Sector: future of work

When is the crowd wise or can the people ever be trusted?

Curated on Posted on by Stefaan Verhulst

Julie Simon at NESTA: “Democratic theory has tended to take a pretty dim view of people and their ability to make decisions. Many political philosophers believe that people are at best uninformed and at worst, ignorant and incompetent.  This view is a common justification for our system of representative democracy – people can’t be trusted to make decisions so this responsibility should fall to those who have the expertise, knowledge or intelligence to do so.

Think back to what Edmund Burke said on the subject in his speech to the Electors of Bristol in 1774, “Your representative owes you, not his industry only, but his judgement; and he betrays, instead of serving you, if he sacrifices it to your opinion.” He reminds us that “government and legislation are matters of reason and judgement, and not of inclination”. Others, like the journalist Charles Mackay, whose book on economic bubbles and crashes,Extraordinary Popular Delusions and the Madness of Crowds, had an even more damning view of the crowd’s capacity to exercise either judgement or reason.

The thing is, if you believe that ‘the crowd’ isn’t wise then there isn’t much point in encouraging participation – these sorts of activities can only ever be tokenistic or a way of legitimising the decisions taken by others.

There are then those political philosophers who effectively argue that citizens’ incompetence doesn’t matter. They argue that the aggregation of views – through voting – eliminates ‘noise’ which enables you to arrive at optimal decisions. The larger the group, the better its decisions will be.  The corollary of this view is that political decision making should involve mass participation and regular referenda – something akin to the Swiss model.

Another standpoint is to say that there is wisdom within crowds – it’s just that it’s domain specific, unevenly distributed and quite hard to transfer. This idea was put forward by Friedrich Hayek in his seminal 1945 essay on The Use of Knowledge in Society in which he argues that:

“…the knowledge of the circumstances of which we must make use never exists in concentrated or integrated form, but solely as the dispersed bits of incomplete and frequently contradictory knowledge which all the separate individuals possess. The economic problem of society is thus not merely a problem of how to allocate ‘given’ resources……it is a problem of the utilization of knowledge not given to anyone in its totality”.

Hayek argued that it was for this reason that central planning couldn’t work since no central planner could ever aggregate all the knowledge distributed across society to make good decisions.

More recently, Eric Von Hippel built on these foundations by introducing the concept of information stickiness; information is ‘sticky’ if it is costly to move from one place to another. One type of information that is frequently ‘sticky’ is information about users’ needs and preferences.[1] This helps to account for why manufacturers tend to develop innovations which are incremental – meeting already identified needs – and why so many organisations are engaging users in their innovation processes:  if knowledge about needs and tools for developing new solutions can be co-located in the same place (i.e. the user) then the cost of transferring sticky information is eliminated…..

There is growing evidence on how crowdsourcing can be used by governments to solve clearly defined technical, scientific or informational problems. Evidently there are significant needs and opportunities for governments to better engage citizens to solve these types of problems.

There’s also a growing body of evidence on how digital tools can be used to support and promote collective intelligence….

So, the critical task for public officials is to have greater clarity over the purpose of engagement –  in order to better understand which methods of engagement should be used and what kinds of  groups should be targeted.

At the same time, the central question for researchers is when and how to tap into collective intelligence: what tools and approaches can be used when we’re looking at arenas which are often sites of contestation? Should this input be limited to providing information and expertise to be used by public officials or representatives, or should these distributed experts exercise some decision making power too? And when we’re dealing with value based judgements when should we rely on large scale voting as a mechanism for making ‘smarter’ decisions and when are deliberative forms of engagement more appropriate? These are all issues we’re exploring as part of our ongoing programme of work on democratic innovations….(More)”

The well-informed city: A decentralized, bottom-up model for a smart city service using information and self-organization

