Information to Action: Strengthening EPA Citizen Science Partnerships for Environmental Protection

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Report by the National Advisory Council for Environmental Policy and Technology: “Citizen science is catalyzing collaboration; new data and information brought about by greater public participation in environmental research are helping to drive a new era of environmental protection. As the body of citizen-generated data and information in the public realm continues to grow, EPA must develop a clear strategy to lead change and encourage action beyond the collection of data. EPA should recognize the variety of opportunities that it has to act as a conduit between the public and key partners, including state, territorial, tribal and local governments; nongovernmental organizations; and leading technology groups in the private sector. The Agency should build collaborations with new partners, identify opportunities to integrate equity into all relationships, and ensure that grassroots and community-based organizations are well supported and fairly resourced in funding strategies.

Key recommendations under this theme:

  • Recommendation 1. Catalyze action from citizen science data and information by providing guidance and leveraging collaboration.
  • Recommendation 2. Build inclusive and equitable partnerships by understanding partners’ diverse concerns and needs, including prioritizing better support for grassroots and community-based partnerships in EPA grantfunding strategies.

Increase state, territorial, tribal and local government engagement with citizen science

The Agency should reach out to tribes, states, territories and local governments throughout the country to understand the best practices and strategies for encouraging and incorporating citizen science in environmental protection. For states and territories looking for ways to engage in citizen science, EPA can help design strategies that recognize the community perspectives while building capacity in state and territorial governments. Recognizing the direct Executive Summary Information to Action: Strengthening EPA Citizen Science Partnerships for Environmental Protection connection between EPA and tribes, the Agency should seek tribal input and support tribes in using citizen science for environmental priorities. EPA should help to increase awareness for citizen science and where jurisdictional efforts already exist, assist in making citizen science accessible through local government agencies. EPA should more proactively listen to the voices of local stakeholders and encourage partners to embrace a vision for citizen science to accelerate the achievement of environmental goals. As part of this approach, EPA should find ways to define and communicate the Agency’s role as a resource in helping communities achieve environmental outcomes.

Key recommendations under this theme:

  • Recommendation 3. Provide EPA support and engage states and territories to better integrate citizen science into program goals.
  • Recommendation 4. Build on the unique strengths of EPA-tribal relationships.
  • Recommendation 5. Align EPA citizen science work to the priorities of local governments.

Leverage external organizations for expertise and project level support

Collaborations between communities and other external organizations—including educational institutions, civic organizations, and community-based organizations— are accelerating the growth of citizen science. Because EPA’s direct connection with members of the public often is limited, the Agency could benefit significantly by consulting with key external organizations to leverage citizen science efforts to provide the greatest benefit for the protection of human health and the environment. EPA should look to external organizations as vital connections to communities engaged in collaboratively led scientific investigation to address community-defined questions, referred to as community citizen science. External organizations can help EPA in assessing gaps in community-driven research and help the Agency to design effective support tools and best management practices for facilitating effective environmental citizen science programs….(More)”.

Most Maps of the New Ebola Outbreak Are Wrong

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Ed Kong in The Atlantic: “Almost all the maps of the outbreak zone that have thus far been released contain mistakes of this kind. Different health organizations all seem to use their own maps, most of which contain significant discrepancies. Things are roughly in the right place, but their exact positions can be off by miles, as can the boundaries between different regions.

Sinai, a cartographer at UCLA, has been working with the Ministry of Health to improve the accuracy of the Congo’s maps, and flew over on Saturday at their request. For each health zone within the outbreak region, Sinai compiled a list of the constituent villages, plotted them using the most up-to-date sources of geographical data, and drew boundaries that include these places and no others. The maps at the top of this piece show the before (left) and after (right) images….

Consider Bikoro, the health zone where the outbreak may have originated, and where most cases are found. Sinai took a list of all Bikoro’s villages, plotted them using the most up-to-date sources of geographical data, and drew a boundary that includes these places and no others. This new shape is roughly similar to the one on current maps, but with critical differences. Notably, existing maps have the village of Ikoko Impenge—one of the epicenters of the outbreak—outside the Bikoro health zone, when it actually lies within the zone.

 “These visualizations are important for communicating the reality on the ground to all levels of the health hierarchy, and to international partners who don’t know the country,” says Mathias Mossoko, the head of disease surveillance data in DRC.

