What we see when we see transparency


Matthew Taylor at the RSA: “…social coordination theory…. is one of the most powerful ways to analyse complex social problems and to develop effective solutions. I spoke this morning to the Transparency Task Force and it gave me an opportunity to apply the theory to the issue of corporate openness.

First, a very brief recap of the theory:

My own approach focusses on the four modes as approaches to the challenge of social coordination. Human beings are complex social beings who have to work together to survive and flourish. The hierarchical perspective puts emphasis on leadership, strategy and expertise as the way to coordinate human activity. The solidaristic view emphasises the glue of belonging and shared values. The individualistic view sees coordination as emerging largely spontaneously and its goal being to provide a platform of individual ambition and competitive endeavour. The fatalistic view sees effective coordination, variously, as intractable, unlikely to deliver intended outcomes or irrelevant to the things that make it hardest to be human.

Each mode has a substrate in human evolution and psychology – these competing theories have emerged from who we are as a species. Each mode or combination of modes has been dominant at different times in our history. Also, each mode generates behavioural and ideological predispositions: Solidaristic views – which emphasise membership and values – are often associated with rigid ideologies (on both the left and right); individualism goes with liberal attitudes (again of both left and right varieties).

In a kind of fractal which stretches from individual preferences to global problem solving, each mode is available as a perspective on complex social problems. Crucially, for the kind of problems which the modern world increasingly generates, the best solutions will combine aspects of each method. But there is a problem: each mode is, in part, a critique of the others. Each has both benign and malign aspects; hierarchy can be strategic and overbearing, individualism can be enterprising and selfish, solidarity can be altruistic and tribal, fatalism can be stoical and defeatist. The theory explains why success can so quickly turn to failure. Even when the modes are successfully combined – what I call a ‘fully engaged’ solution – internal dynamics or external shocks will sooner or later upset the balance.

This is all rather theoretical so let me be more concrete. If we are trying to solve a problem like encouraging and enabling a corporation to be more transparent we need to understand the arguments both in favour and against such a move from each of the perspectives. If we don’t many people, many ideas and many approaches will be ignored. In short, we will be much more likely to fail.

Here is a simple guide to what might be seen as the pros and cons of greater transparency from the four standpoints:

perspectives on transparency Matthew Taylor

People in authority and those who see things from a hierarchical perspective (we all see things from different perspectives at different times and in different places) will worry that transparency will make decision making more difficult and that, by opening up things like the underlying business model to scrutiny by customers and competitors, it would threaten the interests of the organisation. Conversely, the hierarchical case for transparency is that it can increase trust and understanding towards leaders and the challenges they face and it can aid alignment, clarity and commitment by exposing practices that don’t fit with corporate strategy.

Those who approach things from a solidaristic perspective are often the most outspoken champions of transparency. They see it as increasing integrity as companies have to live up to their stated values and fairness as unfair practices are exposed.  However, there are also solidaristic concerns; what if transparency exposes vulnerable people or if transparency makes life harder for the team to which I belong (the company or my part of the company)?

From an individualistic starting point transparency can be viewed suspiciously as promoting a focus on process rather than outcomes and also being adverse to risk and reward (you can justify the means by the ends only if you have been able to achieve the ends without someone looking over your shoulder). On the other hand, the individualistic case for transparency can cite its contribution to innovation (by looking under the bonnet and seeing all the working parts we have better insights into what can be improved) and the promotion of rewards based on fair competition instead of covert rent-seeking or organisational nepotism.

Finally, those in a fatalistic mind set will tend to see transparency as either irrelevant or – and this is a more forensic critique – illusory (the secret stuff will just get hidden better). However there is also an appeal to be made to fatalists that transparency can help reveal warning signs of future dangers and make it easier to mount a defence when things go wrong (‘even if we failed, we can show that we tried’).