Curated on Posted on by Stefaan Verhulst

Paper by Eyal Feder-Levy, Efrat Blumenfeld-Liebertal, and Juval Portugali for the Smart Cities Conference (ISC2), 2016 IEEE International: “Smart Cities, a concept widely growing in popularity, describes cities that use digital technology, data analysis and connectivity to create value. The basic abstraction of a Smart City service includes collecting data about an urban issue, transmitting it to a central decision making process and “improving” the city with the insights generated. This model has spurred much critique, claiming Smart Cities are undemocratic, discriminatory and cannot significantly improve citizen’s quality of life. But what if the citizens were active in the process? It was Jane Jacobs who said “Cities have the capability of providing something for everybody, only because, and only when, they are created by everybody.” In this paper we lay a conceptual groundwork to envision “The Well-Informed City” — a decentralized, self-organizing Smart City service, where the value is created by everybody. The agents, who are the citizens of the city, are the ones who use the data to create value. We base the model on the cities’ feature of Self-Organization as described in the domain of Complexity Theory of Cities. We demonstrate its theoretical possibility, describe a short case study and finish with suggestions for future empirical research. This work is highly significant due to the ubiquitous nature of contemporary mobile based information services and growing open data sets….(More)”

Gov2Vec: Learning Distributed Representations of Institutions and Their Legal Text

Curated on Posted on by Stefaan Verhulst

John J. Nay at arXiv.org:”We compare policy differences across institutions by embedding representations of the entire legal corpus of each institution and the vocabulary shared across all corpora into a continuous vector space. We apply our method, Gov2Vec, to Supreme Court opinions, Presidential actions, and official summaries of Congressional bills. The model discerns meaningful differences between government branches. We also learn representations for more fine-grained word sources: individual Presidents and (2-year) Congresses. The similarities between learned representations of Congresses over time and sitting Presidents are negatively correlated with the bill veto rate, and the temporal ordering of Presidents and Congresses was implicitly learned from only text. With the resulting vectors we answer questions such as: how does Obama and the 113th House differ in addressing climate change and how does this vary from environmental or economic perspectives? Our work illustrates vector-arithmetic-based investigations of complex relationships between word sources based on their texts. We are extending this to create a more comprehensive legal semantic map….(More)”

The challenges and limits of big data algorithms in technocratic governance

Curated on Posted on by Stefaan Verhulst

Paper by Marijn Janssen and George Kuk in Government Information Quarterly: “Big data is driving the use of algorithm in governing mundane but mission-critical tasks. Algorithms seldom operate on their own and their (dis)utilities are dependent on the everyday aspects of data capture, processing and utilization. However, as algorithms become increasingly autonomous and invisible, they become harder for the public to detect and scrutinize their impartiality status. Algorithms can systematically introduce inadvertent bias, reinforce historical discrimination, favor a political orientation or reinforce undesired practices. Yet it is difficult to hold algorithms accountable as they continuously evolve with technologies, systems, data and people, the ebb and flow of policy priorities, and the clashes between new and old institutional logics. Greater openness and transparency do not necessarily improve understanding. In this editorial we argue that through unraveling the imperceptibility, materiality and governmentality of how algorithms work, we can better tackle the inherent challenges in the curatorial practice of data and algorithm. Fruitful avenues for further research on using algorithm to harness the merits and utilities of a computational form of technocratic governance are presented….(More)”

 

Data Ethics: Investing Wisely in Data at Scale

Curated on Posted on by Stefaan Verhulst

Report by David Robinson & Miranda Bogen prepared for the MacArthur and Ford Foundations: ““Data at scale” — digital information collected, stored and used in ways that are newly feasible — opens new avenues for philanthropic investment. At the same time, projects that leverage data at scale create new risks that are not addressed by existing regulatory, legal and best practice frameworks. Data-oriented projects funded by major foundations are a natural proving ground for the ethical principles and controls that should guide the ethical treatment of data in the social sector and beyond.

This project is an initial effort to map the ways that data at scale may pose risks to philanthropic priorities and beneficiaries, for grantmakers at major foundations, and draws from desk research and unstructured interviews with key individuals involved in the grantmaking enterprise at major U.S. foundations. The resulting report was prepared at the joint request of the MacArthur and Ford Foundations.

Grantmakers are exploring data at scale, but currently have poor visibility into its benefits and risks. Rapid technological change, the scarcity of data science expertise, limited training and resources, and a lack of clear guideposts around emergent risks all contribute to this problem.