“It’s really important for the outbreak response to have real and accurate data,” adds Bernice Selo, who leads the cartographic work from the Ministry of Health’s command center in Kinshasa. “You need to know exactly where the villages are, where the health facilities are, where the transport routes and waterways are. All of this helps you understand where the outbreak is, where it’s moving, how it’s moving. You can see which villages have the highest risk.”

To be clear, there’s no evidence that these problems are hampering the response to the current outbreak. It’s not like doctors are showing up in the middle of the forest, wondering why they’re in the wrong place. “Everyone on the ground knows where the health zones start and end,” says Sinai. “I don’t think this will make or break the response. But you surely want the most accurate data.”

It feels unusual to not have this information readily at hand, especially in an era when digital maps are so omnipresent and so supposedly truthful. If you search for San Francisco on Google Maps, you can be pretty sure that what comes up is actually where San Francisco is. On Google Street View, you can even walk along a beach at the other end of the world….(More)”.

But the Congo is a massive country—a quarter the size of the United States with considerably fewer resources. Until very recently, they haven’t had the resources to get accurate geolocalized data. Instead, the boundaries of the health zones and their constituent “health areas,” as well as the position of specific villages, towns, rivers, hospitals, clinics, and other landmarks, are often based on local knowledge and hand-drawn maps. Here’s an example, which I saw when I visited the National Institute for Biomedical Research in March. It does the job, but it’s clearly not to scale.

This is your office on AI

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Article by Jeffrey Brown at a Special Issue of the Wilson Quarterly on AI: “The future has arrived and it’s your first day at your new job. You step across the threshold sporting a nervous smile and harboring visions of virtual handshakes and brain-computer interfaces. After all, this is one of those newfangled, modern offices that science-fiction writers have been dreaming up for ages. Then you bump up against something with a thud. No, it’s not one of the ubiquitous glass walls, but the harsh reality of an office that, at first glance, doesn’t appear much different from what you’re accustomed to. Your new colleagues shuffle between meetings clutching phones and laptops. A kitchenette stocked with stale donuts lurks in the background. And, by the way, you were fifteen minutes late because the commute is still hell.

So where is the fabled “office of the future”? After all, many of us have only ever fantasized about the ways in which technology – and especially artificial intelligence – might transform our working lives for the better. In fact, the AI-enabled office will usher in far more than next-generation desk supplies. It’s only over subsequent weeks that you come to appreciate how the office of the future feels, operates, and yes, senses. It also slowly dawns on you that work itself has changed and that what it means to be a worker has undergone a similar retrofit.

With AI already deployed in everything from the fight against ISIS to the hunt for exoplanets and your cat’s Alexa-enabled Friskies order, its application to the office should come as no surprise. As workers pretty much everywhere can attest, today’s office has issues: It can’t intuitively crack a window when your officemate decides to microwave leftover catfish. It seems to willfully disregard your noise, temperature, light, and workflow preferences. And it certainly doesn’t tell its designers – or your manager – what you are really thinking as you plop down in your annoyingly stiff chair to sip your morning cup of mud.

Now, you may be thinking to yourself, “These seem like trivial issues that can be worked out simply by chatting with another human being, so why do we even need AI in my office?” If so, read on. In your lifetime, companies and workers will channel AI to unlock new value – and immense competitive advantage….(More)”.

Conversations Gone Awry: Detecting Early Signs of Conversational Failure

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Paper by Justine Zhang et al: “One of the main challenges online social systems face is the prevalence of antisocial behavior, such as harassment and personal attacks. In this work, we introduce the task of predicting from the very start of a conversation whether it will get out of hand. As opposed to detecting undesirable behavior after the fact, this task aims to enable early, actionable prediction at a time when the conversation might still be salvaged.
To this end, we develop a framework for capturing pragmatic devices—such as politeness strategies and rhetorical prompts—used to start a conversation, and analyze their relation to its future trajectory. Applying this framework in a controlled setting, we demonstrate the feasibility of detecting early warning signs of antisocial behavior in online discussions…. (More)”.