To win the case for transparency and also to implement it effectively its advocates need to stress the positives from the different perspectives and also address the legitimate concerns; both of which should make it easier to confront objections that are not reasonable. …(More)”

Living in the World of Both/And


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


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)”

Coming soon: The Conversation Global


Screen Shot 2016-09-22 at 8.54.58 AMThe Conversation, an independent news and commentary website produced by academics and journalists, launches its Global edition this month.

The Conversation Global will publish commentary, analysis and research from the academic community worldwide. We will engage scholars from across the world, featuring perspectives from the Global South and North on the most pressing international issues. All content will be published under Creative Commons.

The site is open and free for everyone to read.

The Wisdom of the Crowd is what science really needs


Science/Disrupt: “In a world where technology allows for global collaboration, and in a time when we’re finally championing diversity of thought, there are few barriers to getting the right people together to work on some of our most pressing problems. Governments and research labs are attempting to apply this mentality to science through what is known as ‘Citizen Science’ – research conducted in part by the public (amateur scientists) in partnership with the professionals.

The concept of Citizen Science is brilliant: moving science forward, faster, by utilising the wisdom and volume of the crowd. …

But Citizen Science goes beyond working directly with people with specific data to share. Zooniverse – the home of Citizen Science online – lists hundreds of projects which anyone can get involved with to help advance science. From mapping the galaxy and looking for comets, to seeking outAustralian wildlife and helping computers understand animal faces, the projects span across many subjects.

But when you dig deeper into the tasks being asked of these CitizenScientists, you find that – really – it’s a simple data capture activity. There’s no skill involved other than engaging your eyes to see and fingers to click and type. It’s not the wisdom of the crowd which is being tapped into.

You could argue that people are interested purely in being a part of important research – which of course is true for many – but it misses the point that scientists are simply missing out on a great resource of intellect at their fingertips.

There has been a rise of crowdsourced solutions over the last few years. rLoopis an organisation formed over Reddit to propose a Hyperloop transportation capsule; Techfugees is a Global community of technologists who team up to propose and build solutions to problems facing the increasing numbers of refugees around the world;  and XPRIZE is an open competition offering winning teams large sums of money and support to solve the global problems they select each year.

The difference between crowdsourcing and Citizen Science is that in the former, a high value is placed on ideas. There’s a general understanding that‘two minds are better than one’ and that by empowering a larger, more diverse pool of people to engage with important and purposeful work, a better solution will be found faster.

With Citizen Science, the mood is that of the public only being capable of playing hide and seek with pictures and completing menial, time consuming work that the scientists are simply too busy to do. …(More)”

Responsible Data in Agriculture


Report by Lindsay Ferris and Zara Rahman for GODAN: “The agriculture sector is creating increasing amounts of data, from many different sources. From tractors equipped with GPS tracking, to open data released by government ministries, data is becoming ever more valuable, as agricultural business development and global food policy decisions are being made based upon data. But the sector is also home to severe resource inequality. The largest agricultural companies make billions of dollars per year, in comparison with subsistence farmers growing just enough to feed themselves, or smallholder farmers who grow enough to sell on a year-by-year basis. When it comes to data and technology, these differences in resources translate to stark power imbalances in data access and use. The most well resourced actors are able to delve into new technologies and make the most of those insights, whereas others are unable to take any such risks or divert any of their limited resources. Access to and use of data has radically changed the business models and behaviour of some of those well resourced actors, but in contrast, those with fewer resources are receiving the same, limited access to information that they always have.

In this paper, we have approached these issues from a responsible data perspective, drawing upon the experience of the Responsible Data community1 who over the past three years have created tools, questions and resources to deal with the ethical, legal, privacy and security challenges that come from new uses of data in various sectors. This piece aims to provide a broad overview of some of the responsible data challenges facing these actors, with a focus on the power imbalance between actors, and looking into how that inequality affects behaviour when it comes to the agricultural data ecosystem. What are the concerns of those with limited resources, when it comes to this new and rapidly changing data environment? In addition, what are the ethical grey areas or uncertainties that we need to address in the future? As a first attempt to answer these questions, we spoke to 14 individuals with various perspectives on the sector to understand what the challenges are for them and for the people they work with. We also carried out desk research to dive deeper into these issues, and we provide here an analysis of our findings and responsible data challenges….(More)”

Infostorms. Why do we ‘like’? Explaining individual behavior on the social net.


