Lessons from DataRescue: The Limits of Grassroots Climate Change Data Preservation and the Need for Federal Records Law Reform

Curated on Posted on by Stefaan Verhulst

Essay by Sarah Lamdan at the University of Pennsylvania Law Review: “Shortly after Donald Trump’s victory in the 2016 Presidential election, but before his inauguration, a group of concerned scholars organized in cities and college campuses across the United States, starting with the University of Pennsylvania, to prevent climate change data from disappearing from government websites. The move was led by Michelle Murphy, a scholar who had previously observed the destruction of climate change data and muzzling of government employees in Canadian Prime Minister Stephen Harper’s administration. The “guerrilla archiving” project soon swept the nation, drawing media attention as its volunteers scraped and preserved terabytes of climate change and other environmental data and materials from .gov websites. The archiving project felt urgent and necessary, as the federal government is the largest collector and archive of U.S. environmental data and information.

As it progressed, the guerrilla archiving movement became more defined: two organizations developed, the DataRefuge at the University of Pennsylvania, and the Environmental Data & Governance Initiative (EDGI), which was a national collection of academics and non-profits. These groups co-hosted data gathering sessions called DataRescue events. I joined EDGI to help members work through administrative law concepts and file Freedom of Information Act (FOIA) requests. The day-long archiving events were immensely popular and widely covered by media outlets. Each weekend, hundreds of volunteers would gather to participate in DataRescue events in U.S. cities. I helped organize the New York DataRescue event, which was held less than a month after the initial event in Pennsylvania. We had to turn people away as hundreds of local volunteers lined up to help and dozens more arrived in buses and cars, exceeding the space constraints of NYU’s cavernous MakerSpace engineering facility. Despite the popularity of the project, however, DataRescue’s goals seemed far-fetched: how could thousands of private citizens learn the contours of multitudes of federal environmental information warehouses, gather the data from all of them, and then re-post the materials in a publicly accessible format?…(More)”.

Use our personal data for the common good

Curated on Posted on by Stefaan Verhulst

Hetan Shah at Nature: “Data science brings enormous potential for good — for example, to improve the delivery of public services, and even to track and fight modern slavery. No wonder researchers around the world — including members of my own organization, the Royal Statistical Society in London — have had their heads in their hands over headlines about how Facebook and the data-analytics company Cambridge Analytica might have handled personal data. We know that trustworthiness underpins public support for data innovation, and we have just seen what happens when that trust is lost….But how else might we ensure the use of data for the public good rather than for purely private gain?

Here are two proposals towards this goal.

First, governments should pass legislation to allow national statistical offices to gain anonymized access to large private-sector data sets under openly specified conditions. This provision was part of the United Kingdom’s Digital Economy Act last year and will improve the ability of the UK Office for National Statistics to assess the economy and society for the public interest.

My second proposal is inspired by the legacy of John Sulston, who died earlier this month. Sulston was known for his success in advocating for the Human Genome Project to be openly accessible to the science community, while a competitor sought to sequence the genome first and keep data proprietary.

Like Sulston, we should look for ways of making data available for the common interest. Intellectual-property rights expire after a fixed time period: what if, similarly, technology companies were allowed to use the data that they gather only for a limited period, say, five years? The data could then revert to a national charitable corporation that could provide access to certified researchers, who would both be held to account and be subject to scrutiny that ensure the data are used for the common good.

Technology companies would move from being data owners to becoming data stewards…(More)” (see also http://datacollaboratives.org/).

Leveraging the Power of Bots for Civil Society

Curated on Posted on by Stefaan Verhulst

Allison Fine & Beth Kanter  at the Stanford Social Innovation Review: “Our work in technology has always centered around making sure that people are empowered, healthy, and feel heard in the networks within which they live and work. The arrival of the bots changes this equation. It’s not enough to make sure that people are heard; we now have to make sure that technology adds value to human interactions, rather than replacing them or steering social good in the wrong direction. If technology creates value in a human-centered way, then we will have more time to be people-centric.

