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The terms revolution and disruption in the context of technological innovation are probably bandied about a bit more liberally than they should. Technological revolution and disruption imply upheaval and systemic reevaluations of the way that humans interact with industry and even each other. Actual technological advancement, however, moves at a much slower pace and tends to augment our current processes rather than to outright displace them. Oftentimes, we fail to realize the ubiquity of legacy systems in our everyday lives sometimes to our own detriment.

Consider the keyboard. The QWERTY layout of keys is standard for English keyboards across the world. Even though the layout remains a mainstay of modern office setups, its origins trace back to the mass popularization of a typewriter manufactured and sold by E. Remington & Sons in 1874.[1] Urban legend has it that the layout was designed to slow down typists from jamming typing mechanisms, yet the reality reveals otherwise the layout was actually designed to assist those transcribing messages from Morse code.[2] Once typists took to the format, the keyboard, as we know it today, was embraced as a global standard even as the use of Morse code declined.[3] Like QWERTY, our familiarity and comfort with legacy systems has contributed to their rise. These systems are varied in their scope, and they touch everything: healthcare, supply chains, our financial systems and even the way we interact at a human level. However, their use and value may be tested sooner than we realize.

Artificial intelligence (AI) and blockchain technology (blockchain) are two novel innovations that offer the opportunity for us to move beyond our legacy systems and streamline enterprise management and compliance in ways previously unimaginable. However, their potential is often clouded by their buzzword status, with bad actors taking advantage of the hype. When one cuts through the haze, it becomes clear that these two technologies hold significant transformative potential. While these new innovations can certainly function on their own, AI and blockchain also complement one another in such ways that their combination offers business solutions, not only the ability to build upon legacy enterprise systems but also the power to eventually upend them in favor of next level solutions. Getting to that point, however, takes time and is not without cost. While humans are generally quick to embrace technological change, our regulatory frameworks take longer to adapt. The need to address this constraint is pressing real market solutions for these technologies have started to come online, while regulatory opaqueness hurdles abound. As innovators seek to exploit the convergence of AI and blockchain innovations, they must pay careful attention to overcome both technical and regulatory hurdles that accompany them. Do so successfully, and the rewards promise to be bountiful.

First, a bit of taxonomy is in order.

AI in a Nutshell:

Artificial Intelligence is the capability of machine to imitate intelligent human behavior, such as learning, understanding language, solving problems, planning and identifying objects.[4] More practically speaking, however, todays AI is actually mostly limited to if X, then Y varieties of simple tasks. It is through supervised learning that AI is trained, and this process requires an enormous amount of data. For example, IBMs question-answering supercomputer Watson was able to beat Jeopardy! champions Brad Rutter and Ken Jennings in 2011, because Watson had been coded to understand simple questions by being fed countless iterations and had access to vast knowledge in the form of digital data Likewise, Google DeepMinds AlphaGo defeated the Go champion Lee Sedol in 2016, since AlphaGo had undergone countless instances of Go scenarios and collected them as data. As such, most implementations of AI involve simple tasks, assuming that relevant information is readily accessible. In light of this, Andrew Ng, the Stanford roboticist, noted that, [i]f a typical person can do a mental task with less than one second of thought, we can probably automate it using AI either now or in the near future.[5]

Moreover, a significant portion of AI currently in use or being developed is based on machine learning. Machine learning is a method by which AI adapts its algorithms and models based on exposure to new data thereby allowing AI to learn without being programmed to perform specific tasks. Developing high performance machine learning-based AI, therefore, requires substantial amounts of data. Data high in both quality and quantity will lead to better AI, since an AI instance can indiscriminately accept all data provided to it, and can refine and improve its algorithms to the extent of the provided data. For example, AI that visually distinguishes Labradors from other breeds of dogs will become better at its job the more it is exposed to clear and accurate pictures of Labradors.

It is in these data amalgamations that AI does its job best. Scanning and analyzing vast subsets of data is something that a computer can do very rapidly as compared to a human. However, AI is not perfect, and many of the pitfalls that AI is prone to are often the result of the difficulty in conveying how humans process information in contrast to machines. One example of this phenomenon that has dogged the technology has been AIs penchant for hallucinations. An AI algorithm hallucinates when the input is interpreted by the machine into something that seems implausible to a human looking at the same thing.[6] Case in point, AI has interpreted an image of a turtle as that of a gun or a rifle as a helicopter.[7] This occurs because machines are hypersensitive to, and interpret, the tiniest of pixel patterns that we humans do not process. Because of the complexity of this analysis, developers are only now beginning to understand such AI phenomena.

