Crowdsourcing data to mitigate epidemics


Gabriel M Leung and Kathy Leung at The Lancet: “Coronavirus disease 2019 (COVID-19) has spread with unprecedented speed and scale since the first zoonotic event that introduced the causative virus—severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)—into humans, probably during November, 2019, according to phylogenetic analyses suggesting the most recent common ancestor of the sequenced genomes emerged between Oct 23, and Dec 16, 2019. The reported cumulative number of confirmed patients worldwide already exceeds 70 000 in almost 30 countries and territories as of Feb 19, 2020, although that the actual number of infections is likely to far outnumber this case count.

During any novel emerging epidemic, let alone one with such magnitude and speed of global spread, a first task is to put together a line list of suspected, probable, and confirmed individuals on the basis of working criteria of the respective case definitions. This line list would allow for quick preliminary assessment of epidemic growth and potential for spread, evidence-based determination of the period of quarantine and isolation, and monitoring of efficiency of detection of potential cases. Frequent refreshing of the line list would further enable real-time updates as more clinical, epidemiological, and virological (including genetic) knowledge become available as the outbreak progresses….

We surveyed different and varied sources of possible line lists for COVID-19 (appendix pp 1–4). A bottleneck remains in carefully collating as much relevant data as possible, sifting through and verifying these data, extracting intelligence to forecast and inform outbreak strategies, and thereafter repeating this process in iterative cycles to monitor and evaluate progress. A possible methodological breakthrough would be to develop and validate algorithms for automated bots to search through cyberspaces of all sorts, by text mining and natural language processing (in languages not limited to English) to expedite these processes.In this era of smartphone and their accompanying applications, the authorities are required to combat not only the epidemic per se, but perhaps an even more sinister outbreak of fake news and false rumours, a so-called infodemic…(More)”.

Smart Village Technology


Book by Srikanta Patnaik, Siddhartha Sen and Magdi S. Mahmoud: “This book offers a transdisciplinary perspective on the concept of “smart villages” Written by an authoritative group of scholars, it discusses various aspects that are essential to fostering the development of successful smart villages. Presenting cutting-edge technologies, such as big data and the Internet-of-Things, and showing how they have been successfully applied to promote rural development, it also addresses important policy and sustainability issues. As such, this book offers a timely snapshot of the state-of-the-art in smart village research and practice….(More)”.

The many perks of using critical consumer user data for social benefit


Sushant Kumar at LiveMint: “Business models that thrive on user data have created profitable global technology companies. For comparison, market capitalization of just three tech companies, Google (Alphabet), Facebook and Amazon, combined is higher than the total market capitalization of all listed firms in India. Almost 98% of Facebook’s revenue and 84% of Alphabet’s come from serving targeted advertising powered by data collected from the users. No doubt, these tech companies provide valuable services to consumers. It is also true that profits are concentrated with private corporations and societal value for contributors of data, that is, the user, can be much more significant….

In the existing economic construct, private firms are able to deploy top scientists and sophisticated analytical tools to collect data, derive value and monetize the insights.

Imagine if personalization at this scale was available for more meaningful outcomes, such as for administering personalized treatment for diabetes, recommending crop patterns, optimizing water management and providing access to credit to the unbanked. These socially beneficial applications of data can generate undisputedly massive value.

However, handling critical data with accountability to prevent misuse is a complex and expensive task. What’s more, private sector players do not have any incentives to share the data they collect. These challenges can be resolved by setting up specialized entities that can manage data—collect, analyse, provide insights, manage consent and access rights. These entities would function as a trusted intermediary with public purpose, and may be named “data stewards”….(More)”.

See also: http://datastewards.net/ and https://datacollaboratives.org/

If China valued free speech, there would be no coronavirus crisis


Verna Yu in The Guardian: “…Despite the flourishing of social media, information is more tightly controlled in China than ever. In 2013, an internal Communist party edict known as Document No 9 ordered cadres to tackle seven supposedly subversive influences on society. These included western-inspired notions of press freedom, “universal values” of human rights, civil rights and civic participation. Even within the Communist party, cadres are threatened with disciplinary action for expressing opinions that differ from the leadership.