Funders have important opportunities to invest in, learn from, and innovate around data-intensive projects, in concert with their grantees. Grantmakers should not treat the new ethical risks of data at scale as a barrier to investment, but these risks also must not become a blind spot that threatens the success and effectiveness of philanthropic projects. Those working with data at scale in the philanthropic context have much to learn: throughout our conversations with stakeholders, we heard consistently that grantmakers and grantees lack baseline knowledge on using data at scale, and many said that they are unsure how to make better informed decisions, both about data’s benefits and about its risks. Existing frameworks address many risks introduced by data-intensive grantmaking, but leave some major gaps. In particular, we found that:

  • Some new data-intensive research projects involve meaningful risk to vulnerable populations, but are not covered by existing human subjects regimes, and lack a structured way to consider these risks. In the philanthropic and public sector, human subject review is not always required and program officers, researchers, and implementers do not yet have a shared standard by which to evaluate ethical implications of using public or existing data, which is often exempt from human subjects review.
  • Social sector projects often depend on data that reflects patterns of bias or discrimination against vulnerable groups, and face a challenge of how to avoid reinforcing existing disparities. Automated decisions can absorb and sanitize bias from input data, and responsibly funding or evaluating statistical models in data-intensive projects increasingly demands advanced mathematical literacy which foundations lack.
  • Both data and the capacity to analyze it are being concentrated in the private sector, which could marginalize academic and civil society actors.Some individuals and organizations have begun to call attention to these issues and create their own trainings, guidelines, and policies — but ad hoc solutions can only accomplish so much.

To address these and other challenges, we’ve identified eight key questions that program staff and grantees need to consider in data-intensive work:

  1. For a given project, what data should be collected, and who should have access to it?
  2. How can projects decide when more data will help — and when it won’t?
  3. How can grantmakers best manage the reputational risk of data-oriented projects that may be at a frontier of social acceptability?
  4. When concerns are recognized with respect to a data-intensive grant, how will those concerns get aired and addressed?
  5. How can funders and grantees gain the insight they need in order to critique other institutions’ use of data at scale?
  6. How can the social sector respond to the unique leverage and power that large technology companies are developing through their accumulation of data and data-related expertise?
  7. How should foundations and nonprofits handle their own data?
  8. How can foundations begin to make the needed long term investments in training and capacity?

Newly emergent ethical issues inherent in using data at scale point to the need for both a broader understanding of the possibilities and challenges of using data in the philanthropic context as well as conscientious treatment of data ethics issues. Major foundations can play a meaningful role in building a broader understanding of these possibilities and challenges, and they can set a positive example in creating space for open and candid reflection on these issues. To those ends, we recommend that funders:…(More)”

You Can Help Map the Accessibility of the World

Curated on Posted on by Stefaan Verhulst

Josh Cohen in Next City: “…using a web app called Project Sidewalk….The app, from a team at the University of Maryland’s Human-Computer Interaction Lab, crowdsources audit data in order to map urban accessibility. After taking a brief tutorial on what to look for and a how-to, participants “walk” the D.C. streets using Google Street View. The app provides a set of tools to mark curb ramps (or a lack of them), broken sidewalks, and obstacles in the sidewalk, and rank them on a scale of 1 to 5 for level of accessibility.

Project Sidewalk’s public beta launched on August 30. As of this writing, 212 people have participated and audited 377.5 miles of sidewalk in D.C.

“We’re starting in D.C. as a launch point because we know D.C., we live here, we can do physical audits to validate the data we’re getting,” says Jon Froehlich, a University of Maryland professor who is leading the project. “But we want to expand to 10 more cities in the next year or two.”

Project Sidewalk tutorial

Project Sidewalk wants to produce a few end products with their data too. The first is an accessibility-mapping tool that offers end-to-end route directions that takes into account a person’s particular mobility challenges. Froehlich points out that barriers for someone in an electric wheelchair might be different than someone in a manual wheelchair or someone with vision impairment. The other product is an “access score” map that ranks a neighborhood’s accessibility and highlights problem areas.

Froehlich hopes departments of transportation might adopt the tool as well. “People tasked with improving infrastructure can start to use it to triage their work or verify their own data. A lot of cities don’t have money or time to go out and map the accessibility of their streets,” he says.

Crowdsourcing and using Street View to reduce the amount of labor required to conduct audits is an important first step for Project Sidewalk, but in order to expand to cities throughout the country, they need to automate the review process as much as possible. To do that, the team is experimenting with computer learning….(More)”.