So before the bots become involved with almost every facet of our lives, it is incumbent upon those of us in the nonprofit and social-change sectors to start a discussion on how we both hold on to and lead with our humanity, as opposed to allowing the bots to lead. We are unprepared for this moment, and it does not feel like an understatement to say that the future of humanity relies on our ability to make sure we’re in charge of the bots, not the other way around.

To Bot or Not to Bot?

History shows us that bots can be used in positive ways. Early adopter nonprofits have used bots to automate civic engagement, such as helping citizens register to vote, contact their elected officials, and elevate marginalized voices and issues. And nonprofits are beginning to use online conversational interfaces like Alexa for social good engagement. For example, the Audubon Society has released an Alexa skill to teach bird calls.

And for over a decade, Invisible People founder Mark Horvath has been providing “virtual case management” to homeless people who reach out to him through social media. Horvath says homeless agencies can use chat bots programmed to deliver basic information to people in need, and thus help them connect with services. This reduces the workload for case managers while making data entry more efficient. He explains it working like an airline reservation: The homeless person completes the “paperwork” for services by interacting with a bot and then later shows their ID at the agency. Bots can greatly reduce the need for a homeless person to wait long hours to get needed services. Certainly this is a much more compassionate use of bots than robot security guards who harass homeless people sleeping in front of a business.

But there are also examples where a bot’s usefulness seems limited. A UK-based social service charity, Mencap, which provides support and services to children with learning disabilities and their parents, has a chatbot on its website as part of a public education effort called #HereIAm. The campaign is intended to help people understand more about what it’s like having a learning disability, through the experience of a “learning disabled” chatbot named Aeren. However, this bot can only answer questions, not ask them, and it doesn’t become smarter through human interaction. Is this the best way for people to understand the nature of being learning disabled? Is it making the difficulties feel more or less real for the inquirers? It is clear Mencap thinks the interaction is valuable, as they reported a 3 percent increase in awareness of their charity….

The following discussion questions are the start of conversations we need to have within our organizations and as a sector on the ethical use of bots for social good:

  • What parts of our work will benefit from greater efficiency without reducing the humanness of our efforts? (“Humanness” meaning the power and opportunity for people to learn from and help one another.)
  • Do we have a privacy policy for the use and sharing of data collected through automation? Does the policy emphasize protecting the data of end users? Is the policy easily accessible by the public?
  • Do we make it clear to the people using the bot when they are interacting with a bot?
  • Do we regularly include clients, customers, and end users as advisors when developing programs and services that use bots for delivery?
  • Should bots designed for service delivery also have fundraising capabilities? If so, can we ensure that our donors are not emotionally coerced into giving more than they want to?
  • In order to truly understand our clients’ needs, motivations, and desires, have we designed our bots’ conversational interactions with empathy and compassion, or involved social workers in the design process?
  • Have we planned for weekly checks of the data generated by the bots to ensure that we are staying true to our values and original intentions, as AI helps them learn?….(More)”.

Smart cities need thick data, not big data

Curated on Posted on by Stefaan Verhulst

Adrian Smith at The Guardian: “…The Smart City is an alluring prospect for many city leaders. Even if you haven’t heard of it, you may have already joined in by looking up bus movements on your phone, accessing Council services online or learning about air contamination levels. By inserting sensors across city infrastructures and creating new data sources – including citizens via their mobile devices – Smart City managers can apply Big Data analysis to monitor and anticipate urban phenomena in new ways, and, so the argument goes, efficiently manage urban activity for the benefit of ‘smart citizens’.

Barcelona has been a pioneering Smart City. The Council’s business partners have been installing sensors and opening data platforms for years. Not everyone is comfortable with this technocratic turn. After Ada Colau was elected Mayor on a mandate of democratising the city and putting citizens centre-stage, digital policy has sought to go ‘beyond the Smart City’. Chief Technology Officer Francesca Bria is opening digital platforms to greater citizen participation and oversight. Worried that the city’s knowledge was being ceded to tech vendors, the Council now promotes technological sovereignty.

On the surface, the noise project in Plaça del Sol is an example of such sovereignty. It even features in Council presentations. Look more deeply, however, and it becomes apparent that neighbourhood activists are really appropriating new technologies into the old-fashioned politics of community development….

