideas and innovation efforts, from process simplification and product development to the transformation of customer experiences. Respondents can vote on other people’s suggestions and add comments.

By facilitating targeted conversations across times zones, geographies and corporate functions, crowdsourcing makes possible a new way of listening: of harnessing an organization’s collective wisdom to achieve action by a united and inspired employee population. It’s amazing to see the thoughtfulness, precision and energy unleashed by crowdsourcing efforts. People genuinely want to contribute to their company’s success if you open the doors and let them.

Taking a page from the Silicon Valley hackathon, organizational crowdsourcing campaigns are structured as events of limited duration focused on a specific challenge or business problem….(More)”

Dumpster diving made easier with food donation points

Curated on Posted on by Stefaan Verhulst

Springwise: “With food waste a substantial contributor to both environmental and social problems, communities around the world are trying to find ways to make better use of leftovers as well as reduce the overall production of unused foodstuffs. One of the biggest challenges in getting leftovers to the people who need them is the logistics of finding and connecting the relevant groups and transporting the food. Several on-demand apps, like this one that matches homeless shelters with companies that have leftover food, are taking the guesswork out of what to do with available food. And retailers are getting smarter, like this one in the United States, now selling produce that would previously have been rejected for aesthetic reasons only.

In Brazil, the Makers Society collective designed a campaign called Prato de Rua (Street Dish) to help link people in possession of edible leftovers with community members in need. The campaign centers around a sticker that is affixed to the side of city dumpsters requesting that donated food be left at the specific points. By providing a more organized approach to getting rid of leftover food, the collective hopes to help people think more carefully about what they are getting rid of and why. At the same time, the initiative helps people who would otherwise be forced to go through the contents of a dumpster for edible remains, access good food more safely and swiftly.

The campaign sticker is available for download for communities globally to take on and adapt the idea….(More)”

Unconscious gender bias in the Google algorithm

Curated on Posted on by Stefaan Verhulst

Interview in Metode with Londa Schiebinger, director of Gendered Innovations: “We were interested, because the methods of sex and gender analysis are not in the university curriculum, yet it is very important. The first thing our group did was to develop those methods and we present twelve methods on the website. We knew it would be very important to create case studies or concrete examples where sex and gender analysis added something new to the research. One of my favorite examples is machine translation. If you look at Google Translate, which is the main one in the United States – SYSTRAN is the main one in Europe – we found that it defaults the masculine pronoun. So does SYSTRAN. If I put an article about myself into Google Translate, it defaults to «he said» instead of «she said». So, in an article of one of my visits to Spain, it defaults to «he thinks, he says…» and, occasionally, «it wrote». We wondered why this happened and we found out, because Google Translate works on an algorithm, the problem is that «he said» appears on the web four times more than «she said», so the machine gets it right if it chooses «he said». Because the algorithm is just set up for that. But, anyway, we found that there was a huge change in English language from 1968 to the current time, and the proportion of «he said» and «she said» changed from 4-to-1 to 2-to-1. But, still, the translation does not take this into account. So we went to Google and we said «Hey, what is going on?» and they said «Oh, wow, we didn’t know, we had no idea!». So what we recognized is that there is an unconscious gender bias in the Google algorithm. They did not intend to do this at all, so now there are a lot of people who are trying to fix it….

How can you fix that?

Oh, well, this is the thing! …I think algorithms in general are a problem because if there is any kind of unconscious bias in the data, the algorithm just returns that to you. So even though Google has policies, company policies, to support gender equality, they had an unconscious bias in their product and they do not mean to. Now that they know about it, they can try to fix it….(More)”

Big data may be reinforcing racial bias in the criminal justice system

Curated on Posted on by Stefaan Verhulst

Laurel Eckhouse at the Washington Post: “Big data has expanded to the criminal justice system. In Los Angeles, police use computerized “predictive policing” to anticipate crimes and allocate officers. In Fort Lauderdale, Fla., machine-learning algorithms are used to set bond amounts. In states across the country, data-driven estimates of the risk of recidivism are being used to set jail sentences.