CrowdLaw Manifesto

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At the Rockefeller Foundation Bellagio Center this spring, assembled participants  met to discuss CrowdLaw, namely how to use technology to improve the quality and effectiveness of law and policymaking through greater public engagement. We put together and signed 12 principles to promote the use of CrowdLaw by local legislatures and national parliaments, calling for legislatures, technologists and the public to participate in creating more open and participatory lawmaking practices. We invite you to sign the Manifesto using the form below.

Draft dated May 29, 2018

  1. To improve public trust in democratic institutions, we must improve how we govern in the 21st century.
  2. CrowdLaw is any law, policy-making or public decision-making that offers a meaningful opportunity for the public to participate in one or multiples stages of decision-making, including but not limited to the processes of problem identification, solution identification, proposal drafting, ratification, implementation or evaluation.
  3. CrowdLaw draws on innovative processes and technologies and encompasses diverse forms of engagement among elected representatives, public officials, and those they represent.
  4. When designed well, CrowdLaw may help governing institutions obtain more relevant facts and knowledge as well as more diverse perspectives, opinions and ideas to inform governing at each stage and may help the public exercise political will.
  5. When designed well, CrowdLaw may help democratic institutions build trust and the public to play a more active role in their communities and strengthen both active citizenship and democratic culture.
  6. When designed well, CrowdLaw may enable engagement that is thoughtful, inclusive, informed but also efficient, manageable and sustainable.
  7. Therefore, governing institutions at every level should experiment and iterate with CrowdLaw initiatives in order to create formal processes for diverse members of society to participate in order to improve the legitimacy of decision-making, strengthen public trust and produce better outcomes.
  8. Governing institutions at every level should encourage research and learning about CrowdLaw and its impact on individuals, on institutions and on society.
  9. The public also has a responsibility to improve our democracy by demanding and creating opportunities to engage and then actively contributing expertise, experience, data and opinions.
  10. Technologists should work collaboratively across disciplines to develop, evaluate and iterate varied, ethical and secure CrowdLaw platforms and tools, keeping in mind that different participation mechanisms will achieve different goals.
  11. Governing institutions at every level should encourage collaboration across organizations and sectors to test what works and share good practices.
  12. Governing institutions at every level should create the legal and regulatory frameworks necessary to promote CrowdLaw and better forms of public engagement and usher in a new era of more open, participatory and effective governing.

The CrowdLaw Manifesto has been signed by the following individuals and organizations:

Individuals

  • Victoria Alsina, Senior Fellow at The GovLab and Faculty Associate at Harvard Kennedy School, Harvard University
  • Marta Poblet Balcell , Associate Professor, RMIT University
  • Robert Bjarnason — President & Co-founder, Citizens Foundation; Better Reykjavik
  • Pablo Collada — Former Executive Director, Fundación Ciudadano Inteligente
  • Mukelani Dimba — Co-chair, Open Government Partnership
  • Hélène Landemore, Associate Professor of Political Science, Yale University
  • Shu-Yang Lin, re:architect & co-founder, PDIS.tw
  • José Luis Martí , Vice-Rector for Innovation and Professor of Legal Philosophy, Pompeu Fabra University
  • Jessica Musila — Executive Director, Mzalendo
  • Sabine Romon — Chief Smart City Officer — General Secretariat, Paris City Council
  • Cristiano Ferri Faría — Director, Hacker Lab, Brazilian House of Representatives
  • Nicola Forster — President and Founder, Swiss Forum on Foreign Policy
  • Raffaele Lillo — Chief Data Officer, Digital Transformation Team, Government of Italy
  • Tarik Nesh-Nash — CEO & Co-founder, GovRight; Ashoka Fellow
  • Beth Simone Noveck, Director, The GovLab and Professor at New York University Tandon School of Engineering
  • Ehud Shapiro , Professor of Computer Science and Biology, Weizmann Institute of Science

Organizations

  • Citizens Foundation, Iceland
  • Fundación Ciudadano Inteligente, Chile
  • International School for Transparency, South Africa
  • Mzalendo, Kenya
  • Smart Cities, Paris City Council, Paris, France
  • Hacker Lab, Brazilian House of Representatives, Brazil
  • Swiss Forum on Foreign Policy, Switzerland
  • Digital Transformation Team, Government of Italy, Italy
  • The Governance Lab, New York, United States
  • GovRight, Morocco
  • ICT4Dev, Morocco

AI trust and AI fears: A media debate that could divide society

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Article by Vyacheslav Polonski: “Unless you live under a rock, you probably have been inundated with recent news on machine learning and artificial intelligence (AI). With all the recent breakthroughs, it almost seems like AI can already predict the future. Police forces are using it to map when and where crime is likely to occur. Doctors can use it to predict when a patient is most likely to have a heart attack or stroke. Researchers are even trying to give AI imagination so it can plan for unexpected consequences.

