Citizen science volunteers driven by desire to learn

Curated on Posted on by Stefaan Verhulst

UoP News: “People who give up their time for online volunteering are mainly motivated by a desire to learn, a new study has found.

The research surveyed volunteers on ‘citizen science’ projects and suggests that this type of volunteering could be used to increase general knowledge of science within society.

The study, led by Dr Joe Cox from the Department of Economics and Finance, discovered that an appetite to learn more about the subject was the number one driver for online volunteers, followed by being part of a community. It also revealed that many volunteers are motivated by a desire for escapism.

Online volunteering and crowdsourcing projects typically involve input from large numbers of contributors working individually but towards a common goal. This study surveyed 2000 people who volunteer for ‘citizen science’ projects hosted by Zooniverse, a collection of research projects that rely on volunteers to help scientists with the challenge of interpreting massive amounts of data….“What was interesting was that characteristics such as age, gender and level of education had no correlation with the amount of time people give up and the length of time they stay on a project. These participants were relatively highly educated compared with the rest of the population, but those with the highest levels of education do not appear to contribute the most effort and information towards these projects.”

The study noticed pronounced changes in how people are motivated at different stages of the volunteer process. While a desire to learn is the most important motivation among contributors at the early stages, the opportunities for social interaction and escapism become more important motivations at later stages….

He suggests that online volunteering and citizen science projects could incentivise participation by offering clearly defined opportunities for learning, while representing an effective way of increasing scientific literacy and knowledge within society….(More)”.

Elsevier Is Becoming a Data Company. Should Universities Be Wary?

Curated on Posted on by Stefaan Verhulst

Paul Basken at The Chronicle of Higher Education: “As universities have slowly pushed their scientists to embrace open-access journals, publishers will need new profit centers. Elsevier appears well ahead of the pack in creating a network of products that scientists can use to record, share, store, and measure the value to others of the surging amounts of data they produce.

“Maybe all publishers are going, or wish they were” going, in the direction of becoming data companies, said Vincent Larivière, an associate professor of information science at the University of Montreal. “But Elsevier is the only one that is there.”

A Suite of Services

Universities also recognize the future of data. Their scientists are already seeing that widely and efficiently sharing data in fields such as cancer research has enabled accomplishments that have demonstrably saved lives.

In their eagerness to embrace that future, however, universities may not be paying enough attention to what their choices of systems may eventually cost them, warned Roger C. Schonfeld, a program director at Ithaka S+R. With its comprehensive data-services network, Mr. Schonfeld wrote earlier this year, Elsevier appears ready “to lock in scientists to a research workflow no less powerful than the strength of the lock-in libraries have felt to ‘big deal’ bundles.”….

Some open-access advocates say the situation points to an urgent need to create more robust nonprofit alternatives to Elsevier’s product line of data-compiling and sharing tools. But so far financial backing for the developmental work is thin. One of the best known attempts is the Open Science Framework, a web-based data interface built by the Center for Open Science, which has an annual budget of about $6 million, provided largely by foundations and other private donors.

In general, U.S. research universities — a $70 billion scientific enterprise — have not made major contributions to such projects. The Association of American Universities and the Association of Public and Land-grant Universities have, however, formed a team that’s begun studying the future of data sharing. So far, that effort has been focused on more basic steps such as establishing data-storage facilities, linking them together, and simply persuading scientists to take seriously the need to share data.…(More)”

How data can heal our oceans

Curated on Posted on by Stefaan Verhulst

Nishan Degnarain and Steve Adler at WEF: “We have collected more data on our oceans in the past two years than in the history of the planet.

There has been a proliferation of remote and near sensors above, on, and beneath the oceans. New low-cost micro satellites ring the earth and can record what happens below daily. Thousands of tidal buoys follow currents transmitting ocean temperature, salinity, acidity and current speed every minute. Undersea autonomous drones photograph and map the continental shelf and seabed, explore deep sea volcanic vents, and can help discover mineral and rare earth deposits.

The volume, diversity and frequency of data is increasing as the cost of sensors fall, new low-cost satellites are launched, and an emerging drone sector begins to offer new insights into our oceans. In addition, new processing capabilities are enhancing the value we receive from such data on the biological, physical and chemical properties of our oceans.

Yet it is not enough.

We need much more data at higher frequency, quality, and variety to understand our oceans to the degree we already understand the land. Less than 5% of the oceans are comprehensively monitored. We need more data collection capacity to unlock the sustainable development potential of the oceans and protect critical ecosystems.