What made Plaça del Sol stand out can be traced to a group of technology activists who got in touch with residents early in 2017. The activists were seeking participants in their project called Making Sense, which sought to resurrect a struggling ‘Smart Citizen Kit’ for environmental monitoring. The idea was to provide residents with the tools to measure noise levels, compare them with officially permissible levels, and reduce noise in the square. More than 40 neighbours signed up and installed 25 sensors on balconies and inside apartments.

The neighbours had what project coordinator Mara Balestrini from Ideas for Change calls ‘a matter of concern’. The earlier Smart Citizen Kit had begun as a technological solution looking for a problem: a crowd-funded gadget for measuring pollution, whose data users could upload to a web-platform for comparison with information from other users. Early adopters found the technology trickier to install than developers had presumed. Even successful users stopped monitoring because there was little community purpose. A new approach was needed. Noise in Plaça del Sol provided a problem for this technology fix….

Anthropologist Clifford Geertz argued many years ago that situations can only be made meaningful through ‘thick description’. Applied to the Smart City, this means data cannot really be explained and used without understanding the contexts in which it arises and gets used. Data can only mobilise people and change things when it becomes thick with social meaning….(More)”

Online gamers control trash collecting water robot

Curated on Posted on by Stefaan Verhulst

Springwise: “Urban Rivers is a Chicago-based charity focused on cleaning up the city’s rivers and re-wilding bankside habitats. One of their most visible pieces of work is a floating habitat installed in the middle of the river that runs through the city. An immediate problem that arose after installation was the accumulation of trash. At first, the company sent someone out on a kayak every other day to clean the habitat. Yet in less than a day, huge amounts of garbage would again be choking the space. The company’s solution was to create a Trash Task Force. The outcome of the Task Force’s work is the TrashBot, a remote-controlled garbage-collecting robot. The TrashBot allows gamers all over the world to do their bit in cleaning up Chicago’s river.

Anyone interested in playing the cleaning game can sign up via the Urban River website. Future development of the bot will likely focus on wildlife monitoring. Similarly, the end goal of the game will be that no one wants to play because there is no more garbage for collection.

From crowdsourced ocean data gathered by the fins of surfers’ boards to a solar-powered autonomous drone that gathers waste from harbor waters, the health of the world’s waterways is being improved in a number of ways. The surfboard fins use sensors to monitor sea salinity, acidity levels and wave motion. Those are all important coastal ecosystem factors that could be affected by climate change. The water drones are intelligent and use on-board cameras and sensors to learn about their environment and avoid other craft as they collect garbage from rivers, canals and harbors….(More)”.

Obfuscating with transparency

Curated on Posted on by Stefaan Verhulst

“These approaches…limit the impact of valuable information in developing policies…”

Under the new policy, studies that do not fully meet transparency criteria would be excluded from use in EPA policy development. This proposal follows unsuccessful attempts to enact the Honest and Open New EPA Science Treatment (HONEST) Act and its predecessor, the Secret Science Reform Act. These approaches undervalue many scientific publications and limit the impact of valuable information in developing policies in the areas that the EPA regulates….In developing effective policies, earnest evaluations of facts and fair-minded assessments of the associated uncertainties are foundational. Policy discussions require an assessment of the likelihood that a particular observation is true and examinations of the short- and long-term consequences of potential actions or inactions, including a wide range of different sorts of costs. Those with training in making these judgments with access to as much relevant information as possible are crucial for this process. Of course, policy development requires considerations other than those related to science. Such discussions should follow clear assessment after access to all of the available evidence. The scientific enterprise should stand up against efforts that distort initiatives aimed to improve scientific practice, just to pursue other agendas…(More)”.

What if a nuke goes off in Washington, D.C.? Simulations of artificial societies help planners cope with the unthinkable

Curated on Posted on by Stefaan Verhulst

Mitchell Waldrop at Science: “…The point of such models is to avoid describing human affairs from the top down with fixed equations, as is traditionally done in such fields as economics and epidemiology. Instead, outcomes such as a financial crash or the spread of a disease emerge from the bottom up, through the interactions of many individuals, leading to a real-world richness and spontaneity that is otherwise hard to simulate.