Of course, many decisions in our lives require a good forecast, and AI agents are almost always better at forecasting than their human counterparts. Yet for all these technological advances, we still seem to deeply lack confidence in AI predictions. Recent cases show that people don’t like relying on AI and prefer to trust human experts, even if these experts are wrong.

If we want AI to really benefit people, we need to find a way to get people to trust it. To do that, we need to understand why people are so reluctant to trust AI in the first place….

Many people are also simply not familiar with many instances of AI actually working, because it often happens in the background. Instead, they are acutely aware of instances where AI goes terribly wrong:

These unfortunate examples have received a disproportionate amount of media attention, emphasising the message that humans cannot always rely on technology. In the end, it all goes back to the simple truth that machine learning is not foolproof, in part because the humans who design it aren’t….

Fortunately we already have some ideas about how to improve trust in AI — there’s light at the end of the tunnel.

  1. Experience: One solution may be to provide more hands-on experiences with automation apps and other AI applications in everyday situations (like this robot that can get you a beer from the fridge). Thus, instead of presenting the Sony’s new robot dog Aibo as an exclusive product for the upper-class, we’d recommend making these kinds of innovations more accessible to the masses. Simply having previous experience with AI can significantly improve people’s attitudes towards the technology, as we found in our experimental study. And this is especially important for the general public that may not have a very sophisticated understanding of the technology. Similar evidence also suggests the more you use other technologies such as the Internet, the more you trust them.
  2. Insight: Another solution may be to open the “black-box” of machine learning algorithms and be slightly more transparent about how they work. Companies such as Google, Airbnb and Twitter already release transparency reports on a regular basis. These reports provide information about government requests and surveillance disclosures. A similar practice for AI systems could help people have a better understanding of how algorithmic decisions are made. Therefore, providing people with a top-level understanding of machine learning systems could go a long way towards alleviating algorithmic aversion.
  3. Control: Lastly, creating more of a collaborative decision-making process will help build trust and allow the AI to learn from human experience. In our work at Avantgarde Analytics, we have also found that involving people more in the AI decision-making process could improve trust and transparency. In a similar vein, a group of researchers at the University of Pennsylvania recently found that giving people control over algorithms can help create more trust in AI predictions. Volunteers in their study who were given the freedom to slightly modify an algorithm felt more satisfied with it, more likely to believe it was superior and more likely to use in in the future.

These guidelines (experience, insight and control) could help making AI systems more transparent and comprehensible to the individuals affected by their decisions….(More)”.

Open data work: understanding open data usage from a practice lens

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Paper by Emma Ruijer in the International Review of Administrative Sciences: “During recent years, the amount of data released on platforms by public administrations around the world have exploded. Open government data platforms are aimed at enhancing transparency and participation. Even though the promises of these platforms are high, their full potential has not yet been reached. Scholars have identified technical and quality barriers of open data usage. Although useful, these issues fail to acknowledge that the meaning of open data also depends on the context and people involved. In this study we analyze open data usage from a practice lens – as a social construction that emerges over time in interaction with governments and users in a specific context – to enhance our understanding of the role of context and agency in the development of open data platforms. This study is based on innovative action-based research in which civil servants’ and citizens’ initiatives collaborate to find solutions for public problems using an open data platform. It provides an insider perspective of Open Data Work. The findings show that an absence of a shared cognitive framework for understanding open data and a lack of high-quality datasets can prevent processes of collaborative learning. Our contextual approach stresses the need for open data practices that work on the basis of rich interactions with users rather than government-centric implementations….(More)”.

Crowdbreaks: Tracking Health Trends using Public Social Media Data and Crowdsourcing

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Paper by Martin Mueller and Marcel Salath: “In the past decade, tracking health trends using social media data has shown great promise, due to a powerful combination of massive adoption of social media around the world, and increasingly potent hardware and software that enables us to work with these new big data streams.

