How Charities Are Using Artificial Intelligence to Boost Impact

Curated on Posted on by Stefaan Verhulst

Nicole Wallace at the Chronicle of Philanthropy: “The chaos and confusion of conflict often separate family members fleeing for safety. The nonprofit Refunite uses advanced technology to help loved ones reconnect, sometimes across continents and after years of separation.

Refugees register with the service by providing basic information — their name, age, birthplace, clan and subclan, and so forth — along with similar facts about the people they’re trying to find. Powerful algorithms search for possible matches among the more than 1.1 million individuals in the Refunite system. The analytics are further refined using the more than 2,000 searches that the refugees themselves do daily.

The goal: find loved ones or those connected to them who might help in the hunt. Since Refunite introduced the first version of the system in 2010, it has helped more than 40,000 people reconnect.

One factor complicating the work: Cultures define family lineage differently. Refunite co-founder Christopher Mikkelsen confronted this problem when he asked a boy in a refugee camp if he knew where his mother was. “He asked me, ‘Well, what mother do you mean?’ ” Mikkelsen remembers. “And I went, ‘Uh-huh, this is going to be challenging.’ ”

Fortunately, artificial intelligence is well suited to learn and recognize different family patterns. But the technology struggles with some simple things like distinguishing the image of a chicken from that of a car. Mikkelsen believes refugees in camps could offset this weakness by tagging photographs — “car” or “not car” — to help train algorithms. Such work could earn them badly needed cash: The group hopes to set up a system that pays refugees for doing such work.

“To an American, earning $4 a day just isn’t viable as a living,” Mikkelsen says. “But to the global poor, getting an access point to earning this is revolutionizing.”

Another group, Wild Me, a nonprofit created by scientists and technologists, has created an open-source software platform that combines artificial intelligence and image recognition, to identify and track individual animals. Using the system, scientists can better estimate the number of endangered animals and follow them over large expanses without using invasive techniques….

To fight sex trafficking, police officers often go undercover and interact with people trying to buy sex online. Sadly, demand is high, and there are never enough officers.

Enter Seattle Against Slavery. The nonprofit’s tech-savvy volunteers created chatbots designed to disrupt sex trafficking significantly. Using input from trafficking survivors and law-enforcement agencies, the bots can conduct simultaneous conversations with hundreds of people, engaging them in multiple, drawn-out conversations, and arranging rendezvous that don’t materialize. The group hopes to frustrate buyers so much that they give up their hunt for sex online….

A Philadelphia charity is using machine learning to adapt its services to clients’ needs.

Benefits Data Trust helps people enroll for government-assistance programs like food stamps and Medicaid. Since 2005, the group has helped more than 650,000 people access $7 billion in aid.

The nonprofit has data-sharing agreements with jurisdictions to access more than 40 lists of people who likely qualify for government benefits but do not receive them. The charity contacts those who might be eligible and encourages them to call the Benefits Data Trust for help applying….(More)”.

What is a data trust?

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Essay by Jack Hardinges at ODI: “There are different interpretations of what a data trust is, or should be…

There’s not a well-used definition of ‘a data trust’, or even consensus on what one is. Much of the recent interest in data trusts in the UK has been fuelled by them being recommended as a way to ‘share data in a fair, safe and equitable way’ by a UK government-commissioned independent review into Artificial Intelligence (AI) in 2017. However, there has been wider international interest in the concept for some time.

At a very high level, the aim of data trusts appears to be to give people and organisations confidence when enabling access to data in ways that provide them with some value (either directly or indirectly) in return. Beyond that high level goal, there are a variety of thoughts about what form they should take. In our work so far, we’ve found different interpretations of the term ‘data trust’:

  • A data trust as a repeatable framework of terms and mechanisms.
  • A data trust as a mutual organisation.
  • A data trust as a legal structure.
  • A data trust as a store of data.
  • A data trust as public oversight of data access….(More)”

Hope for Democracy: 30 years of Participatory Budgeting Worldwide

Curated on Posted on by Stefaan Verhulst

Book edited by Nelson Dias: “Hope for Democracy” is not only the title of this book, but also the translation of a state of mind infected by innovation and transformative action of many people who in different parts of the world, are engaged in the construction of more lasting and intense ways of living democracy.