Compared with 17 years ago, Chinese citizens enjoy even fewer rights of speech and expression. A few days after 34-year-old Li posted a note in his medical school alumni social media group on 30 December, stating that seven workers from a local live-animal market had been diagnosed with an illness similar to Sars and were quarantined in his hospital, he was summoned by police. He was made to sign a humiliating statement saying he understood if he “stayed stubborn and failed to repent and continue illegal activities, (he) will be disciplined by the law”….

Unless Chinese citizens’ freedom of speech and other basic rights are respected, such crises will only happen again. With a more globalised world, the magnitude may become even greater – the death toll from the coronavirus outbreak is already comparable to the total Sars death toll.

Human rights in China may appear to have little to do with the rest of the world but as we have seen in this crisis, disaster could occur when China thwarts the freedoms of its citizens. Surely it is time the international community takes this issue more seriously….(More)”.

Urban Poverty Alleviation Endeavor Through E-Warong Program: Smart City (Smart People) Concept Initiative in Yogyakarta


Paper by Djaka Marwasta and Farid Suprianto: “In the era of Industrial Revolution 4.0, technology became a factor that could contribute significantly to improving the quality of life and welfare of the people of a nation. Information and Communication Technology (ICT) penetration through Internet of Things (IoT), Big Data, and Artificial Intelligence (AI) which are disruptively, has led to fundamental advances in civilization. The expansion of Industrial Revolution 4.0 has also changed the pattern of government and citizen relations which has implications for the needs of policy governance and internal government transformation. One of them is a change in social welfare development policies, where government officials are required to be responsive to social dynamics that have consequences for increasing demands for public accountability and transparency.

This paper aims to elaborate on the e-Warong program as one of the breakthroughs to reduce poverty by utilizing digital technology. E-Warong (electronic mutual cooperation shop) is an Indonesian government program based on the empowerment of the poor Grass Root Innovation (GRI) with an approach to building group awareness in encouraging the independence of the poor to develop joint ventures through mutual cooperation with utilizing ICT advantages. This program is an implementation of the Smart City concept, especially Smart Economy, within the Sustainable Development Goals framework….(More)”.

An AI Epidemiologist Sent the First Warnings of the Wuhan Virus


Eric Niiler at Wired: “On January 9, the World Health Organization notified the public of a flu-like outbreak in China: a cluster of pneumonia cases had been reported in Wuhan, possibly from vendors’ exposure to live animals at the Huanan Seafood Market. The US Centers for Disease Control and Prevention had gotten the word out a few days earlier, on January 6. But a Canadian health monitoring platform had beaten them both to the punch, sending word of the outbreak to its customers on December 31.

BlueDot uses an AI-driven algorithm that scours foreign-language news reports, animal and plant disease networks, and official proclamations to give its clients advance warning to avoid danger zones like Wuhan.

Speed matters during an outbreak, and tight-lipped Chinese officials do not have a good track record of sharing information about diseases, air pollution, or natural disasters. But public health officials at WHO and the CDC have to rely on these very same health officials for their own disease monitoring. So maybe an AI can get there faster. “We know that governments may not be relied upon to provide information in a timely fashion,” says Kamran Khan, BlueDot’s founder and CEO. “We can pick up news of possible outbreaks, little murmurs or forums or blogs of indications of some kind of unusual events going on.”…

The firm isn’t the first to look for an end-run around public health officials, but they are hoping to do better than Google Flu Trends, which was euthanized after underestimating the severity of the 2013 flu season by 140 percent. BlueDot successfully predicted the location of the Zika outbreak in South Florida in a publication in the British medical journal The Lancet….(More)”.

How randomised trials became big in development economics


Seán Mfundza Muller, Grieve Chelwa, and Nimi Hoffmann at the Conversation: “…One view of the challenge of development is that it is fundamentally about answering causal questions. If a country adopts a particular policy, will that cause an increase in economic growth, a reduction in poverty or some other improvement in the well-being of citizens?