Europe Should Promote Data for Social Good

Curated on Posted on by Stefaan Verhulst

Daniel Castro at Center for Data Innovation: “Changing demographics in Europe are creating enormous challenges for the European Union (EU) and its member states. The population is getting older, putting strain on the healthcare and welfare systems. Many young people are struggling to find work as economies recover from the 2008 financial crisis. Europe is facing a swell in immigration, increasingly from war-torn Syria, and governments are finding it difficult to integrate refugees and other migrants into society.These pressures have already propelled permanent changes to the EU. This summer, a slim majority of British voters chose to leave the Union, and many of those in favor of Brexit cited immigration as a motive for their vote.

Europe needs to find solutions to these challenges. Fortunately, advances in data-driven innovation that have helped businesses boost performance can also create significant social benefits. They can support EU policy priorities for social protection and inclusion by better informing policy and program design, improving service delivery, and spurring social innovations. While some governments, nonprofit organizations, universities, and companies are using data-driven insights and technologies to support disadvantaged populations, including unemployed workers, young people, older adults, and migrants, progress has been uneven across the EU due to resource constraints, digital inequality, and restrictive data regulations. renewed European commitment to using data for social good is needed to address these challenges.

This report examines how the EU, member-states, and the private sector are using data to support social inclusion and protection. Examples include programs for employment and labor-market inclusion, youth employment and education, care for older adults, and social services for migrants and refugees. It also identifies the barriers that prevent European countries from fully capitalizing on opportunities to use data for social good. Finally, it proposes a number of actions policymakers in the EU should take to enable the public and private sectors to more effectively tackle the social challenges of a changing Europe through data-driven innovation. Policymakers should:

  • Support the collection and use of relevant, timely data on the populations they seek to better serve;
  • Participate in and fund cross-sector collaboration with data experts to make better use of data collected by governments and non-profit organizations working on social issues;
  • Focus government research funding on data analysis of social inequalities and require grant applicants to submit plans for data use and sharing;
  • Establish appropriate consent and sharing exemptions in data protection regulations for social science research; and
  • Revise EU regulations to accommodate social-service organizations and their institutional partners in exploring innovative uses of data….(More)”

Can This Data-Driven Organization Help Those Most Desperate Escape Life on the Streets?

Curated on Posted on by Stefaan Verhulst

NationSwell: “…Community Solutions works in neighborhoods around the country to provide practical, data-driven solutions to the complicated problems involved in homelessness. The organization has already achieved great success: its 100,000 Homes campaign, which ran from 2010 to 2014, helped 186 participating communities house more than 105,000 homeless Americans across the country.” (Chronically homeless individuals make up 15 percent of the total homeless population, yet they utilize the majority of social services devoted towards helping them, including drop-in shelters.) To do this, it challenged the traditional approach of ending homelessness: requiring those living on the streets to demonstrate sobriety, steady income or mental health treatment, for example. Instead, it housed people first, an approach that has demonstrated overwhelming success: research finds that more than 85 percent of chronically homeless people housed through “Housing First” programs are still in homes two years later and unlikely to become homeless again.

“Technology played a critical role in the success of the 100,000 Homes campaign because it enabled multiple agencies to share and use the same data,” says Erin Connor, portfolio manager with the Cisco Foundation, which has supported Community Solutions’ technology-based initiatives. “By rigorously tracking, reporting and making decisions based on shared data, participating communities could track and monitor their progress against targets and contribute to achieving the collective goal.” As a result of this campaign, the estimated taxpayer savings was an astonishing $1.3 billion. Building on this achievement, its current Zero 2016 campaign works in 75 communities to sustainably end chronic and veteran homelessness altogether.

Technology and data gathering is critical for local and nationwide campaigns since homelessness is intimately connected to other social problems, like unemployment and poverty. One example of the local impact Community Solutions has had is in Brownsville (a neighborhood in Brooklyn, N.Y., that’s dominated by multiple public housing projects) via the Brownsville Partnership, which is demonstrating that these problems can be solved — to create “the endgame of homelessness,” as Haggerty puts it.

In Brownsville, the official unemployment rate is 16 percent, “about double that of Brooklyn” as a whole, Haggerty says, noting that the statistic excludes those not currently looking for work. In response, the organization works with existing job training programs, digging into their data and analyzing it to improve effectiveness and achieve success.