The articles found within this publication are “scales” of a fascinating journey through the paths of participatory democracy, from North America to Asia, Oceania to Europe, and Latin America to Africa.

With no single directions, it is up to the readers to choose the route they want to travel, being however invited to reinforce this “democratizing wave”, encouraging the emergence of new and renewed spaces of participation in the territories where they live and work….(More)

What if people were paid for their data?

Curated on Posted on by Stefaan Verhulst

The Economist: “Data Slavery” Jennifer Lyn Morone, an American artist, thinks this is the state in which most people now live. To get free online services, she laments, they hand over intimate information to technology firms. “Personal data are much more valuable than you think,” she says. To highlight this sorry state of affairs, Ms Morone has resorted to what she calls “extreme capitalism”: she registered herself as a company in Delaware in an effort to exploit her personal data for financial gain. She created dossiers containing different subsets of data, which she displayed in a London gallery in 2016 and offered for sale, starting at £100 ($135). The entire collection, including her health data and social-security number, can be had for £7,000.

Only a few buyers have taken her up on this offer and she finds “the whole thing really absurd”. ..Given the current state of digital affairs, in which the collection and exploitation of personal data is dominated by big tech firms, Ms Morone’s approach, in which individuals offer their data for sale, seems unlikely to catch on. But what if people really controlled their data—and the tech giants were required to pay for access? What would such a data economy look like?…

Labour, like data, is a resource that is hard to pin down. Workers were not properly compensated for labour for most of human history. Even once people were free to sell their labour, it took decades for wages to reach liveable levels on average. History won’t repeat itself, but chances are that it will rhyme, Mr Weyl predicts in “Radical Markets”, a provocative new book he has co-written with Eric Posner of the University of Chicago. He argues that in the age of artificial intelligence, it makes sense to treat data as a form of labour.

To understand why, it helps to keep in mind that “artificial intelligence” is something of a misnomer. Messrs Weyl and Posner call it “collective intelligence”: most AI algorithms need to be trained using reams of human-generated examples, in a process called machine learning. Unless they know what the right answers (provided by humans) are meant to be, algorithms cannot translate languages, understand speech or recognise objects in images. Data provided by humans can thus be seen as a form of labour which powers AI. As the data economy grows up, such data work will take many forms. Much of it will be passive, as people engage in all kinds of activities—liking social-media posts, listening to music, recommending restaurants—that generate the data needed to power new services. But some people’s data work will be more active, as they make decisions (such as labelling images or steering a car through a busy city) that can be used as the basis for training AI systems….

But much still needs to happen for personal data to be widely considered as labour, and paid for as such. For one thing, the right legal framework will be needed to encourage the emergence of a new data economy. The European Union’s new General Data Protection Regulation, which came into effect in May, already gives people extensive rights to check, download and even delete personal data held by companies. Second, the technology to keep track of data flows needs to become much more capable. Research to calculate the value of particular data to an AI service is in its infancy.

Third, and most important, people will have to develop a “class consciousness” as data workers. Most people say they want their personal information to be protected, but then trade it away for nearly nothing, something known as the “privacy paradox”. Yet things may be changing: more than 90% of Americans think being in control of who can get data on them is important, according to the Pew Research Centre, a think-tank….(More)”.

Smart Cities: Digital Solutions for a More Livable Future

Curated on Posted on by Stefaan Verhulst

Report by the McKinsey Global Institute (MGI): “After a decade of experimentation, smart cities are entering a new phase. Although they are only one part of the full tool kit for making a city great, digital solutions are the most powerful and cost-effective additions to that tool kit in many years. This report analyzes dozens of current applications and finds that cities could use them to improve some quality-of-life indicators by 10–30 percent.It also finds that even the most cutting-edge smart cities on the planet are still at the beginning of their journey. ƒ

Smart cities add digital intelligence to existing urban systems, making it possible to do more with less. Connected applications put real-time, transparent information into the hands of users to help them make better choices. These tools can save lives, prevent crime, and reduce the disease burden. They can save time, reduce waste, and even help boost social connectedness. When cities function more efficiently, they also become more productive places to do business. ƒ