In recent decades economists have been concerned about the reliability of previously used methods for identifying causal relationships. In addition to those methodological concerns, some have argued that “grand theories of development” are either incorrect or at least have failed to yield meaningful improvements in many developing countries.

Two notable examples are the idea that developing countries may be caught in a poverty trap that requires a “big push” to escape and the view that institutions are key for growth and development.

These concerns about methods and policies provided a fertile ground for randomised experiments in development economics. The surge of interest in experimental approaches in economics began in the early 1990s. Researchers began to use “natural experiments”, where for example random variation was part of a policy rather than decided by a researcher, to look at causation.

But it really gathered momentum in the 2000s, with researchers such as the Nobel awardees designing and implementing experiments to study a wide range of microeconomic questions.

Randomised trials

Proponents of these methods argued that a focus on “small” problems was more likely to succeed. They also argued that randomised experiments would bring credibility to economic analysis by providing a simple solution to causal questions.

These experiments randomly allocate a treatment to some members of a group and compare the outcomes against the other members who did not receive treatment. For example, to test whether providing credit helps to grow small firms or increase their likelihood of success, a researcher might partner with a financial institution and randomly allocate credit to applicants that meet certain basic requirements. Then a year later the researcher would compare changes in sales or employment in small firms that received the credit to those that did not.

Randomised trials are not a new research method. They are best known for their use in testing new medicines. The first medical experiment to use controlled randomisation occurred in the aftermath of the second world war. The British government used it to assess the effectiveness of a drug for tuberculosis treatment.

In the early 20th century and mid-20th century American researchers had used experiments like this to examine the effects of various social policies. Examples included income protection and social housing.

The introduction of these methods into development economics also followed an increase in their use in other areas of economics. One example was the study of labour markets.

Randomised control trials in economics are now mostly used to evaluate the impact of social policy interventions in poor and middle-income countries. Work by the 2019 Nobel awardees – Michael Kremer, Abhijit Banerjee and Esther Duflo – includes experiments in Kenya and India on teacher attendancetextbook provisionmonitoring of nurse attendance and the provision of microcredit.

The popularity, among academics and policymakers, of the approach is not only due to its seeming ability to solve methodological and policy concerns. It is also due to very deliberate, well-funded advocacy by its proponents….(More)”.

A World With a Billion Cameras Watching You Is Just Around the Corner


Liza Lin and Newley Purnell at the Wall Street Journal: “As governments and companies invest more in security networks, hundreds of millions more surveillance cameras will be watching the world in 2021, mostly in China, according to a new report.

The report, from industry researcher IHS Markit, to be released Thursday, said the number of cameras used for surveillance would climb above 1 billion by the end of 2021. That would represent an almost 30% increase from the 770 million cameras today. China would continue to account for a little over half the total.

Fast-growing, populous nations such as India, Brazil and Indonesia would also help drive growth in the sector, the report said. The number of surveillance cameras in the U.S. would grow to 85 million by 2021, from 70 million last year, as American schools, malls and offices seek to tighten security on their premises, IHS analyst Oliver Philippou said.

Mr. Philippou said government programs to implement widespread video surveillance to monitor the public would be the biggest catalyst for the growth in China. City surveillance also was driving demand elsewhere.

“It’s a public-safety issue,” Mr. Philippou said in an interview. “There is a big focus on crime and terrorism in recent years.”

The global security-camera industry has been energized by breakthroughs in image quality and artificial intelligence. These allow better and faster facial recognition and video analytics, which governments are using to do everything from managing traffic to predicting crimes.

China leads the world in the rollout of this kind of technology. It is home to the world’s largest camera makers, with its cameras on street corners, along busy roads and in residential neighborhoods….(More)”.

Facial recognition needs a wider policy debate


Editorial Team of the Financial Times: “In his dystopian novel 1984, George Orwell warned of a future under the ever vigilant gaze of Big Brother. Developments in surveillance technology, in particular facial recognition, mean the prospect is no longer the stuff of science fiction.

