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)”.

AI Isn’t a Solution to All Our Problems


Article by Griffin McCutcheon, John Malloy, Caitlyn Hall, and Nivedita Mahesh: “From the esoteric worlds of predictive health care and cybersecurity to Google’s e-mail completion and translation apps, the impacts of AI are increasingly being felt in our everyday lived experience. The way it has crepted into our lives in such diverse ways and its proficiency in low-level knowledge shows that AI is here to stay. But like any helpful new tool, there are notable flaws and consequences to blindly adapting it. 

AI is a tool—not a cure-all to modern problems….

Connecterra is trying to use TensorFlow to address global hunger through AI-enabled efficient farming and sustainable food development. The company uses AI-equipped sensors to track cattle health, helping farmers look for signs of illness early on. But, this only benefits one type of farmer: those rearing cattle who are able to afford a device to outfit their entire herd. Applied this way, AI can only improve the productivity of specific resource-intensive dairy farms and is unlikely to meet Connecterra’s goal of ending world hunger.

This solution, and others like it, ignores the wider social context of AI’s application. The belief that AI is a cure-all tool that will magically deliver solutions if only you can collect enough data is misleading and ultimately dangerous as it prevents other effective solutions from being implemented earlier or even explored. Instead, we need to both build AI responsibly and understand where it can be reasonably applied. 

Challenges with AI are exacerbated because these tools often come to the public as a “black boxes”—easy to use but entirely opaque in nature. This shields the user from understanding what biases and risks may be involved, and this lack of public understanding of AI tools and their limitations is a serious problem. We shouldn’t put our complete trust in programs whose workings their creators cannot interpret. These poorly understood conclusions from AI generate risk for individual users, companies or government projects where these tools are used. 

With AI’s pervasiveness and the slow change of policy, where do we go from here? We need a more rigorous system in place to evaluate and manage risk for AI tools….(More)”.

Information literacy in the age of algorithms


Report by Alison J. Head, Ph.D., Barbara Fister, Margy MacMillan: “…Three sets of questions guided this report’s inquiry:

  1. What is the nature of our current information environment, and how has it influenced how we access, evaluate, and create knowledge today? What do findings from a decade of PIL research tell us about the information skills and habits students will need for the future?
  2. How aware are current students of the algorithms that filter and shape the news and information they encounter daily? What
    concerns do they have about how automated decision-making systems may influence us, divide us, and deepen inequalities?
  3. What must higher education do to prepare students to understand the new media landscape so they will be able to participate in sharing and creating information responsibly in a changing and challenged world?
    To investigate these questions, we draw on qualitative data that PIL researchers collected from student focus groups and faculty interviews during fall 2019 at eight U.S. colleges and universities. Findings from a sample of 103 students and 37 professors reveal levels of awareness and concerns about the age of algorithms on college campuses. They are presented as research takeaways….(More)”.

Machine Learning, Big Data and the Regulation of Consumer Credit Markets: The Case of Algorithmic Credit Scoring


Paper by Nikita Aggarwal et al: “Recent advances in machine learning (ML) and Big Data techniques have facilitated the development of more sophisticated, automated consumer credit scoring models — a trend referred to as ‘algorithmic credit scoring’ in recognition of the increasing reliance on computer (particularly ML) algorithms for credit scoring. This chapter, which forms part of the 2018 collection of short essays ‘Autonomous Systems and the Law’, examines the rise of algorithmic credit scoring, and considers its implications for the regulation of consumer creditworthiness assessment and consumer credit markets more broadly.

The chapter argues that algorithmic credit scoring, and the Big Data and ML technologies underlying it, offer both benefits and risks for consumer credit markets. On the one hand, it could increase allocative efficiency and distributional fairness in these markets, by widening access to, and lowering the cost of, credit, particularly for ‘thin-file’ and ‘no-file’ consumers. On the other hand, algorithmic credit scoring could undermine distributional fairness and efficiency, by perpetuating discrimination in lending against certain groups and by enabling the more effective exploitation of borrowers.