Advocates say these data-driven tools remove human bias from the system, making it more fair as well as more effective. But even as they have become widespread, we have little information about exactly how they work. Few of the organizations producing them have released the data and algorithms they use to determine risk.

 We need to know more, because it’s clear that such systems face a fundamental problem: The data they rely on are collected by a criminal justice system in which race makes a big difference in the probability of arrest — even for people who behave identically. Inputs derived from biased policing will inevitably make black and Latino defendants look riskier than white defendants to a computer. As a result, data-driven decision-making risks exacerbating, rather than eliminating, racial bias in criminal justice.
Consider a judge tasked with making a decision about bail for two defendants, one black and one white. Our two defendants have behaved in exactly the same way prior to their arrest: They used drugs in the same amount, have committed the same traffic offenses, owned similar homes and took their two children to the same school every morning. But the criminal justice algorithms do not rely on all of a defendant’s prior actions to reach a bail assessment — just those actions for which he or she has been previously arrested and convicted. Because of racial biases in arrest and conviction rates, the black defendant is more likely to have a prior conviction than the white one, despite identical conduct. A risk assessment relying on racially compromised criminal-history data will unfairly rate the black defendant as riskier than the white defendant.

To make matters worse, risk-assessment tools typically evaluate their success in predicting a defendant’s dangerousness on rearrests — not on defendants’ overall behavior after release. If our two defendants return to the same neighborhood and continue their identical lives, the black defendant is more likely to be arrested. Thus, the tool will falsely appear to predict dangerousness effectively, because the entire process is circular: Racial disparities in arrests bias both the predictions and the justification for those predictions.

We know that a black person and a white person are not equally likely to be stopped by police: Evidence on New York’s stop-and-frisk policy, investigatory stops, vehicle searches and drug arrests show that black and Latino civilians are more likely to be stopped, searched and arrested than whites. In 2012, a white attorney spent days trying to get himself arrested in Brooklyn for carrying graffiti stencils and spray paint, a Class B misdemeanor. Even when police saw him tagging the City Hall gateposts, they sped past him, ignoring a crime for which 3,598 people were arrested by the New York Police Department the following year.

Before adopting risk-assessment tools in the judicial decision-making process, jurisdictions should demand that any tool being implemented undergo a thorough and independent peer-review process. We need more transparencyand better data to learn whether these risk assessments have disparate impacts on defendants of different races. Foundations and organizations developing risk-assessment tools should be willing to release the data used to build these tools to researchers to evaluate their techniques for internal racial bias and problems of statistical interpretation. Even better, with multiple sources of data, researchers could identify biases in data generated by the criminal justice system before the data is used to make decisions about liberty. Unfortunately, producers of risk-assessment tools — even nonprofit organizations — have not voluntarily released anonymized data and computational details to other researchers, as is now standard in quantitative social science research….(More)”.

How to Do Social Science Without Data

Curated on Posted on by Stefaan Verhulst

Neil Gross in the New York Times: With the death last month of the sociologist Zygmunt Bauman at age 91, the intellectual world lost a thinker of rare insight and range. Because his style of work was radically different from that of most social scientists in the United States today, his passing is an occasion to consider what might be gained if more members of our profession were to follow his example….

Weber saw bureaucracies as powerful, but dispiritingly impersonal. Mr. Bauman amended this: Bureaucracy can be inhuman. Bureaucratic structures had deadened the moral sense of ordinary German soldiers, he contended, which made the Holocaust possible. They could tell themselves they were just doing their job and following orders.

Later, Mr. Bauman turned his scholarly attention to the postwar and late-20th-century worlds, where the nature and role of all-encompassing institutions were again his focal point. Craving stability after the war, he argued, people had set up such institutions to direct their lives — more benign versions of Weber’s bureaucracy. You could go to work for a company at a young age and know that it would be a sheltering umbrella for you until you retired. Governments kept the peace and helped those who couldn’t help themselves. Marriages were formed through community ties and were expected to last.

