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?
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:
- Self-reinforcing bias…
- Vulnerability to attack…
- Who’s calling the shots?…
- Are governments up to it?…
- 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
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
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
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
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
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)”
From binoculars to big data: Citizen scientists use emerging technology in the wild
Interview by Rebecca Kondos: “For years, citizen scientists have trekked through local fields, rivers, and forests to observe, measure, and report on species and habitats with notebooks, binoculars, butterfly nets, and cameras in hand. It’s a slow process, and the gathered data isn’t easily shared. It’s a system that has worked to some degree, but one that’s in need of a technology and methodology overhaul.
Thanks to the team behind Wildme.org and their Wildbook software, both citizen and professional scientists are becoming active participants in using AI, computer vision, and big data. Wildbook is working to transform the data collection process, and citizen scientists who use the software have more transparency into conservation research and the impact it’s making. As a result, engagement levels have increased; scientists can more easily share their work; and, most important, endangered species like the whale shark benefit.
In this interview, Colin Kingen, a software engineer for WildBook, (with assistance from his colleagues Jason Holmberg and Jon Van Oast) discusses Wildbook’s work, explains classic problems in field observation science, and shares how Wildbook is working to solve some of the big problems that have plagued wildlife research. He also addresses something I’ve wondered about: why isn’t there an “uberdatabase” to share the work of scientists across all global efforts? The work Kingen and his team are doing exemplifies what can be accomplished when computer scientists with big hearts apply their talents to saving wildlife….(More)”.
Some Countries Like ‘Nudges’ More Than Others
Cass Sunstein at Bloomberg: “All over the world, private and public institutions have been adopting “nudges” — interventions that preserve freedom of choice, but that steer people in a particular direction.
A GPS device nudges you. So does a reminder from your doctor, informing you that you have an appointment next Wednesday; an automatic enrollment policy from your employer, defaulting you into a 401(k) plan; and a calorie label at fast-food restaurants, telling you that a cheeseburger won’t be great for your waistline.
Recent evidence demonstrates that nudges can be amazingly effective — far more so, per dollar spent, than other tools, such as economic incentives. But a big question remains: Across different nations, do nudges have the same impact? Here’s a cautionary note.
One of the most famous success stories in the annals of nudging comes from the U.K. To encourage delinquent taxpayers to pay up, British officials simply informed them, by letter, that the overwhelming majority of British taxpayers pay their taxes on time.
It worked. Within just a few weeks, the letters produced millions of dollars in additional revenue. Consistent with standard findings in behavioral science, recipients of the letters didn’t like learning that they were deviating from the social norm. Like most of us, they didn’t want to be creeps or shirkers, and so they paid up.
For other nations, including the U.S., that was an intriguing finding. So our Department of Treasury tried the same approach. It sent letters to delinquent taxpayers, informing them (accurately) that 91 percent of American taxpayers pay on time. On the basis of the British data, the expectation was that a lot of people would be ashamed, and pay their taxes.
Except they didn’t. The U.S. Treasury didn’t get any more money.
How come? It’s reasonable to speculate that in the U.S., delinquent taxpayers just don’t care about the social norm. If they learn about it, they still aren’t motivated to pay.
This finding demonstrates that different groups can react very differently to nudges. It’s well known that when people are told that they are using more energy than their neighbors, they scale back — so that information is an effective nudge. But a study in California suggests that things are a bit more complicated….
In general, we don’t yet have a lot of evidence of international differences on the impact of nudges. But it would be surprising if such evidence doesn’t start to accumulate. Wherever people begin with strong preferences, and don’t like the direction in which they are being nudged, nudges are going to have a weaker effect.
For many nudges, that’s just fine. Actually, it’s part of the point….(More)”.
Open data on universities – New fuel for transformation
François van Schalkwyk at University World News: “Accessible, usable and relevant open data on South African universities makes it possible for a wide range of stakeholders to monitor, advise and challenge the transformation of South Africa’s universities from an informed perspective.
Some describe data as the new oil while others suggest it is a new form of capital or compare it to electricity. Either way, there appears to be a groundswell of interest in the potential of data to fuel development.
Whether the proliferation of data is skewing development in favour of globally networked elites or disrupting existing asymmetries of information and power, is the subject of ongoing debate. Certainly, there are those who will claim that open data, from a development perspective, could catalyse disruption and redistribution.
Open data is data that is free to use without restriction. Governments and their agencies, universities and their researchers, non-governmental organisations and their donors, and even corporations, are all potential sources of open data.
Open government data, as a public rather than a private resource, embedded in principles of universal access, participation and transparency, is touted as being able to restore the deteriorating levels of trust between citizens and their governments.
Open data promises to do so by making the decisions and processes of the state more transparent and inclusive, empowering citizens to participate and to hold public institutions to account for the distribution of public services and resources.
Benefits of open data
Open data has other benefits over its more cloistered cousins (data in private networks, big data, etc). By democratising access, open data makes possible the use of data on, for example, health services, crime, the environment, procurement and education by a range of different users, each bringing their own perspective to bear on the data. This can expose bias in the data or may improve the quality of the data by surfacing data errors. Both are important when data is used to shape government policies.
By removing barriers to reusing data such as copyright or licence-fees, tech-savvy entrepreneurs can develop applications to assist the public to make more informed decisions by making available easy-to-understand information on medicine prices, crime hot-spots, air quality, beneficial ownership, school performance, etc. And access to open research data can improve quality and efficiency in science.
Scientists can check and confirm the data on which important discoveries are based if the data is open, and, in some cases, researchers can reuse open data from other studies, saving them the cost and effort of collecting the data themselves.
‘Open washing’
But access alone is not enough for open data to realise its potential. Open data must also be used. And data is used if it holds some value for the user. Governments have been known to publish server rooms full of data that no one is interested in to support claims of transparency and supporting the knowledge economy. That practice is called ‘open washing’. …(More)”