More data from satellites will help identify illegal fishing activity, track plastic pollution, and detect whales and prevent vessel collisions. More data will help speed the placement of offshore wind and tide farms, improve vessel telematics, develop smart aquaculture, protect urban coastal zones, and enhance coastal tourism.

Unlocking the ocean data market

But we’re not there yet.

This new wave of data innovation is constrained by inadequate data supply, demand, and governance. The supply of existing ocean data is locked by paper records, old formats, proprietary archives, inadequate infrastructure, and scarce ocean data skills and capacity.

The market for ocean observation is driven by science and science isn’t adequately funded.

To unlock future commercial potential, new financing mechanisms are needed to create market demand that will stimulate greater investments in new ocean data collection, innovation and capacity.

Efforts such as the Financial Stability Board’s Taskforce on Climate-related Financial Disclosure have gone some way to raise awareness and create demand for such ocean-related climate risk data.

Much data that is produced is collected by nations, universities and research organizations, NGO’s, and the private sector, but only a small percentage is Open Data and widely available.

Data creates more value when it is widely utilized and well governed. Helping organize to improve data infrastructure, quality, integrity, and availability is a requirement for achieving new ocean data-driven business models and markets. New Ocean Data Governance models, standards, platforms, and skills are urgently needed to stimulate new market demand for innovation and sustainable development….(More)”.

Crowd Research: Open and Scalable University Laboratories

Curated on Posted on by Stefaan Verhulst

Paper by Rajan Vaish et al: “Research experiences today are limited to a privileged few at select universities. Providing open access to research experiences would enable global upward mobility and increased diversity in the scientific workforce. How can we coordinate a crowd of diverse volunteers on open-ended research? How could a PI have enough visibility into each person’s contributions to recommend them for further study? We present Crowd Research, a crowdsourcing technique that coordinates open-ended research through an iterative cycle of open contribution, synchronous collaboration, and peer assessment. To aid upward mobility and recognize contributions in publications, we introduce a decentralized credit system: participants allocate credits to each other, which a graph centrality algorithm translates into a collectively-created author order. Over 1,500 people from 62 countries have participated, 74% from institutions with low access to research. Over two years and three projects, this crowd has produced articles at top-tier Computer Science venues, and participants have gone on to leading graduate programs….(More)”.

Rage against the machines: is AI-powered government worth it?

Curated on Posted on by Stefaan Verhulst

Maëlle Gavet at the WEF: “…the Australian government’s new “data-driven profiling” trial for drug testing welfare recipients, to US law enforcement’s use of facial recognition technology and the deployment of proprietary software in sentencing in many US courts … almost by stealth and with remarkably little outcry, technology is transforming the way we are policed, categorized as citizens and, perhaps one day soon, governed. We are only in the earliest stages of so-called algorithmic regulation — intelligent machines deploying big data, machine learning and artificial intelligence (AI) to regulate human behaviour and enforce laws — but it already has profound implications for the relationship between private citizens and the state….

Some may herald this as democracy rebooted. In my view it represents nothing less than a threat to democracy itself — and deep scepticism should prevail. There are five major problems with bringing algorithms into the policy arena:

  1. Self-reinforcing bias…
  2. Vulnerability to attack…
  3. Who’s calling the shots?…
  4. Are governments up to it?…
  5. Algorithms don’t do nuance….

All the problems notwithstanding, there’s little doubt that AI-powered government of some kind will happen. So, how can we avoid it becoming the stuff of bad science fiction? To begin with, we should leverage AI to explore positive alternatives instead of just applying it to support traditional solutions to society’s perceived problems. Rather than simply finding and sending criminals to jail faster in order to protect the public, how about using AI to figure out the effectiveness of other potential solutions? Offering young adult literacy, numeracy and other skills might well represent a far superior and more cost-effective solution to crime than more aggressive law enforcement. Moreover, AI should always be used at a population level, rather than at the individual level, in order to avoid stigmatizing people on the basis of their history, their genes and where they live. The same goes for the more subtle, yet even more pervasive data-driven targeting by prospective employers, health insurers, credit card companies and mortgage providers. While the commercial imperative for AI-powered categorization is clear, when it targets individuals it amounts to profiling with the inevitable consequence that entire sections of society are locked out of opportunity….(More)”.

We have unrealistic expectations of a tech-driven future utopia

Curated on Posted on by Stefaan Verhulst

Bob O’Donnell in RECODE: “No one likes to think about limits, especially in the tech industry, where the idea of putting constraints on almost anything is perceived as anathema.