That kind of detail is exactly what emergency managers need, says Christopher Barrett, a computer scientist who directs the Biocomplexity Institute at Virginia Polytechnic Institute and State University (Virginia Tech) in Blacksburg, which developed the NPS1 model for the government. The NPS1 model can warn managers, for example, that a power failure at point X might well lead to a surprise traffic jam at point Y. If they decide to deploy mobile cell towers in the early hours of the crisis to restore communications, NPS1 can tell them whether more civilians will take to the roads, or fewer. “Agent-based models are how you get all these pieces sorted out and look at the interactions,” Barrett says.

The downside is that models like NPS1 tend to be big—each of the model’s initial runs kept a 500-microprocessor computing cluster busy for a day and a half—forcing the agents to be relatively simple-minded. “There’s a fundamental trade-off between the complexity of individual agents and the size of the simulation,” says Jonathan Pfautz, who funds agent-based modeling of social behavior as a program manager at the Defense Advanced Research Projects Agency in Arlington, Virginia.

But computers keep getting bigger and more powerful, as do the data sets used to populate and calibrate the models. In fields as diverse as economics, transportation, public health, and urban planning, more and more decision-makers are taking agent-based models seriously. “They’re the most flexible and detailed models out there,” says Ira Longini, who models epidemics at the University of Florida in Gainesville, “which makes them by far the most effective in understanding and directing policy.”

he roots of agent-based modeling go back at least to the 1940s, when computer pioneers such as Alan Turing experimented with locally interacting bits of software to model complex behavior in physics and biology. But the current wave of development didn’t get underway until the mid-1990s….(More)”.

Modernizing Crime Statistics: New Systems for Measuring Crime

Curated on Posted on by Stefaan Verhulst

(Second) Report by the National Academies of Sciences, Engineering, and Medicine: “To derive statistics about crime – to estimate its levels and trends, assess its costs to and impacts on society, and inform law enforcement approaches to prevent it – a conceptual framework for defining and thinking about crime is virtually a prerequisite. Developing and maintaining such a framework is no easy task, because the mechanics of crime are ever evolving and shifting: tied to shifts and development in technology, society, and legislation.

Interest in understanding crime surged in the 1920s, which proved to be a pivotal decade for the collection of nationwide crime statistics. Now established as a permanent agency, the Census Bureau commissioned the drafting of a manual for preparing crime statistics—intended for use by the police, corrections departments, and courts alike. The new manual sought to solve a perennial problem by suggesting a standard taxonomy of crime. Shortly after the Census Bureau issued its manual, the International Association of Chiefs of Police in convention adopted a resolution to create a Committee on Uniform Crime Records —to begin the process of describing what a national system of data on crimes known to the police might look like.

Report 1 performed a comprehensive reassessment of what is meant by crime in U.S. crime statistics and recommends a new classification of crime to organize measurement efforts. This second report examines methodological and implementation issues and presents a conceptual blueprint for modernizing crime statistics….(More)”.

UK can lead the way on ethical AI, says Lords Committee

Curated on Posted on by Stefaan Verhulst

Lords Select Committee: “The UK is in a strong position to be a world leader in the development of artificial intelligence (AI). This position, coupled with the wider adoption of AI, could deliver a major boost to the economy for years to come. The best way to do this is to put ethics at the centre of AI’s development and use concludes a report by the House of Lords Select Committee on Artificial Intelligence, AI in the UK: ready, willing and able?, published today….

One of the recommendations of the report is for a cross-sector AI Code to be established, which can be adopted nationally, and internationally. The Committee’s suggested five principles for such a code are:

  1. Artificial intelligence should be developed for the common good and benefit of humanity.
  2. Artificial intelligence should operate on principles of intelligibility and fairness.
  3. Artificial intelligence should not be used to diminish the data rights or privacy of individuals, families or communities.
  4. All citizens should have the right to be educated to enable them to flourish mentally, emotionally and economically alongside artificial intelligence.
  5. The autonomous power to hurt, destroy or deceive human beings should never be vested in artificial intelligence.