When one moves beyond pictures of guns and turtles, however, AIs shortfalls can become much less innocuous. AI learning is based on inputted data, yet much of this data reflects the inherent shortfalls and behaviors of everyday individuals. As such, without proper correction for bias and other human assumptions, AI can, for example, perpetuate racial stereotypes and racial profiling.[8] Therefore, proper care for what goes into the system and who gets access to the outputs must be employed for the ethical employment of AI, but therein lies an additional problem who has access to enough data to really take full advantage of and develop robust AI?

Not surprisingly, because large companies are better able to collect and manage increasingly larger amounts of data than individuals or smaller entities, such companies have remained better positioned in developing complex AI. In response to this tilted landscape, various private and public organizations, including the U.S. Department of Justices Bureau of Justice, Google Scholar and the International Monetary Fund, have launched open source initiatives to make publicly available vast amounts of data that such organizations have collected over many years.

Blockchain in a Nutshell:

Blockchain technology as we know it today came onto the scene in late 2009 with the rise of Bitcoin, perhaps the most famous application of the technology. Fundamentally, blockchain is a data structure that makes it possible to create a tamper-proof, distributed, peer-to-peer system of ledgers containing immutable, time-stamped and cryptographically connected blocks of data. In practice, this means that data can be written only once onto a ledger, which is then read-only for every user. However, many of the most utilized blockchain protocols, for example, the Bitcoin or Ethereum networks, maintain and update their distributed ledgers in a decentralized manner, which stands in contrast to traditional networks reliant on a trusted, centralized data repository.[9] In structuring the network in this way, these blockchain mechanisms function to remove the need for a trusted third party to handle and store transaction data. Instead, data are distributed so that every user has access to the same information at the same time. In order to update a ledgers distributed information, the network employs pre-defined consensus mechanisms and militarygrade cryptography to prevent malicious actors from going back and retroactively editing or tampering with previously recorded information. In most cases, networks are open source, maintained by a dedicated community and made accessible to any connected device that can validate transactions on a ledger, which is referred to as a node.

Nevertheless, the decentralizing feature of blockchain comes with significant resource and processing drawbacks. Many blockchain-enabled platforms run very slowly and have interoperability and scalability problems. Moreover, these networks use massive amounts of energy. For example, the Bitcoin network requires the expenditure of about 50 terawatt hours per year equivalent to the energy needs of the entire country of Singapore.[10] To ameliorate these problems, several market participants have developed enterprise blockchains with permissioned networks. While many of them may be open source, the networks are led by known entities that determine who may verify transactions on that blockchain, and, therefore, the required consensus mechanisms are much more energy efficient.

Not unlike AI, a blockchain can also be coded with certain automated processes to augment its recordkeeping abilities, and, arguably, it is these types of processes that contributed to blockchains rise. That rise, some may say, began with the introduction of the Ethereum network and its engineering around smart contracts a term used to describe computer code that automatically executes all or part of an agreement and is stored on a blockchain-enabled platform. Smart contracts are neither contracts in the sense of legally binding agreements nor smart in employing applications of AI. Rather, they consist of coded automated parameters responsive to what is recorded on a blockchain. For example, if the parties in a blockchain network have indicated, by initiating a transaction, that certain parameters have been met, the code will execute the step or steps triggered by those coded parameters. The input parameters and the execution steps for smart contracts need to be specific the digital equivalent of if X, then Y statements. In other words, when required conditions have been met, a particular specified outcome occurs; in the same way that a vending machine sells a can of soda once change has been deposited, smart contracts allow title to digital assets to be transferred upon the occurrence of certain events. Nevertheless, the tasks that smart contracts are currently capable of performing are fairly rudimentary. As developers figure out how to expand their networks, integrate them with enterprise-level technologies and develop more responsive smart contracts, there is every reason to believe that smart contracts and their decentralized applications (dApps) will see increased adoption.