Book by Hendricks, Vincent F. and  Hansen, Pelle G.: “With points of departure in philosophy, logic, social psychology, economics, and choice and game theory, Infostorms shows how information may be used to improve the quality of personal decision and group thinking but also warns against the informational pitfalls which modern information technology may amplify: From science to reality culture and what it really is, that makes you buy a book like this.

The information society is upon us. New technologies have given us back pocket libraries, online discussion forums, blogs, crowdbased opinion aggregators, social media and breaking news wherever, whenever. But are we more enlightened and rational because of it?

Infostorms provides the nuts and bolts of how irrational group behaviour may get amplified by social media and information technology. If we could be collectively dense before, now we can do it at light speed and with potentially global reach. That’s how things go viral, that is how cyberbullying, rude comments online, opinion bubbles, status bubbles, political polarisation and a host of other everyday unpleasantries start. Infostorms will give the story of the mechanics of these phenomena. This will help you to avoid them if you want or learn to start them if you must. It will allow you to stay sane in an insane world of information….(More)”

Research Handbook on Digital Transformations


Book edited by F. Xavier Olleros and Majlinda Zhegu: “The digital transition of the world economy is now entering a phase of broad and deep societal impact. While there is one overall transition, there are many different sectoral transformations, from health and legal services to tax reports and taxi rides, as well as a rising number of transversal trends and policy issues, from widespread precarious employment and privacy concerns to market monopoly and cybercrime. This Research Handbook offers a rich and interdisciplinary synthesis of some of the recent research on the digital transformations currently under way.

This comprehensive study contains chapters covering sectoral and transversal analyses, all of which are specially commissioned and include cutting-edge research. The contributions featured are global, spanning four continents and seven different countries, as well as interdisciplinary, including experts in economics, sociology, law, finance, urban planning and innovation management. The digital transformations discussed are fertile ground for researchers, as established laws and regulations, organizational structures, business models, value networks and workflow routines are contested and displaced by newer alternatives….(More)”

‘Homo sapiens is an obsolete algorithm’


Extract from Homo Deus: A Brief History of Tomorrow by Yuval Noah Harari: “There’s an emerging market called Dataism, which venerates neither gods nor man – it worships data. From a Dataist perspective, we may interpret the entire human species as a single data-processing system, with individual humans serving as its chips. If so, we can also understand the whole of history as a process of improving the efficiency of this system, through four basic methods:

1. Increasing the number of processors. A city of 100,000 people has more computing power than a village of 1,000 people.

2. Increasing the variety of processors. Different processors may use diverse ways to calculate and analyse data. Using several kinds of processors in a single system may therefore increase its dynamism and creativity. A conversation between a peasant, a priest and a physician may produce novel ideas that would never emerge from a conversation between three hunter-gatherers.

3. Increasing the number of connections between processors. There is little point in increasing the mere number and variety of processors if they are poorly connected. A trade network linking ten cities is likely to result in many more economic, technological and social innovations than ten isolated cities.

4. Increasing the freedom of movement along existing connections. Connecting processors is hardly useful if data cannot flow freely. Just building roads between ten cities won’t be very useful if they are plagued by robbers, or if some autocratic despot doesn’t allow merchants and travellers to move as they wish.
These four methods often contradict one another. The greater the number and variety of processors, the harder it is to freely connect them. The construction of the sapiens data-processing system accordingly passed through four main stages, each of which was characterised by an emphasis on different methods.

The first stage began with the cognitive revolution, which made it possible to connect unlimited sapiens into a single data-processing network. This gave sapiens an advantage over all other human and animal species. Although there is a limit to the number of Neanderthals, chimpanzees or elephants you can connect to the same net, there is no limit to the number of sapiens.