The chapter considers how consumer financial regulation should respond to these risks, focusing on the UK/EU regulatory framework. As a general matter, it argues that the broadly principles and conduct-based approach of UK consumer credit regulation provides the flexibility necessary for regulators and market participants to respond dynamically to these risks. However, this approach could be enhanced through the introduction of more robust product oversight and governance requirements for firms in relation to their use of ML systems and processes. Supervisory authorities could also themselves make greater use of ML and Big Data techniques in order to strengthen the supervision of consumer credit firms.

Finally, the chapter notes that cross-sectoral data protection regulation, recently updated in the EU under the GDPR, offers an important avenue to mitigate risks to consumers arising from the use of their personal data. However, further guidance is needed on the application and scope of this regime in the consumer financial context….(More)”.

The future is intelligent: Harnessing the potential of artificial intelligence in Africa


Youssef Travaly and Kevin Muvunyi at Brookings: “…AI in particular presents countless avenues for both the public and private sectors to optimize solutions to the most crucial problems facing the continent today, especially for struggling industries. For example, in health care, AI solutions can help scarce personnel and facilities do more with less by speeding initial processing, triage, diagnosis, and post-care follow up. Furthermore, AI-based pharmacogenomics applications, which focus on the likely response of an individual to therapeutic drugs based on certain genetic markers, can be used to tailor treatments. Considering the genetic diversity found on the African continent, it is highly likely that the application of these technologies in Africa will result in considerable advancement in medical treatment on a global level.

In agricultureAbdoulaye Baniré Diallo, co-founder and chief scientific officer of the AI startup My Intelligent Machines, is working with advanced algorithms and machine learning methods to leverage genomic precision in livestock production models. With genomic precision, it is possible to build intelligent breeding programs that minimize the ecological footprint, address changing consumer demands, and contribute to the well-being of people and animals alike through the selection of good genetic characteristics at an early stage of the livestock production process. These are just a few examples that illustrate the transformative potential of AI technology in Africa.

However, a number of structural challenges undermine rapid adoption and implementation of AI on the continent. Inadequate basic and digital infrastructure seriously erodes efforts to activate AI-powered solutions as it reduces crucial connectivity. (For more on strategies to improve Africa’s digital infrastructure, see the viewpoint on page 67 of the full report). A lack of flexible and dynamic regulatory systems also frustrates the growth of a digital ecosystem that favors AI technology, especially as tech leaders want to scale across borders. Furthermore, lack of relevant technical skills, particularly for young people, is a growing threat. This skills gap means that those who would have otherwise been at the forefront of building AI are left out, preventing the continent from harnessing the full potential of transformative technologies and industries.

Similarly, the lack of adequate investments in research and development is an important obstacle. Africa must develop innovative financial instruments and public-private partnerships to fund human capital development, including a focus on industrial research and innovation hubs that bridge the gap between higher education institutions and the private sector to ensure the transition of AI products from lab to market….(More)”.

Technology Can't Fix Algorithmic Injustice


Annette Zimmerman, Elena Di Rosa and Hochan Kima at Boston Review: “A great deal of recent public debate about artificial intelligence has been driven by apocalyptic visions of the future. Humanity, we are told, is engaged in an existential struggle against its own creation. Such worries are fueled in large part by tech industry leaders and futurists, who anticipate systems so sophisticated that they can perform general tasks and operate autonomously, without human control. Stephen Hawking, Elon Musk, and Bill Gates have all publicly expressed their concerns about the advent of this kind of “strong” (or “general”) AI—and the associated existential risk that it may pose for humanity. In Hawking’s words, the development of strong AI “could spell the end of the human race.”