AI and blockchain technology may appear to be diametric opposites. AI is an active technology it analyzes what is around and formulates solutions based on the history of what it has been exposed to. By contrast, blockchain is data agnostic with respect to what is written into it the technology bundle is largely passive. It is primarily in that distinction that we find synergy, for each technology augments the strengths and tempers the weaknesses of the other. For example, AI technology requires access to big data sets in order to learn and improve, yet many of the sources of these data sets are hidden in proprietary silos. With blockchain, stakeholders are empowered to contribute data to an openly available and distributed network with immutability of data as a core feature. With a potentially larger pool of data to work from, the machine learning mechanisms of a widely distributed, blockchain-enabled and AI-powered solution could improve far faster than that of a private data AI counterpart. These technologies on their own are more limited. Blockchain technology, in and of itself, is not capable of evaluating the accuracy of the data written into its immutable network garbage in, garbage out. AI can, however, act as a learned gatekeeper for what information may come on and off the network and from whom. Indeed, the interplay between these diverse capabilities will likely lead to improvements across a broad array of industries, each with unique challenges that the two technologies together may overcome.

[1] See Rachel Metz, Why We Cant Quit the QWERTY Keyboard, MIT Technology Review (Oct. 13, 2018), available at: https://www.technologyreview.com/s/611620/why-we-cant-quit-the-qwerty-keyboard/.

[2] Alexis Madrigal, The Lies Youve Been Told About the Origin of the QWERTY Keyboard, The Atlantic (May 3, 2013), available at: https://www.theatlantic.com/technology/archive/2013/05/the-lies-youve-been-told-about-the-origin-of-the-qwerty-keyboard/275537/.

[3] See Metz, supra note 1.

[4] See Artificial Intelligence, Merriam-Websters Online Dictionary, Merriam-Webster (last accessed Mar. 27, 2019), available at: https://www.merriam-webster.com/dictionary/artificial%20intelligence.

[5] See Andrew Ng, What Artificial Intelligence Can and Cant Do Right Now, Harvard Business Review (Nov. 9, 2016), available at: https://hbr.org/2016/11/what-artificial-intelligence-can-and-cant-do-right-now.

[6] Louise Matsakis, Artificial Intelligence May Not Hallucinate After All, Wired (May 8, 2019), available at: https://www.wired.com/story/adversarial-examples-ai-may-not-hallucinate/.

[7] Id.

[8] Jerry Kaplan, Opinion: Why Your AI Might Be Racist, Washington Post (Dec. 17, 2018), available at: https://www.washingtonpost.com/opinions/2018/12/17/why-your-ai-might-be-racist/?noredirect=on&utm_term=.568983d5e3ec.

[9] See Shanaan Cohsey, David A. Hoffman, Jeremy Sklaroff and David A. Wishnick, Coin-Operated Capitalism, Penn. Inst. for L. & Econ. (No. 18-37) (Jul. 17, 2018) at 12, available at: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3215345##.

[10] See Bitcoin Energy Consumption Index (last accessed May 13, 2019), available at: https://digiconomist.net/bitcoin-energy-consumption.

[View source.]

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The New ABCs: Artificial Intelligence, Blockchain And How Each Complements The Other - JD Supra

Enterprises are using multiple applications powered by the convergence of blockchain and artificial intelligence, to increase efficiency and effectiveness of RPA.

It is common knowledge that Robotics is powered by artificial intelligence delivering excellence and efficiency in well-known areas- cryptocurrencies, chatbots, or voice-assisted technologies.

Blockchain In the Times of AI

The field of robotics is immensely challenging, and to grow in this segment, companies need to offer reliable and affordable solutions to their clients and customers.

The exciting news is that RPA is also one of the most promising areas utilizing the convergence of Blockchain and AI. This convergence is now showing never before massive efficiencies in the field of robotics.

Robotics has gained massive popularity across industries over the years using artificial intelligence, making all processes more effective and error-free. Now, blockchain will keep the data decentralized and free from any central or concentrated control. By combining the decentralized power of blockchain with the agility of artificial intelligence, the field of robotics can be elevated and advanced in several ways.

The features offered by artificial intelligence will increase the efficiency of robots using automation multi-fold, while data immutability offered by blockchain will tamper-proof the processes. Leveraging these technologies simultaneously to the robotics, the operating mechanism is pre-set to achieve the desired objectives and business goals.

Swarm Robotics: The one to be benefitted the most?

The significance of artificial intelligence and blockchain is the most prominent in the case of Swarm Robotics. This is mainly because both these innovations can be applied collectively to control a group of robots. AI controls every Swarm Robot as it operates according to the pre-set principles and requirements. The collective response and behavior of the Robots can be significantly enhanced with the application of artificial intelligence and blockchain.