Sapiens used their advantage in data processing to overrun the entire world. However, as they spread into different lands and climates they lost touch with one another, and underwent diverse cultural transformations. The result was an immense variety of human cultures, each with its own lifestyle, behaviour patterns and world view. Hence the first phase of history involved an increase in the number and variety of human processors, at the expense of connectivity: 20,000 years ago there were many more sapiens than 70,000 years ago, and sapiens in Europe processed information differently from sapiens in China. However, there were no connections between people in Europe and China, and it would have seemed utterly impossible that all sapiens may one day be part of a single data-processing web.
The second stage began with agriculture and continued until the invention of writing and money. Agriculture accelerated demographic growth, so the number of human processors rose sharply, while simultaneously enabling many more people to live together in the same place, thereby generating dense local networks that contained an unprecedented number of processors. In addition, agriculture created new incentives and opportunities for different networks to trade and communicate.

Nevertheless, during the second phase, centrifugal forces remained predominant. In the absence of writing and money, humans could not establish cities, kingdoms or empires. Humankind was still divided into innumerable little tribes, each with its own lifestyle and world view. Uniting the whole of humankind was not even a fantasy.
The third stage kicked off with the appearance of writing and money about 5,000 years ago, and lasted until the beginning of the scientific revolution. Thanks to writing and money, the gravitational field of human co-operation finally overpowered the centrifugal forces. Human groups bonded and merged to form cities and kingdoms. Political and commercial links between different cities and kingdoms also tightened. At least since the first millennium BC – when coinage, empires, and universal religions appeared – humans began to consciously dream about forging a single network that would encompass the entire globe.

This dream became a reality during the fourth and last stage of history, which began around 1492. Early modern explorers, conquerors and traders wove the first thin threads that encompassed the whole world. In the late modern period, these threads were made stronger and denser, so that the spider’s web of Columbus’s days became the steel and asphalt grid of the 21st century. Even more importantly, information was allowed to flow increasingly freely along this global grid. When Columbus first hooked up the Eurasian net to the American net, only a few bits of data could cross the ocean each year, running the gauntlet of cultural prejudices, strict censorship and political repression.

But as the years went by, the free market, the scientific community, the rule of law and the spread of democracy all helped to lift the barriers. We often imagine that democracy and the free market won because they were “good”. In truth, they won because they improved the global data-processing system.

So over the last 70,000 years humankind first spread out, then separated into distinct groups and finally merged again. Yet the process of unification did not take us back to the beginning. When the different human groups fused into the global village of today, each brought along its unique legacy of thoughts, tools and behaviours, which it collected and developed along the way. Our modern larders are now stuffed with Middle Eastern wheat, Andean potatoes, New Guinean sugar and Ethiopian coffee. Similarly, our language, religion, music and politics are replete with heirlooms from across the planet.
If humankind is indeed a single data-processing system, what is its output? Dataists would say that its output will be the creation of a new and even more efficient data-processing system, called the Internet-of-All-Things. Once this mission is accomplished, Homo sapiens will vanish….(More)

The SAGE Handbook of Digital Journalism


Book edited by Tamara WitschgeC. W. AndersonDavid Domingo, and Alfred Hermida: “The production and consumption of news in the digital era is blurring the boundaries between professionals, citizens and activists. Actors producing information are multiplying, but still media companies hold central position. Journalism research faces important challenges to capture, examine, and understand the current news environment. The SAGE Handbook of Digital Journalism starts from the pressing need for a thorough and bold debate to redefine the assumptions of research in the changing field of journalism. The 38 chapters, written by a team of global experts, are organised into four key areas:

Section A: Changing Contexts

Section B: News Practices in the Digital Era

Section C: Conceptualizations of Journalism

Section D: Research Strategies

By addressing both institutional and non-institutional news production and providing ample attention to the question ‘who is a journalist?’ and the changing practices of news audiences in the digital era, this Handbook shapes the field and defines the roadmap for the research challenges that scholars will face in the coming decades….(More)”