In China, the government was this year found to have used facial recognition to track the Uighurs, a largely Muslim minority. In Hong Kong, protesters took down smart lamp posts for fear of their actions being monitored by the authorities. In London, the consortium behind the King’s Cross development was forced to halt the use of two cameras with facial recognition capabilities after regulators intervened. All over the world, companies are pouring money into the technology.

At the same time, governments and law enforcement agencies of all hues are proving willing buyers of a technology that is still evolving — and doing so despite concerns over the erosion of people’s privacy and human rights in the digital age. Flaws in the technology have, in certain cases, led to inaccuracies, in particular when identifying women and minorities.

The news this week that Chinese companies are shaping new standards at the UN is the latest sign that it is time for a wider policy debate. Documents seen by this newspaper revealed Chinese companies have proposed new international standards at the International Telecommunication Union, or ITU, a Geneva-based organisation of industry and official representatives, for things such as facial recognition. Setting standards for what is a revolutionary technology — one recently described as the “plutonium of artificial intelligence” — before a wider debate about its merits and what limits should be imposed on its use, can only lead to unintended consequences. Crucially, standards ratified in the ITU are commonly adopted as policy by developing nations in Africa and elsewhere — regions where China has long wanted to expand its influence. A case in point is Zimbabwe, where the government has partnered with Chinese facial recognition company CloudWalk Technology. The investment, part of Beijing’s Belt and Road investment in the country, will see CloudWalk technology monitor major transport hubs. It will give the Chinese company access to valuable data on African faces, helping to improve the accuracy of its algorithms….

Progress is needed on regulation. Proposals by the European Commission for laws to give EU citizens explicit rights over the use of their facial recognition data as part of a wider overhaul of regulation governing artificial intelligence are welcome. The move would bolster citizens’ protection above existing restrictions laid out under its general data protection regulation. Above all, policymakers should be mindful that if the technology’s unrestrained rollout continues, it could hold implications for other, potentially more insidious, innovations. Western governments should step up to the mark — or risk having control of the technology’s future direction taken from them….(More)”.

Artificial Intelligence and National Security


CRS Report: “Artificial intelligence (AI) is a rapidly growing field of technology with potentially significant implications for national security. As such, the U.S. Department of Defense (DOD) and other nations are developing AI applications for a range of military functions. AI research is underway in the fields of intelligence collection and analysis, logistics, cyber operations, information operations, command and control, and in a variety of semiautonomous and autonomous vehicles.

Already, AI has been incorporated into military operations in Iraq and Syria. Congressional action has the potential to shape the technology’s development further, with budgetary and legislative decisions influencing the growth of military applications as well as the pace of their adoption.

AI technologies present unique challenges for military integration, particularly because the bulk of AI development is happening in the commercial sector. Although AI is not unique in this regard, the defense acquisition process may need to be adapted for acquiring emerging technologies like AI. In addition, many commercial AI applications must undergo significant modification prior to being functional for the military.

A number of cultural issues also challenge AI acquisition, as some commercial AI companies are averse to partnering with DOD due to ethical concerns, and even within the department, there can be resistance to incorporating AI technology into existing weapons systems and processes.

Potential international rivals in the AI market are creating pressure for the United States to compete for innovative military AI applications. China is a leading competitor in this regard, releasing a plan in 2017 to capture the global lead in AI development by 2030. Currently, China is primarily focused on using AI to make faster and more well-informed decisions, as well as on developing a variety of autonomous military vehicles. Russia is also active in military AI development, with a primary focus on robotics.

Although AI has the potential to impart a number of advantages in the military context, it may also introduce distinct challenges. AI technology could, for example, facilitate autonomous operations, lead to more informed military decisionmaking, and increase the speed and scale of military action. However, it may also be unpredictable or vulnerable to unique forms of manipulation. As a result of these factors, analysts hold a broad range of opinions on how influential AI will be in future combat operations. While a small number of analysts believe that the technology will have minimal impact, most believe that AI will have at least an evolutionary—if not revolutionary—effect….(More)”.