AIoT Convergence of Artificial Intelligence with the Internet of Things

This convergence has enormous benefits on scalability with the enhanced scope of operations. Global enterprises have already started witnessing the application of blockchain and artificial intelligence with the Swarm robotics gaining popularity, specifically in the areas related to entertainment, healthcare, and farming. Although several stakeholders have explicitly expressed concerns about the security and safety of the features, there is hardly any negative view about the potential of applications to benefit the industry. Blockchain is a credible technology measure to alleviate the concerns of the stakeholders about the privacy and secrecy of the data. Using the secure cryptographic signatures and other advanced technologies available in the blockchain space, security, and safety concerns regarding robots can be easily handled.

Artificial intelligence will power the Robots while continuing to be the strength of this integration, while Blockchain technology will be playing a passive role by providing backup support to ensure data security and safety. Hence, with this convergence is applied to robotics in an integrated manner, robotics will transform and benefit the industry in an unbelievably positive way.

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Blockchain and Artificial Intelligence Convergence Powering the Robotics Capability - EnterpriseTalk

A nonprofit organization claims to have created a phone app that uses artificial intelligence to identify people the group believes to be known white nationalists. Of course, the organization considers people and organizations to be white nationalists even if theyre just regular conservativesand even if theyre not white.

IREHR Sees White Nationalists Everywhere

In February, the Kansas City Star ran a widely circulated hit piece on America First Students, a new student organization at Kansas State University. The smear campaign was based on research by the Institute for Education and Research on Human Rights (IREHR). Despite being a little-known and little-funded organization, IREHR regularly publishes large volumes of research and has even created a bigotry tracking app that promises to use artificial intelligence (AI) and machine learning to out known white nationalists.

Founded in 1983, IREHRs vision, according to its founder Leonard Zeskind, is to fight against what it sees as white nationalism. Zeskind claims that white nationalism is in a symbiotic relationship with mainstream conservatism, such as the Christian right and paleoconservativism. In 2011, Zeskind wrote about IREHRs Re-Birth in the Huffington Post, explicitly attacking the Tea Party movement for its racism in his second sentence. Tea Party Nationalism even has its own section on the IREHR website, right under Race, Racism & White Nationalism. In 2018, years after the Tea Party movement was at its peak, IREHR ran a piece titled Guns and Racism: From the National Rifle Association to Far Right Militias and the Tea Party. The Tea Party movement is such a target of IREHRs ire thatZeskind, along with IREHR president Devin Burghart, wrote a report called Tea Party Nationalism, commissioned by the NAACP. The NAACP also created a companion website: TeaPartyNationalism.com.

In addition to going after the Tea Party movement and the National Rifle Association, IREHR also targets the American Conservative Unions Political Action Conference (CPAC). In 2014, Burghart published The Unbearable Whiteness of CPAC.

Given how IREHR sees white nationalism throughout mainstream conservatism and considers whiteness as something unbearable, its phone application that promises to identify known white nationalists via artificial intelligence sounds incredibly Orwellian.

The Trepper App

Burghart announced the creation of IREHRs anti-bigotry app during a Holocaust Remembrance Day Speech in May 2019. The app is named after Leopold Trepper, the head of a Soviet anti-Nazi spy ring during WWII known as the Red Orchestra. Trepper was later imprisoned by Stalin, reportedly because Trepperhimself Jewishsurrounded himself with Jews. (This detail was not included in IREHRs speech on Holocaust Remembrance Day; however, IREHRs founder and president still generally appreciates communist figures, even visiting the grave of Marxist philosopher Antonio Gramsci.)

During the speech, Burghart boasted that the app now allows us to use the latest in machine learning and artificial intelligence to see if people in the videos you submit are known white nationalists. Machine learning and artificial intelligence are often codewords for facial recognition software. It is unclear, however, how exactly machine learning and artificial intelligence are being used in the app.

Trepper: The Anti-Bigotry App promises to provide instant updates about new threats near you. According to the IREHR website, the app will allow users to receive push notifications of seemingly white nationalist activity, and it allows users to upload their own events, photos, and videos to the app.

Downloading Trepper, users are greeted with a page telling them what they can find on the app: a news tab, a Tools for Response and Resistance tab, and a way to Report incidents (see the screenshot below).