In fact, the entire tech industry is arguably built on the concept of bursting through limitations and enabling things that weren’t possible before. New technology developments have clearly created incredible new capabilities and opportunities, and have generally helped improve the world around us.

But there does come a point — and I think we’ve arrived there — where it’s worth stepping back to both think about and talk about the potential value of, yes, technology limits … on several different levels.

On a technical level, we’ve reached a point where advances in computing applications like AI, or medical applications like gene splicing, are raising even more ethical questions than practical ones on issues such as how they work and for what applications they might be used. Not surprisingly, there aren’t any clear or easy answers to these questions, and it’s going to take a lot more time and thought to create frameworks or guidelines for both the appropriate and inappropriate uses of these potentially life-changing technologies.

Does this mean these kinds of technological advances should be stopped? Of course not. But having more discourse on the types of technologies that get created and released certainly needs to happen.

 Even on a practical level, the need for limiting people’s expectations about what a technology can or cannot do is becoming increasingly important. With science-fiction-like advances becoming daily occurrences, it’s easy to fall into the trap that there are no limits to what a given technology can do. As a result, people are increasingly willing to believe and accept almost any kind of statements or predictions about the future of many increasingly well-known technologies, from autonomous driving to VR to AI and machine learning. I hate to say it, but it’s the fake news of tech.

Just as we’ve seen the fallout from fake news on all sides of the political perspective, so, too, are we starting to see that unbridled and unlimited expectations for certain new technologies are starting to have negative implications of their own. Essentially, we’re starting to build unrealistic expectations for a tech-driven nirvana that doesn’t clearly jibe with the realities of the modern world, particularly in the time frames that are often discussed….(More)”.

The accuracy of farmer-generated data in an agricultural citizen science methodology

Curated on Posted on by Stefaan Verhulst

Jonathan Steinke, Jacob van Etten and Pablo Mejía Zelan in Agronomy for Sustainable Development: “Over the last decades, participatory approaches involving on-farm experimentation have become more prevalent in agricultural research. Nevertheless, these approaches remain difficult to scale because they usually require close attention from well-trained professionals. Novel large-N participatory trials, building on recent advances in citizen science and crowdsourcing methodologies, involve large numbers of participants and little researcher supervision. Reduced supervision may affect data quality, but the “Wisdom of Crowds” principle implies that many independent observations from a diverse group of people often lead to highly accurate results when taken together. In this study, we test whether farmer-generated data in agricultural citizen science are good enough to generate valid statements about the research topic. We experimentally assess the accuracy of farmer observations in trials of crowdsourced crop variety selection that use triadic comparisons of technologies (tricot). At five sites in Honduras, 35 farmers (women and men) participated in tricot experiments. They ranked three varieties of common bean (Phaseolus vulgaris L.) for Plant vigor, Plant architecture, Pest resistance, and Disease resistance. Furthermore, with a simulation approach using the empirical data, we did an order-of-magnitude estimation of the sample size of participants needed to produce relevant results. Reliability of farmers’ experimental observations was generally low (Kendall’s W 0.174 to 0.676). But aggregated observations contained information and had sufficient validity (Kendall’s tau coefficient 0.33 to 0.76) to identify the correct ranking orders of varieties by fitting Mallows-Bradley-Terry models to the data. Our sample size simulation shows that low reliability can be compensated by engaging higher numbers of observers to generate statistically meaningful results, demonstrating the usefulness of the Wisdom of Crowds principle in agricultural research. In this first study on data quality from a farmer citizen science methodology, we show that realistic numbers of less than 200 participants can produce meaningful results for agricultural research by tricot-style trials….(More)”.

China seeks glimpse of citizens’ future with crime-predicting AI

Curated on Posted on by Stefaan Verhulst

Yuan Yang, Yingzhi Yang and Sherry Fei Ju in the Financial Times: “China, a surveillance state where authorities have unchecked access to citizens’ histories, is seeking to look into their future with technology designed to predict and prevent crime. Companies are helping police develop artificial intelligence they say will help them identify and apprehend suspects before criminal acts are committed. “If we use our smart systems and smart facilities well, we can know beforehand . . . who might be a terrorist, who might do something bad,” Li Meng, vice-minister of science and technology, said on Friday.