At the same time, many challenging problems have been identified. First, there is often a mismatch between how rapidly online data can change, and how rapidly algorithms are updated, which means that there is limited reusability for algorithms trained on past data as their performance decreases over time. Second, much of the work is focusing on specific issues during a specific past period in time, even though public health institutions would need flexible tools to assess multiple evolving situations in real time. Third, most tools providing such capabilities are proprietary systems with little algorithmic or data transparency, and thus little buy-in from the global public health and research community.

Here, we introduce Crowdbreaks, an open platform which allows tracking of health trends by making use of continuous crowdsourced labelling of public social media content. The system is built in a way which automatizes the typical workflow from data collection, filtering, labelling and training of machine learning classifiers and therefore can greatly accelerate the research process in the public health domain. This work introduces the technical aspects of the platform and explores its future use cases…(More)”.

Superminds: The Surprising Power of People and Computers Thinking Together

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Book by Thomas W. Malone: “If you’re like most people, you probably believe that humans are the most intelligent animals on our planet. But there’s another kind of entity that can be far smarter: groups of people. In this groundbreaking book, Thomas Malone, the founding director of the MIT Center for Collective Intelligence, shows how groups of people working together in superminds — like hierarchies, markets, democracies, and communities — have been responsible for almost all human achievements in business, government, science, and beyond. And these collectively intelligent human groups are about to get much smarter.

Using dozens of striking examples and case studies, Malone shows how computers can help create more intelligent superminds not just with artificial intelligence, but perhaps even more importantly with hyperconnectivity:  connecting humans to one another at massive scales and in rich new ways. Together, these changes will have far-reaching implications for everything from the way we buy groceries and plan business strategies to how we respond to climate change, and even for democracy itself. By understanding how these collectively intelligent groups work, we can learn how to harness their genius to achieve our human goals….(More)”.

The Future of Fishing Is Big Data and Artificial Intelligence

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Meg Wilcox at Civil Eats: “New England’s groundfish season is in full swing, as hundreds of dayboat fishermen from Rhode Island to Maine take to the water in search of the region’s iconic cod and haddock. But this year, several dozen of them are hauling in their catch under the watchful eye of video cameras as part of a new effort to use technology to better sustain the area’s fisheries and the communities that depend on them.

Video observation on fishing boats—electronic monitoring—is picking up steam in the Northeast and nationally as a cost-effective means to ensure that fishing vessels aren’t catching more fish than allowed while informing local fisheries management. While several issues remain to be solved before the technology can be widely deployed—such as the costs of reviewing and storing data—electronic monitoring is beginning to deliver on its potential to lower fishermen’s costs, provide scientists with better data, restore trust where it’s broken, and ultimately help consumers gain a greater understanding of where their seafood is coming from….

Muto’s vessel was outfitted with cameras, at a cost of about $8,000, through a collaborative venture between NOAA’s regional office and science center, The Nature Conservancy (TNC), the Gulf of Maine Research Institute, and the Cape Cod Commercial Fishermen’s Alliance. Camera costs are currently subsidized by NOAA Fisheries and its partners.

The cameras run the entire time Muto and his crew are out on the water. They record how the fisherman handle their discards, the fish they’re not allowed to keep because of size or species type, but that count towards their quotas. The cost is lower than what he’d pay for an in-person monitor.The biggest cost of electronic monitoring, however, is the labor required to review the video. …

Another way to cut costs is to use computers to review the footage. McGuire says there’s been a lot of talk about automating the review, but the common refrain is that it’s still five years off.

To spur faster action, TNC last year spearheaded an online competition, offering a $50,000 prize to computer scientists who could crack the code—that is, teach a computer how to count fish, size them, and identify their species.

“We created an arms race,” says McGuire. “That’s why you do a competition. You’ll never get the top minds to do this because they don’t care about your fish. They all want to work for Google, and one way to get recognized by Google is to win a few of these competitions.”The contest exceeded McGuire’s expectations. “Winners got close to 100 percent in count and 75 percent accurate on identifying species,” he says. “We proved that automated review is now. Not in five years. And now all of the video-review companies are investing in machine leaning.” It’s only a matter of time before a commercial product is available, McGuire believes….(More).