The Report Bigotry tab allows users to request help (a feature coming soon), record video of an ongoing live event, or write a report about an instance of bigotry they witnessed.

The reporting feature lets users provide details about a reportedly bigotry-related event, ranging from murder, the first item on the list, to something internet-based or other.

Much of the app is still under construction. The signs and symbols of bigotry section of the response toolkit is coming soon as is the help, FAQ, tips, etc. section. The section for users to find or create their own anti-bigotry groups loads a blank page. As of writing, the only video uploaded to the Trepper app is a video of a Patriot Prayer rally from 2018. Patriot Prayer is a Portland-based group that has been maligned by the Southern Poverty Law Center (SPLC) and targeted by Antifa activists.

Curiously, Patriot Prayer is included on the Trepper app even though its controversial founder, Joey Gibson, identifies as Japanese, not Caucasian, and has repeatedly condemned white supremacy. Apparently, to the IREHR app, a person doesnt even need to be white to be a white nationalist.

In fact, in the security section of the response toolkit, the Trepper app deviates from merely attacking white nationalism: It suggests ways to protect against the far right, whomever IREHR deems the far right to be. The security section of the app tells users to protect their offices and homes from possible far right infiltrators:

The app also tells users involved in their anti-racist activity to shred documents, handle potential hate mail with tongs, and circulate pictures online of people they suspect to be following them.

Who Is Funding IREHR?

A section of the IREHR app is devoted to learning more about IREHR, with the option to donate to IREHR. According to tax filings, the organization has not had gross receipts of more than $50,000 since 2008. It is unclear how such a small organization with so little funding can create such a complicated appwith the potential of harnessing facial recognition technology.

Garbage In, Garbage Out

IREHRs plans to implement what appears to be facial recognition technology to identify people accused of having the wrong political beliefs is terrifying. IREHR has a history of identifying people, all on its own, as white nationalists who are not white nationalists at all. IREHR already attacks mainstream conservative groups and mainstream conservative ideas, groups, and figures as white nationalist, or as being tied to white nationalism. It is a terrible precedent in general to use artificial intelligence to identify (and catalog) people because they have the wrong political beliefs.

Excerpt from:
Trepper: The Anti-Bigotry App That Uses Artificial Intelligence to Identify White Nationalists - Capital Research Center

Four days later, the Vatican issued a paper calling for "new forms of regulation" of AI based on the principles of "transparency, inclusion, responsibility, impartiality, reliability, security and privacy".

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The striking thing about both these pronouncements is the degree to which they align with the official line from Silicon Valley, which couches ethics as a set of voluntary principles that will guide, rather than direct, the development of AI.

By proposing broad principles, which are notoriously difficult to define legally, they avoid the guard rails or red lines that would give genuine oversight over the way this technology develops.

The other problem with these voluntary codes is they will always be in conflict with the key drivers of technological change: to make money (if you are a business) or save money (if you are a government).

But theres an alternative approach to harnessing technological change that warrants serious consideration. It is proposed by the Australian Human Rights Commission. Rather than woolly guiding principles, Commissioner Ed Santow argues that AI should be developed within three clear parameters.

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First, it should comply with human rights law. Second, it should be used in ways that minimise harm. Finally, humans need to be accountable for the way AI is used. The difference with this approach is that it anchors AI development within the existing legal framework.

To legally operate in Australia, under this proposal, the development of artificial intelligence would need to ensure it did not discriminate on the grounds of gender, race or social demographic, either directly or in effect.

The AI proponents would also need to show they had thought through the impact of their technology, much like a property developer needs to conduct an environmental impact statement before building.

And critically, an AI tool should have a human a flesh-and-blood person who is responsible for its design and operation.

How would these principles work in practice? Its worth looking at the failed robodebt program, under which recipients of government benefits were sent letters demanding they repay money because they had been overpaid.

If it had been scrutinised before it went live, robodebt is likely to have been found discriminatory, as it shifted the onus of proof onto people from societys most marginalised groups to show their payments were valid.

If it had been subject to a public impact review, the glaring anomalies and inconsistencies in matching Australian Tax Office and social security information would have become apparent before it was trialled on vulnerable people. And if a human had been accountable for its operation, those who received a notice would have had a course of review, rather than feeling as though they were speaking to a machine.

The whole costly and destructive debacle might have been prevented.