Facial recognition company Cloud Walk has been trialling a system that uses data on individuals’ movements and behaviour — for instance visits to shops where weapons are sold — to assess their chances of committing a crime. Its software warns police when a citizen’s crime risk becomes dangerously high, allowing the police to intervene. “The police are using a big-data rating system to rate highly suspicious groups of people based on where they go and what they do,” a company spokesperson told the Financial Times. Risks rise if the individual “frequently visits transport hubs and goes to suspicious places like a knife store”, the spokesperson added. China’s authoritarian government has always amassed personal data to monitor and control its citizens — whether they are criminals or suspected of politically sensitive activity. But new technology, from phones and computers to fast-developing AI software, is amplifying its capabilities. These are being used to crack down on even the most minor of infractions — facial recognition cameras, for instance, are also being used to identify and shame jaywalkers, according to state media. Mr Li said crime prediction would become an important use for AI technology in the government sphere.

China’s crime-prediction technology relies on several AI techniques, including facial recognition and gait analysis, to identify people from surveillance footage. In addition, “crowd analysis” can be used to detect “suspicious” patterns of behaviour in crowds, for example to single out thieves from normal passengers at a train stations. As well as tracking people with a criminal history, Cloud Walk’s technology is being used to monitor “high-risk” places such as hardware stores…(More)”

The DeepMind debacle demands dialogue on data

Curated on Posted on by Stefaan Verhulst

Hetan Shah in Nature: “Without public approval, advances in how we use data will stall. That is why a regulator’s ruling against the operator of three London hospitals is about more than mishandling records from 1.6 million patients. It is a missed opportunity to have a conversation with the public about appropriate uses for their data….

What can be done to address this deficit? Beyond meeting legal standards, all relevant institutions must take care to show themselves trustworthy in the eyes of the public. The lapses of the Royal Free hospitals and DeepMind provide, by omission, valuable lessons.

The first is to be open about what data are transferred. The extent of data transfer between the Royal Free and DeepMind came to light through investigative journalism. In my opinion, had the project proceeded under open contracting, it would have been subject to public scrutiny, and to questions about whether a company owned by Google — often accused of data monopoly — was best suited to create a relatively simple app.

The second lesson is that data transfer should be proportionate to the task. Information-sharing agreements should specify clear limits. It is unclear why an app for kidney injury requires the identifiable records of every patient seen by three hospitals over a five-year period.

Finally, governance mechanisms must be strengthened. It is shocking to me that the Royal Free did not assess the privacy impact of its actions before handing over access to records. DeepMind does deserve credit for (belatedly) setting up an independent review panel for health-care projects, especially because the panel has a designated budget and has not required members to sign non-disclosure agreements. (The two groups also agreed a new contract late last year, after criticism.)

More is needed. The Information Commissioner asked the Royal Free to improve its processes but did not fine it or require it to rescind data. This rap on the knuckles is unlikely to deter future, potentially worse, misuses of data. People are aware of the potential for over-reach, from the US government’s demands for state voter records to the Chinese government’s alleged plans to create a ‘social credit’ system that would monitor private behaviour.

Innovations such as artificial intelligence, machine learning and the Internet of Things offer great opportunities, but will falter without a public consensus around the role of data. To develop this, all data collectors and crunchers must be open and transparent. Consider how public confidence in genetic modification was lost in Europe, and how that has set back progress.

Public dialogue can build trust through collaborative efforts. A 14-member Citizen’s Reference Panel on health technologies was convened in Ontario, Canada in 2009. The Engage2020 programme incorporates societal input in the Horizon2020 stream of European Union science funding….(More)”

Policy Entrepreneurship at the White House

Curated on Posted on by Stefaan Verhulst

Tom Kalil on “getting things done in large organizations“: “For a total of 16 years, I had the honor and privilege of working at the White House, first for President Clinton (1993-2001) and later for President Obama (2009-2017). My colleagues and I had the opportunity to help design, launch, and sustain dozens of science and technology policy initiatives. We launched major research initiatives to create the “industries of the future,” such as robotics and advanced materials. We worked with Congress to give every agency the authority to support incentive prizes of up to $50 million, and to make it easier for startups to raise capital and go public. We built coalitions of government agencies, companies, foundations, universities, and nonprofits to prepare 100,000 K-12 STEM teachers, foster more vibrant startup ecosystems all over America, advance the Maker Movement and accelerate the commercialization of federally funded research. On a good day we were able to serve as “policy entrepreneurs,” which involved generating or spotting new ideas and taking the steps needed to identify and evaluate policy options, support a sound decisionmaking process, ensure implementation, and monitor the effectiveness of the president’s policies and initiatives….(More)”