MGI assessed how dozens of current smart city applications could perform in three sample cities with varying legacy infrastructure systems and baseline starting points. We found that these tools could reduce fatalities by 8–10 percent, accelerate emergency response times by 20–35 percent, shave the average commute by 15–20 percent, lower the disease burden by 8–15 percent, and cut greenhouse gas emissions by 10–15 percent, among other positive outcomes. ƒ

Our snapshot of deployment in 50 cities around the world shows that wealthier urban areas are generally transforming faster, although many have low public awareness and usage of the applications they have implemented. Asian megacities, with their young populations of digital natives and big urban problems to solve, are achieving exceptionally high adoption. Measured against what is possible today, even the global leaders have more work to do in building out the technology base, rolling out the full range of possible applications, and boosting adoption and user satisfaction. Many cities have not yet implemented some of the applications that could have the biggest potential impact. Since technology never stands still, the bar will only get higher. ƒ

The public sector would be the natural owner of 70 percent of the applications we examined. But 60 percent of the initial investment required to implement the full range of applications could come from private actors. Furthermore, more than half of the initial investment made by the public sector could generate a positive return, whether in direct savings or opportunities to produce revenue. ƒ

The technologies analyzed in this report can help cities make moderate or significant progress toward 70 percent of the Sustainable Development Goals. Yet becoming a smart city is less effective as an economic development strategy for job creation. ƒ Smart cities may disrupt some industries even as they present substantial market opportunities. Customer needs will force a reevaluation of current products and services to meet higher expectations of quality, cost, and efficiency in everything from mobility to healthcare.

Smart city solutions will shift value across the landscape of cities and throughout value chains. Companies looking to enter smart city markets will need different skill sets, creative financing models, and a sharper focus on civic engagement.

Becoming a smart city is not a goal but a means to an end. The entire point is to respond more effectively and dynamically to the needs and desires of residents. Technology is simply a tool to optimize the infrastructure, resources, and spaces they share. Few cities want to lag behind, but it is critical not to get caught up in technology for its own sake. Smart cities need to focus on improving outcomes for residents and enlisting their active participation in shaping the places they call home….(More)”.

Reduced‐Boundary Governance: The Advantages of Working Together

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Introduction by Jeremy L. Hall and R. Paul Battaglio of Special Issue of the Public Administration Review: “Collaboration, cooperation, and coproduction are all approaches that reflect the realization that creative solutions look beyond traditional, organizational, and structural boundaries to overcome various capacity deficiencies while working toward shared goals….One of the factors complicating measurement and analysis in multistakeholder approaches to solving problems and delivering services is the inherently intergovernmental and intersectoral nature of the work. Performance now depends on accumulated capacity across organizations, including a special form of capacity—the ability to work together collaboratively. Such activity within a government has been referred to as “whole of government” approaches or “joined up government” (Christensen and Lægreid 2007). We have terms for work across levels of government (intergovernmental relations) and between government and the public and private sectors (intersectoral relations), but on the whole, the creative, collaborative, and interactive activities in which governments are involved today transcend even these neat categories and classifications. We might call this phenomenon reduced‐boundary governance. Moving between levels of government or between sectors often changes the variables that are available for analysis, or at least introduces validity issues associated with differences in measurement and estimation (see Brandsen and Honingh 2016; Nabatchi, Sancino, and Sicilia 2017). Sometimes data are not available at all. And, of course, collaboration or pooling of resources typically occurs in an ad hoc or one‐off basis that is limited to a single problem, a single program, or a single defined period of time, further complicating study and knowledge accumulation.

Increasingly, public service is accomplished together rather than alone. Boundaries between organizations are becoming blurred in new approaches to solving public problems (Christensen and Lægreid 2007). PAR is committed to better understanding the circumstances under which collaboration, cooperation, and coproduction occurs. What are the necessary antecedents? What are the deterrents? We are interested in the challenges that organizations face as they pursue collaborative action that transcends boundaries. And, of course, we are interested in the efficiency and performance gains that are achieved as a result of those efforts, as well as in their long‐term sustainability.