Embracing a future where these "disruptive" technologies remake our society guided by voluntary ethical principles is not good enough. As Robert-Elliott Smith observes in his excellent book Rage Inside the Machine, the idea that AI is amoral is bunkum. The values and priorities of the humans who commission and design it will determine the end product.

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This challenge will become more pressing as algorithms begin to process banks of photos and video that purport to "recognise" individuals, track their movements and predict their motivations. The Human Rights Commission report calls for a moratorium on the use of this technology in high-stakes areas such as policing. It seeks to protect citizens from "bad" applications, but also to provide an incentive for industry to support the development of an enforceable legal framework.

Champions of technology may well argue that government intervention will slow down development and risk Australia being "left behind". But if we succeed in ensuring AI is "fair by design", we might end up with a distinctly Australian technology, which reflects our values, to share with the world.

Peter Lewis is the director of the Centre for Responsible Technology.

Peter Lewis is the executive director of Essential, a progressive research and communications company and the director of the Centre for Responsible Technology.

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Artificial intelligence won't rule the world so long as humans rule AI - The Age

Eindhoven-based start-up Fruitpunch AI is developing a system that uses artificial intelligence to analyze how effective the Dutch governments policy is in combating Corona virus infections.

Over the next two months, the platform from the AI engineers will collect data that will provide insight into how citizens have behaved in response to measures taken to stop the disease. In addition, the platform is to analyze the impact of the pandemic on the economy. It will do this by comparing the increase in the number of Corona patients with the fall in stock market prices, among other things. On this basis, it is possible to assess the probability of a recession as a result of the pandemic. And predict how deep it might become.

In turn, the effects of recession have an impact on citizens health and well-being. Due to loss of work, income, exposure to stress, debt, cardiovascular disease and so on.

A relationship between these various trends has already been proven by scientists. Theyve previously published articles on this subject in e.g. the renowned medical scientific journal The Lancet. The researchers also aim to relate this to the governments anti-Corona policy.

The idea to start this AI Fruitpunch against covid-19 challenge came from the AI Fruitpunch founders Buster Franken and Vincent Fokker. The reason was that we had to cancel our events because there were going to be more than a thousand people, says Buster Franken. Back then, that was the limit that the government had announced for the number of visitors when deciding whether an event could go ahead or not. I thought that was strange. Its unclear as to what risks it could reduce. I then googled to find out more about how the pandemic was unfolding. I saw pretty quickly that there are patterns in the rates of infection.

As an example, Frankens conclusion is that the Chinese government has failed in its policy to combat the disease. You see that the number of people infected with Corona has never increased as rapidly in any other country as it did in China. Thats now dropping because its getting warmer. But in actual fact, the Chinese government has failed.

What I want to know is how many infected people are causing a snowball effect which makes it impossible to control the pandemic. Plus there arent nearly enough beds in ICUs for people to recover. If you know that, you are then able to communicate effectively as a government. You can say to the population: we are taking certain measures to prevent infection because we now have so many patients. We dont want to reach the level that will cause an exponential increase in the number of infections beyond what hospitals are capable of coping with. If you say that, people will have a better understanding of why they need to stay away from certain events as much as possible.

In two weeks time, Fruitpunch will embark on a second challenge in collaboration with Omdena, a Silicon Valley-based global platform for AI engineers, a partner organization of AI Fruitpunch. Subsequently, the AI engineers will analyze the effects of government measures around the world on the rise in the number of Corona patients. This will likely also reveal the effect of having children at home compared to the effect of letting them go to school. This is a discussion point in The Netherlands. Since in other countries children are obliged to stay home. Like in neighboring such as Belgium and France.

You can see that there wasnt a game plan in place for the outbreak of a virus. And that every country is doing something different, says Franken. Which in itself is pretty ridiculous seeing that something like this is likely to happen.

The fact that the World Health Organization (WHO) does not have any such plan in place is not surprising, Franken says, considering it has no say in the health policies of the various member countries. But we do intend to cooperate with the WHO on this.

Franken adds that the data thats needed to make the various analyses is fairly easy to find. Both economic and medical. Were using the WHO updates. Along with an earlier study of the relationship between a recession and public health in Brazil. We adjust the results to the variances between Brazil and the other countries. For example, such as such as public access to healthcare.

Read all our other IO articles on the COVID-19 here.

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Artificial Intelligence checks influence Corona policy has on the infection explosion - Innovation Origins