These are legitimate long-term worries. But they are not all we have to worry about, and placing them center stage distracts from ethical questions that AI is raising here and now. Some contend that strong AI may be only decades away, but this focus obscures the reality that “weak” (or “narrow”) AI is already reshaping existing social and political institutions. Algorithmic decision making and decision support systems are currently being deployed in many high-stakes domains, from criminal justice, law enforcement, and employment decisions to credit scoring, school assignment mechanisms, health care, and public benefits eligibility assessments. Never mind the far-off specter of doomsday; AI is already here, working behind the scenes of many of our social systems.

What responsibilities and obligations do we bear for AI’s social consequences in the present—not just in the distant future? To answer this question, we must resist the learned helplessness that has come to see AI development as inevitable. Instead, we should recognize that developing and deploying weak AI involves making consequential choices—choices that demand greater democratic oversight not just from AI developers and designers, but from all members of society….(More)”.

Will Artificial Intelligence Eat the Law? The Rise of Hybrid Social-Ordering Systems


Paper by Tim Wu: “Software has partially or fully displaced many former human activities, such as catching speeders or flying airplanes, and proven itself able to surpass humans in certain contests, like Chess and Jeopardy. What are the prospects for the displacement of human courts as the centerpiece of legal decision-making?

Based on the case study of hate speech control on major tech platforms, particularly on Twitter and Facebook, this Essay suggests displacement of human courts remains a distant prospect, but suggests that hybrid machine–human systems are the predictable future of legal adjudication, and that there lies some hope in that combination, if done well….(More)”.

Too much information? The new challenge for decision-makers


Daniel Winter at the Financial Times: “…Concern over technology’s capacity both to shrink the world and complicate it has grown steadily since the second world war — little wonder, perhaps, when the existential threats it throws up have expanded from nuclear weapons to encompass climate change (and any consequent geoengineering), gene editing and AI as well. The financial crisis of 2008, in which poorly understood investment instruments made economies totter, has added to the unease over our ability to make sense of things.

From preoccupying cold war planners, attempts to codify best practice in sense-making have gone on to exercise (often profitably) business academics and management consultants, and now draw large audiences online.

Blogs, podcasts and YouTube channels such as Rebel Wisdom and Future Thinkers aim to arm their followers with the tools they need to understand the world, and make the right decisions. Daniel Schmachtenberger is one such voice, whose interviews on YouTube and his podcast Civilization Emerging have reached hundreds of thousands of people.

“Due to increasing technological capacity — increasing population multiplied by increasing impact per person — we’re making more and more consequential choices with worse and worse sense-making to inform those choices,” he says in one video. “Exponential tech is leading to exponential disinformation.” Strengthening individuals’ ability to handle and filter information would go a long way towards improving the “information ecology”, Mr Schmachtenberger argues. People need to get used to handling complex information and should train themselves to be less distracted. “The impulse to say, ‘hey, make it really simple so everyone can get it’ and the impulse to say ‘[let’s] help people actually make sense of the world well’ are different things,” he says. Of course, societies have long been accustomed to handling complexity. No one person can possibly memorise the entirety of US law or be an expert in every field of medicine. Libraries, databases, and professional and academic networks exist to aggregate expertise.

The increasing bombardment of data — the growing amount of evidence that can inform any course of action — pushes such systems to the limit, prompting people to offload the work to computers. Yet this only defers the problem. As AI becomes more sophisticated, its decision-making processes become more opaque. The choice as to whether to trust it — to let it run a self-driving car in a crowded town, say — still rests with us.

Far from being able to outsource all complex thinking to the cloud, Prof Guillén warns that leaders will need to be as skilled as ever at handling and critically evaluating information. It will be vital, he suggests, to build flexibility into the policymaking process.

“The feedback loop between the effects of the policy and how you need to recalibrate the policy in real time becomes so much faster and so much more unpredictable,” he says. “That’s the effect that complex policies produce.” A more piecemeal approach could better suit regulation in fast-moving fields, he argues, with shorter “bursts” of rulemaking, followed by analysis of the effects and then adjustments or additions where necessary.