But by the end of the century, under pressure from various sources, those institutions were withering. Economically, global trade had expanded, while in Europe and North America manufacturing went into decline; job security vanished. Politically, too, changes were afoot: The Cold War drew to an end, Europe integrated and politicians trimmed back the welfare state. Culturally, consumerism seemed to pervade everything. Mr. Bauman noted major shifts in love and intimacy as well, including a growing belief in the contingency of marriage and — eventually — the popularity of online dating.

In Mr. Bauman’s view, it all connected. He argued we were witnessing a transition from the “solid modernity” of the mid-20th century to the “liquid modernity” of today. Life had become freer, more fluid and a lot more risky. In principle, contemporary workers could change jobs whenever they got bored. They could relocate abroad or reinvent themselves through shopping. They could find new sexual partners with the push of a button. But there was little continuity.

Mr. Bauman considered the implications. Some thrived in this new atmosphere; the institutions and norms previously in place could be stultifying, oppressive. But could a transient work force come together to fight for a more equitable distribution of resources? Could shopping-obsessed consumers return to the task of being responsible, engaged citizens? Could intimate partners motivated by short-term desire ever learn the value of commitment?…(More)”

Facebook introduces a way to help your neighbors after a disaster

Curated on Posted on by Stefaan Verhulst

Casey Newton at the Verge: “Last year Facebook announced Community Help, a new part of its Safety Check feature designed to connect disaster victims with Facebook users in the area who are offering their help. Now whenever Safety Check is activated, Community Help will let users find or offer food, shelter, transportation, and other forms of assistance. After testing the feature in December, Facebook is beginning to roll it out today in the United States, Canada, India, Saudi Arabia, Australia, and New Zealand.

Facebook says Community Help represents a logical next step for Safety Check, which was first announced in November 2014. Initially, each Safety Check was essentially created manually by Facebook’s team.

In November, the company announced that Safety Check would become more automated. Global crisis reporting agencies send Facebook alerts, which it then attempts to match to user posts in a geographic area. When it finds a spike in user posts, coupled with the alert, Facebook activates Safety Check. The company says employees oversee the process to prevent false positives — something it hasn’t always succeeded at doing.

In discussions with relief agencies, Facebook says it found that disaster victims were often coming to Facebook in search of help — or to offer some. In some cases, product designer Preethi Chethan says, they were pasting Facebook posts into spreadsheets to help sort them.

Community Help is designed to make post-disaster matchmaking easier. You’ll find it inside Safety Check — go there in the wake of a calamity, and after marking yourself safe you can create a post seeking or offering help. For starters, Community Help will only be available after natural disasters and accidents….(More)”.

It takes more than social media to make a social movement

Curated on Posted on by Stefaan Verhulst

Hayley Tsukayama in the Washington Post: “President Trump may have used the power of social media to make his way into the White House, but now social media networks are showing that muscle can work for his opposition, too. Last week, more than 1 million marchers went to Washington and cities around the country — sparked by a Facebook post from one woman with no history of activism. This weekend, the Internet exploded again in discussion about Trump’s travel suspension order, and many used social media to get together and protest the decision.

Twitter said that more than 25 million tweets were sent about the order — as compared with 12 million about Trump’s inauguration. Facebook said that its users generated 151 million “likes, posts, comments and shares” related to the ban, less than the 208 million interactions generated about the inauguration. The companies didn’t reveal how many of those were aimed at organizing, but the social media calls to get people to protest are a testament to the power of these platforms to move people.

The real question, however, is whether this burgeoning new movement can avoid the fate of many so others kick-started by the power of social networks — only to find that it’s much harder to make political change than to make a popular hashtag….

Zeynep Tufekci, an associate professor at the University of North Carolina at Chapel Hill who has written a forthcoming book on the power and fragility of movements borne of social media, found in her research that the very ability for these movements to scale quickly is, in part, why they also can fall apart so quickly compared with traditional grass-roots campaigns….

Now, organizers can bypass the time it takes to build up the infrastructure for a massive march and all the publicity that comes with it. But that also means their high-profile movements skip some crucial organizing steps.