Other conclusions from the report include:

  • Many jobs will be enhanced by AI, many will disappear and many new, as yet unknown jobs, will be created. Significant Government investment in skills and training will be necessary to mitigate the negative effects of AI. Retraining will become a lifelong necessity.
  • Individuals need to be able to have greater personal control over their data, and the way in which it is used. The ways in which data is gathered and accessed needs to change, so that everyone can have fair and reasonable access to data, while citizens and consumers can protect their privacy and personal agency. This means using established concepts, such as open data, ethics advisory boards and data protection legislation, and developing new frameworks and mechanisms, such as data portability and data trusts.
  • The monopolisation of data by big technology companies must be avoided, and greater competition is required. The Government, with the Competition and Markets Authority, must review the use of data by large technology companies operating in the UK.
  • The prejudices of the past must not be unwittingly built into automated systems. The Government should incentivise the development of new approaches to the auditing of datasets used in AI, and also to encourage greater diversity in the training and recruitment of AI specialists.
  • Transparency in AI is needed. The industry, through the AI Council, should establish a voluntary mechanism to inform consumers when AI is being used to make significant or sensitive decisions.
  • At earlier stages of education, children need to be adequately prepared for working with, and using, AI. The ethical design and use of AI should become an integral part of the curriculum.
  • The Government should be bold and use targeted procurement to provide a boost to AI development and deployment. It could encourage the development of solutions to public policy challenges through speculative investment. There have been impressive advances in AI for healthcare, which the NHS should capitalise on.
  • It is not currently clear whether existing liability law will be sufficient when AI systems malfunction or cause harm to users, and clarity in this area is needed. The Committee recommend that the Law Commission investigate this issue.
  • The Government needs to draw up a national policy framework, in lockstep with the Industrial Strategy, to ensure the coordination and successful delivery of AI policy in the UK….(More)”.

From Texts to Tweets to Satellites: The Power of Big Data to Fill Gender Data Gaps

Curated on Posted on by Stefaan Verhulst

 at UN Foundation Blog: “Twitter posts, credit card purchases, phone calls, and satellites are all part of our day-to-day digital landscape.

Detailed data, known broadly as “big data” because of the massive amounts of passively collected and high-frequency information that such interactions generate, are produced every time we use one of these technologies. These digital traces have great potential and have already developed a track record for application in global development and humanitarian response.

Data2X has focused particularly on what big data can tell us about the lives of women and girls in resource-poor settings. Our research, released today in a new report, Big Data and the Well-Being of Women and Girls, demonstrates how four big data sources can be harnessed to fill gender data gaps and inform policy aimed at mitigating global gender inequality. Big data can complement traditional surveys and other data sources, offering a glimpse into dimensions of girls’ and women’s lives that have otherwise been overlooked and providing a level of precision and timeliness that policymakers need to make actionable decisions.

Here are three findings from our report that underscore the power and potential offered by big data to fill gender data gaps:

  1. Social media data can improve understanding of the mental health of girls and women.

Mental health conditions, from anxiety to depression, are thought to be significant contributors to the global burden of disease, particularly for young women, though precise data on mental health is sparse in most countries. However, research by Georgia Tech University, commissioned by Data2X, finds that social media provides an accurate barometer of mental health status…..

  1. Cell phone and credit card records can illustrate women’s economic and social patterns – and track impacts of shocks in the economy.

Our spending priorities and social habits often indicate economic status, and these activities can also expose economic disparities between women and men.

By compiling cell phone and credit card records, our research partners at MIT traced patterns of women’s expenditures, spending priorities, and physical mobility. The research found that women have less mobility diversity than men, live further away from city centers, and report less total expenditure per capita…..

  1. Satellite imagery can map rivers and roads, but it can also measure gender inequality.

Satellite imagery has the power to capture high-resolution, real-time data on everything from natural landscape features, like vegetation and river flows, to human infrastructure, like roads and schools. Research by our partners at the Flowminder Foundation finds that it is also able to measure gender inequality….(More)”.