Yet however adept policymakers become at dealing with a complex world, their task will at some point always resist simplification. That point is where the responsibility resides. Much as we may wish it otherwise, governance will always be as much an art as a science….(More)”.

Lack of guidance leaves public services in limbo on AI, says watchdog


Dan Sabbagh at the Guardian: “Police forces, hospitals and councils struggle to understand how to use artificial intelligence because of a lack of clear ethical guidance from the government, according to the country’s only surveillance regulator.

The surveillance camera commissioner, Tony Porter, said he received requests for guidance all the time from public bodies which do not know where the limits lie when it comes to the use of facial, biometric and lip-reading technology.

“Facial recognition technology is now being sold as standard in CCTV systems, for example, so hospitals are having to work out if they should use it,” Porter said. “Police are increasingly wearing body cameras. What are the appropriate limits for their use?

“The problem is that there is insufficient guidance for public bodies to know what is appropriate and what is not, and the public have no idea what is going on because there is no real transparency.”

The watchdog’s comments came as it emerged that Downing Street had commissioned a review led by the Committee on Standards in Public Life, whose chairman had called on public bodies to reveal when they use algorithms in decision making.

Lord Evans, a former MI5 chief, told the Sunday Telegraph that “it was very difficult to find out where AI is being used in the public sector” and that “at the very minimum, it should be visible, and declared, where it has the potential for impacting on civil liberties and human rights and freedoms”.

AI is increasingly deployed across the public sector in surveillance and elsewhere. The high court ruled in September that the police use of automatic facial recognition technology to scan people in crowds was lawful.

Its use by South Wales police was challenged by Ed Bridges, a former Lib Dem councillor, who noticed the cameras when he went out to buy a lunchtime sandwich, but the court held that the intrusion into privacy was proportionate….(More)”.

Biased Algorithms Are Easier to Fix Than Biased People


Sendhil Mullainathan in The New York Times: “In one study published 15 years ago, two people applied for a job. Their résumés were about as similar as two résumés can be. One person was named Jamal, the other Brendan.

In a study published this year, two patients sought medical care. Both were grappling with diabetes and high blood pressure. One patient was black, the other was white.

Both studies documented racial injustice: In the first, the applicant with a black-sounding name got fewer job interviews. In the second, the black patient received worse care.

But they differed in one crucial respect. In the first, hiring managers made biased decisions. In the second, the culprit was a computer program.

As a co-author of both studies, I see them as a lesson in contrasts. Side by side, they show the stark differences between two types of bias: human and algorithmic.

Marianne Bertrand, an economist at the University of Chicago, and I conducted the first study: We responded to actual job listings with fictitious résumés, half of which were randomly assigned a distinctively black name.

The study was: “Are Emily and Greg more employable than Lakisha and Jamal?”

The answer: Yes, and by a lot. Simply having a white name increased callbacks for job interviews by 50 percent.

I published the other study in the journal “Science” in late October with my co-authors: Ziad Obermeyer, a professor of health policy at University of California at Berkeley; Brian Powers, a clinical fellow at Brigham and Women’s Hospital; and Christine Vogeli, a professor of medicine at Harvard Medical School. We focused on an algorithm that is widely used in allocating health care services, and has affected roughly a hundred million people in the United States.

To better target care and provide help, health care systems are turning to voluminous data and elaborately constructed algorithms to identify the sickest patients.

We found these algorithms have a built-in racial bias. At similar levels of sickness, black patients were deemed to be at lower risk than white patients. The magnitude of the distortion was immense: Eliminating the algorithmic bias would more than double the number of black patients who would receive extra help. The problem lay in a subtle engineering choice: to measure “sickness,” they used the most readily available data, health care expenditures. But because society spends less on black patients than equally sick white ones, the algorithm understated the black patients’ true needs.

One difference between these studies is the work needed to uncover bias…(More)”.