In this issue, we feature a series of articles that highlight research that focuses on working together, through collaboration, coproduction, or cooperation. The issue begins with a look at right‐sizing the use of volunteerism in public and nonprofit organizations given their limitations and possibilities (Nesbit, Christensen, and Brudney 2018). Uzochukwu and Thomas (2018) then explore coproduction using a case study of Atlanta to better understand who uses it and why. Klok et al. (2018) presents a fascinating look at intermunicipal cooperation through polycentric regional governance in the Netherlands, with an eye toward the costs and effectiveness of those arrangements. McGuire, Hoang, and Prakash (2018) look at the effectiveness of voluntary environmental programs in pollution reduction. Using different policy tools as lenses for analysis, Jung, Malatesta, and LaLonde (2018) ask whether work release programs are improved by working together or working alone. Finally, Yi et al. (2018) explore the role of regional governance and institutional collective action in promoting environmental sustainability. Each of these pieces explores unique dimensions of working together, or governing beyond traditional boundaries….(More)”.

The Global Council on Extended Intelligence

Curated on Posted on by Stefaan Verhulst

“The IEEE Standards Association (IEEE-SA) and the MIT Media Lab are joining forces to launch a global Council on Extended Intelligence (CXI) composed of individuals who agree on the following:

One of the most powerful narratives of modern times is the story of scientific and technological progress. While our future will undoubtedly be shaped by the use of existing and emerging technologies – in particular, of autonomous and intelligent systems (A/IS) – there is no guarantee that progress defined by “the next” is beneficial. Growth for humanity’s future should not be defined by reductionist ideas of speed or size alone but as the holistic evolution of our species in positive alignment with the environmental and other systems comprising the modern algorithmic world.

We believe all systems must be responsibly created to best utilize science and technology for tangible social and ethical progress. Individuals, businesses and communities involved in the development and deployment of autonomous and intelligent technologies should mitigate predictable risks at the inception and design phase and not as an afterthought. This will help ensure these systems are created in such a way that their outcomes are beneficial to society, culture and the environment.

Autonomous and intelligent technologies also need to be created via participatory design, where systems thinking can help us avoid repeating past failures stemming from attempts to control and govern the complex-adaptive systems we are part of. Responsible living with or in the systems we are part of requires an awareness of the constrictive paradigms we operate in today. Our future practices will be shaped by our individual and collective imaginations and by the stories we tell about who we are and what we desire, for ourselves and the societies in which we live.

These stories must move beyond the “us versus them” media mentality pitting humans against machines. Autonomous and intelligent technologies have the potential to enhance our personal and social skills; they are much more fully integrated and less discrete than the term “artificial intelligence” implies. And while this process may enlarge our cognitive intelligence or make certain individuals or groups more powerful, it does not necessarily make our systems more stable or socially beneficial.

We cannot create sound governance for autonomous and intelligent systems in the Algorithmic Age while utilizing reductionist methodologies. By proliferating the ideals of responsible participant design, data symmetry and metrics of economic prosperity prioritizing people and the planet over profit and productivity, The Council on Extended Intelligence will work to transform reductionist thinking of the past to prepare for a flourishing future.

Three Priority Areas to Fulfill Our Vision

1 – Build a new narrative for intelligent and autonomous technologies inspired by principles of systems dynamics and design.

“Extended Intelligence” is based on the hypothesis that intelligence, ideas, analysis and action are not formed in any one individual collection of neurons or code…..

2 – Reclaim our digital identity in the algorithmic age

Business models based on tracking behavior and using outdated modes of consent are compounded by the appetites of states, industries and agencies for all data that may be gathered….

3 – Rethink our metrics for success

Although very widely used, concepts of exponential growth and productivity such as the gross domestic product (GDP) index are insufficient to holistically measure societal prosperity. … (More)”.

Balancing Act: Innovation vs. Privacy in the Age of Data Portability

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Thursday, July 12, 2018 @ 2 MetroTech Center, Brooklyn, NY 11201

RSVP here.

The ability of people to move or copy data about themselves from one service to another — data portability — has been hailed as a way of increasing competition and driving innovation. In many areas, such as through the Open Banking initiative in the United Kingdom, the practice of data portability is fully underway and propagating. The launch of GDPR in Europe has also elevated the issue among companies and individuals alike. But recent online security breaches and other experiences of personal data being transferred surreptitiously from private companies, (e.g., Cambridge Analytica’s appropriation of Facebook data), highlight how data portability can also undermine people’s privacy.

The GovLab at the NYU Tandon School of Engineering is pleased to present Jeni Tennison, CEO of the Open Data Institute, for its next Ideas Lunch, where she will discuss how data portability has been regulated in the UK and Europe, and what governments, businesses and people need to do to strike the balance between its risks and benefits.