“Data is at the heart of everything we do, as far as understanding where to focus our efforts and how to improve our collective performance,” Haggerty explains. Analyzing usage data, Community Solutions works with health care providers, nonprofits, and city and state governments to figure out where the most vulnerable populations live, what systems they interact with and what help they need….(More)”

Collective intelligence and international development

Curated on Posted on by Stefaan Verhulst

Gina Lucarelli, Tom Saunders and Eddie Copeland at Nesta: “The mountain kingdom of Lesotho, a small landlocked country in Sub-Saharan Africa, is an unlikely place to look for healthcare innovation. Yet in 2016, it became the first country in Africa to deploy the test and treat strategy for treating people with HIV. Rather than waiting for white blood cell counts to drop, patients begin treatment as soon as they are diagnosed. This strategy is backed by the WHO as it has the potential to increase the number of people who are able to access treatment, consequently reducing transmisssion and keeping people with HIV healthy and alive for longer.

While lots of good work is underway in Lesotho, and billions have been spent on HIV programmes in the country, the percentage of the population infected with HIV has remained steady and is now almost 23%. Challenges of this scale need new ideas and better ways to adopt them.

On a recent trip to Lesotho as part of a project with the United Nations Development Group, we met various UN agencies, the World Bank, government leaders, civil society actors and local businesses, to learn about the key development issues in Lesotho and to discuss the role that ‘collective intelligence’ might play in creating better country development plans. The key question Nesta and the UN are working on is: how can we increase the impact of the UN’s work by tapping into the ideas, information and possible solutions which are distributed among many partners, the private sector, and the 2 million people of Lesotho?

…our framework of collective intelligence, a set of iterative stages which can help organisations like the UN tap into the ideas, information and possible solutions of groups and individuals which are not normally involved included in the problem solving process. For each stage, we also presented a number of examples of how this works in practice.

Collective intelligence framework – stages and examples

  1. Better understanding the facts, data and experiences: New tools, from smartphones to online communities enable researchers, practitioners and policymakers to collect much larger amounts of data much more quickly. Organisations can use this data to target their resources at the most critical issues as well as feed into the development of products and services that more accurately meet the needs of citizens. Examples include mPower, a clinical study which used an app to collect data about people with Parkinsons disease via surveys and smartphone sensors.

  2. Better development of options and ideas: Beyond data collection, organisations can use digital tools to tap into the collective brainpower of citizens to come up with better ideas and options for action. Examples include participatory budgeting platforms like “Madame Mayor, I have an idea” and challenge prizes, such as USAID’s Ebola grand challenge.

  3. Better, more inclusive decision making: Decision making and problem solving are usually left to experts, yet citizens are often best placed to make the decisions that will affect them. New digital tools make it easier than ever for governments to involve citizens in policymaking, planning and budgeting. Our D-CENT tools enable citizen involvement in decision making in a number of fields. Another example is the Open Medicine Project, which designs digital tools for healthcare in consultation with both practitioners and patients.

  4. Better oversight and improvement of what is done: From monitoring corruption to scrutinising budgets, a number of tools allow broad involvement in the oversight of public sector activity, potentially increasing accountability and transparency. The Family and Friends Test is a tool that allows NHS users in the UK to submit feedback on services they have experienced. So far, 25 million pieces of feedback have been submitted. This feedback can be used to stimulate local improvement and empower staff to carry out changes… (More)”

Bringing together the United States of data

Curated on Posted on by Stefaan Verhulst

“The U.S. Data Federation will support government-wide data standardization and data federation initiatives across both Federal agencies and local governments. This is intended to be a fundamental coordinating mechanism for a more open and interconnected digital government by profiling and supporting use-cases that demonstrate unified and coherent data architectures across disparate government agencies. These examples will highlight emerging data standards and API initiatives across all levels of government, convey the level of maturity for each effort, and facilitate greater participation by government agencies. Initiatives that may be profiled within the U.S. Data Federation include Open311, DOT’s National Transit Map, the Project Open Data metadata schema, Contact USA, and the Police Data Initiative. As part of the U.S. Data Federation, GSA will also pilot the development of reusable components needed for a successful data federation strategy including schema documentation tools, schema validation tools, and automated data aggregation and normalization capabilities. The U.S. Data Federation will provide more sophisticated and seamless opportunities on the foundation of U.S. open data initiatives by allowing the public to more easily do comparative data analysis across government bodies and create applications that work across multiple government agencies….(More)”

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