“Digitally networked movements look like the old movements. But by the time the civil rights movement had such a large march, they’d been working on [the issues] for 10 years — if not more,” Tufekci said. The months or even years spent discussing logistics, leafleting and building a coalition, she said, were crucial to the success of the civil rights movements. Other successful efforts, such as the Human Rights Campaign’s efforts to end the “don’t ask, don’t tell” policy against allowing gay people to serve openly in the military were also rooted in organization structures that had been developing and refining their demands for years to present a unified front. Movements organized over social networks often have more trouble jelling, she said, particularly if different factions air their differences on Facebook and Twitter, drawing attention to fractures in a movement….(More).”

The City as a Lab: Open Innovation Meets the Collaborative Economy

Curated on Posted on by Stefaan Verhulst

Introduction to Special Issue of California Management Review by Boyd Cohen, Esteve Almirall, and Henry Chesbrough: “This article introduces the special issue on the increasing role of cities as a driver for (open) innovation and entrepreneurship. It frames the innovation space being cultivated by proactive cities. Drawing on the diverse papers selected in this special issue, this introduction explores a series of tensions that are emerging as innovators and entrepreneurs seek to engage with local governments and citizens in an effort to improve the quality of life and promote local economic growth…Urbanization, the democratization of innovation and technology, and collaboration are converging paradigms helping to drive entrepreneurship and innovation in urban areas around the globe. These three factors are converging to drive innovation and entrepreneurship in cities and have been referred to as the urbanpreneur spiral….(More)”figure

Using GitHub in Government: A Look at a New Collaboration Platform

Curated on Posted on by Stefaan Verhulst

Justin Longo at the Center for Policy Informatics: “…I became interested in the potential for using GitHub to facilitate collaboration on text documents. This was largely inspired by the 2012 TED Talk by Clay Shirky where he argued that open source programmers could teach us something about how to do open governance:

Somebody put up a tool during the copyright debate last year in the Senate, saying, “It’s strange that Hollywood has more access to Canadian legislators than Canadian citizens do. Why don’t we use GitHub to show them what a citizen-developed bill might look like?” …

For this research, we undertook a census of Canadian government and public servant accounts on GitHub and surveyed those users, supplemented by interviews with key government technology leaders.

This research has now been published in the journal Canadian Public Administration. (If you don’t have access to the full document through the publisher, you can also find it here).

Despite the growing enthusiasm for GitHub (mostly from those familiar with open source software development), and the general rhetoric in favour of collaboration, we suspected that getting GitHub used in public sector organizations for text collaboration might be an uphill battle – not least of which because of the steep learning curve involved in using GitHub, and its inflexibility when being used to edit text.

The history of computer-supported collaborative work platforms is littered with really cool interfaces that failed to appeal to users. The experience to date with GitHub in Canadian governments reflects this, as far as our research shows.

We found few government agencies having an active presence on GitHub compared to social media presence in general. And while federal departments and public servants on GitHub are rare, provincial, territorial, First Nations and local governments are even rarer.

For individual accounts held by public servants, most were found in the federal government at higher rates than those found in broader society (see Mapping Collaborative Software). Within this small community, the distribution of contributions per user follows the classic long-tail distribution with a small number of contributors responsible for most of the work, a larger number of contributors doing very little on average, and many users contributing nothing.

GitHub is still resisted by all but the most technically savvy. With a peculiar terminology and work model that presupposes a familiarity with command line computer operations and the language of software coding, using GitHub presents many barriers to the novice user. But while it is tempting to dismiss GitHub, as it currently exists, as ill-suited as a collaboration tool to support document writing, it holds potential as a useful platform for facilitating collaboration in the public sector.

As an example, to help understand how GitHub might be used within governments for collaboration on text documents, we discuss a briefing note document flow in the paper (see the paper for a description of this lovely graphic).

screen-shot-2017-01-21-at-8-54-24-pm

A few other finding are addressed in the paper, from why public servants may choose not to collaborate even though they believe it’s the right thing to do, to an interesting story about what propelled the use of GitHub in the government of Canada in the first place….(More)”