—

Jeni Tennison is the CEO of the Open Data Institute. She gained her PhD from the University of Nottingham then worked as an independent consultant, specialising in open data publishing and consumption, before joining the ODI in 2012. Jeni was awarded an OBE for services to technology and open data in the 2014 New Year Honours.

Before joining the ODI, Jeni was the technical architect and lead developer for legislation.gov.uk. She worked on the early linked data work on data.gov.uk, including helping to engineer new standards for publishing statistics as linked data. She continues her work within the UK’s public sector as a member of the Open Standards Board.

Jeni also works on international web standards. She was appointed to serve on the W3C’s Technical Architecture Group from 2011 to 2015 and in 2014 she started to co-chair the W3C’s CSV on the Web Working Group. She also sits on the Advisory Boards for Open Contracting Partnership and the Data Transparency Lab.

Twitter handle: @JeniT

Against the Dehumanisation of Decision-Making – Algorithmic Decisions at the Crossroads of Intellectual Property, Data Protection, and Freedom of Information

Curated on Posted on by Stefaan Verhulst

Paper by Guido Noto La Diega: “Nowadays algorithms can decide if one can get a loan, is allowed to cross a border, or must go to prison. Artificial intelligence techniques (natural language processing and machine learning in the first place) enable private and public decision-makers to analyse big data in order to build profiles, which are used to make decisions in an automated way.

This work presents ten arguments against algorithmic decision-making. These revolve around the concepts of ubiquitous discretionary interpretation, holistic intuition, algorithmic bias, the three black boxes, psychology of conformity, power of sanctions, civilising force of hypocrisy, pluralism, empathy, and technocracy.

The lack of transparency of the algorithmic decision-making process does not stem merely from the characteristics of the relevant techniques used, which can make it impossible to access the rationale of the decision. It depends also on the abuse of and overlap between intellectual property rights (the “legal black box”). In the US, nearly half a million patented inventions concern algorithms; more than 67% of the algorithm-related patents were issued over the last ten years and the trend is increasing.

To counter the increased monopolisation of algorithms by means of intellectual property rights (with trade secrets leading the way), this paper presents three legal routes that enable citizens to ‘open’ the algorithms.

First, copyright and patent exceptions, as well as trade secrets are discussed.

Second, the GDPR is critically assessed. In principle, data controllers are not allowed to use algorithms to take decisions that have legal effects on the data subject’s life or similarly significantly affect them. However, when they are allowed to do so, the data subject still has the right to obtain human intervention, to express their point of view, as well as to contest the decision. Additionally, the data controller shall provide meaningful information about the logic involved in the algorithmic decision.

Third, this paper critically analyses the first known case of a court using the access right under the freedom of information regime to grant an injunction to release the source code of the computer program that implements an algorithm.

Only an integrated approach – which takes into account intellectual property, data protection, and freedom of information – may provide the citizen affected by an algorithmic decision of an effective remedy as required by the Charter of Fundamental Rights of the EU and the European Convention on Human Rights….(More)”.

Developing an impact framework for cultural change in government

Curated on Posted on by Stefaan Verhulst

Jesper Christiansen at Nesta: “Innovation teams and labs around the world are increasingly being tasked with building capacity and contributing to cultural change in government. There’s also an increasing recognition that we need to go beyond projects or single structures and make innovation become a part of the way governments operate more broadly.

However, there is a significant gap in our understanding of what “cultural change” or better “capacity” actually means.

At the same time, most innovation labs and teams are still being held to account in ways that don’t productively support this work. There is a lack of useful ways to measure outcomes, as opposed to outputs (for example, being asked to account for the number of workshops, rather than the increased capacity or impact that these workshops led to).

Consequently, we need a more developed awareness and understanding of what the signs of success look like, and what the intermediary outcomes (and measures) are in order to create a shift in accountability and better support ongoing capacity building….

One of the goals of States of Change, the collective we initiated last year to build this capability and culture, is to proactively address the common challenges that innovation practitioners face again and again. The field of public innovation is still emerging and evolving, and so our aim is to inspire action through practice-oriented, collaborative R&D activities and to develop the field based on practice rather than theory….(More)”.