Paper by Sofia Ranchordas: “Recent EU legislative and policy initiatives aim to offer flexible, innovation-friendly, and future-proof regulatory frameworks. Key examples are the EU Coordinated Plan on AI and the recently published EU AI Regulation Proposal which refer to the importance of experimenting with regulatory sandboxes so as to balance innovation in AI against its potential risks. Originally developed in the Fintech sector, regulatory sandboxes create a testbed for a selected number of innovative projects, by waiving otherwise applicable rules, guiding compliance, or customizing enforcement. Despite the burgeoning literature on regulatory sandboxes and the regulation of AI, the legal, methodological, and ethical challenges of regulatory sandboxes have remained understudied. This exploratory article delves into the some of the benefits and intricacies of employing experimental legal instruments in the context of the regulation of AI. This article’s contribution is twofold: first, it contextualizes the adoption of regulatory sandboxes in the broader discussion on experimental approaches to regulation; second, it offers a reflection on the steps ahead for the design and implementation of AI regulatory sandboxes….(More)”.
Bridging the digital divide for underserved communities
Report by Deloitte: “…This “digital divide” was first noted more than 25 years ago as consumer communications needs shifted from landline voice to internet access. The economics of broadband spawned availability, adoption, and affordability disparities between rural and urban geographies and between lower- and higher-income segments. Today, the digital divide still presents a significant gap after more than $100 billion of infrastructure investment has been allocated by the US government over the past decade to address this issue. The current debate regarding additional funds for broadband deployment implies that further examination is warranted regarding how to get to broadband for all and achieve the resulting economic prosperity.
Quantifying the economic impact of bridging the digital divide clearly shows the criticality of broadband infrastructure to the US economy. Deloitte developed economic models to evaluate the relationship between broadband and economic growth. Our models indicate that a 10-percentage-point increase of broadband penetration in 2016 would have resulted in more than 806,000 additional jobs in 2019, or an average annual increase of 269,000 jobs. Moreover, we found a strong correlation between broadband availability and jobs and GDP growth. A 10-percentage-point increase of broadband access in 2014 would have resulted in more than 875,000 additional US jobs and $186B more in economic output in 2019. The analysis also showed that higher broadband speeds drive noticeable improvements in job growth, albeit with diminishing returns. As an example, the gain in jobs from 50 to 100 Mbps is more than the gain in jobs from 100 to 150 Mbps….(More)”.
WHO, Germany launch new global hub for pandemic and epidemic intelligence
Press Release: “The World Health Organization (WHO) and the Federal Republic of Germany will establish a new global hub for pandemic and epidemic intelligence, data, surveillance and analytics innovation. The Hub, based in Berlin and working with partners around the world, will lead innovations in data analytics across the largest network of global data to predict, prevent, detect prepare for and respond to pandemic and epidemic risks worldwide.
H.E. German Federal Chancellor Dr Angela Merkel said: “The current COVID-19 pandemic has taught us that we can only fight pandemics and epidemics together. The new WHO Hub will be a global platform for pandemic prevention, bringing together various governmental, academic and private sector institutions. I am delighted that WHO chose Berlin as its location and invite partners from all around the world to contribute to the WHO Hub.”
The WHO Hub for Pandemic and Epidemic Intelligence is part of WHO’s Health Emergencies Programme and will be a new collaboration of countries and partners worldwide, driving innovations to increase availability and linkage of diverse data; develop tools and predictive models for risk analysis; and to monitor disease control measures, community acceptance and infodemics. Critically, the WHO Hub will support the work of public health experts and policy-makers in all countries with insights so they can take rapid decisions to prevent and respond to future public health emergencies.
“We need to identify pandemic and epidemic risks as quickly as possible, wherever they occur in the world. For that aim, we need to strengthen the global early warning surveillance system with improved collection of health-related data and inter-disciplinary risk analysis,” said Jens Spahn, German Minister of Health. “Germany has consistently been committed to support WHO’s work in preparing for and responding to health emergencies, and the WHO Hub is a concrete initiative that will make the world safer.”
Working with partners globally, the WHO Hub will drive a scale-up in innovation for existing forecasting and early warning capacities in WHO and Member States. At the same time, the WHO Hub will accelerate global collaborations across public and private sector organizations, academia, and international partner networks. It will help them to collaborate and co-create the necessary tools for managing and analyzing data for early warning surveillance. It will also promote greater access to data and information….(More)”.
Artificial intelligence (AI) has become one of the most impactful technologies of the twenty-first century
Lynne Parker at the AI.gov website: “Artificial intelligence (AI) has become one of the most impactful technologies of the twenty-first century. Nearly every sector of the economy and society has been affected by the capabilities and potential of AI. AI is enabling farmers to grow food more efficiently, medical researchers to better understand and treat COVID-19, scientists to develop new materials, transportation professionals to deliver more goods faster and with less energy, weather forecasters to more accurately predict the tracks of hurricanes, and national security protectors to better defend our Nation.
At the same time, AI has raised important societal concerns. What is the impact of AI on the changing nature of work? How can we ensure that AI is used appropriately, and does not result in unfair discrimination or bias? How can we guard against uses of AI that infringe upon human rights and democratic principles?
These dual perspectives on AI have led to the concept of “trustworthy AI”. Trustworthy AI is AI that is designed, developed, and used in a manner that is lawful, fair, unbiased, accurate, reliable, effective, safe, secure, resilient, understandable, and with processes in place to regularly monitor and evaluate the AI system’s performance and outcomes.
Achieving trustworthy AI requires an all-of-government and all-of-Nation approach, combining the efforts of industry, academia, government, and civil society. The Federal government is doing its part through a national strategy, called the National AI Initiative Act of 2020 (NAIIA). The National AI Initiative (NAII) builds upon several years of impactful AI policy actions, many of which were outcomes from EO 13859 on Maintaining American Leadership in AI.
Six key pillars define the Nation’s AI strategy:
- prioritizing AI research and development;
- strengthening AI research infrastructure;
- advancing trustworthy AI through technical standards and governance;
- training an AI-ready workforce;
- promoting international AI engagement; and
- leveraging trustworthy AI for government and national security.
Coordinating all of these efforts is the National AI Initiative Office, which is legislated by the NAIIA to coordinate and support the NAII. This Office serves as the central point of contact for exchanging technical and programmatic information on AI activities at Federal departments and agencies, as well as related Initiative activities in industry, academia, nonprofit organizations, professional societies, State and tribal governments, and others.
The AI.gov website provides a portal for exploring in more depth the many AI actions, initiatives, strategies, programs, reports, and related efforts across the Federal government. It serves as a resource for those who want to learn more about how to take full advantage of the opportunities of AI, and to learn how the Federal government is advancing the design, development, and use of trustworthy AI….(More)”
In scramble to respond to Covid-19, hospitals turned to models with high risk of bias
Article by Elise Reuter: “…Michigan Medicine is one of 80 hospitals contacted by MedCity News between January and April in a survey of decision-support systems implemented during the pandemic. Of the 26 respondents, 12 used machine learning tools or automated decision systems as part of their pandemic response. Larger hospitals and academic medical centers used them more frequently.
Faced with scarcities in testing, masks, hospital beds and vaccines, several of the hospitals turned to models as they prepared for difficult decisions. The deterioration index created by Epic was one of the most widely implemented — more than 100 hospitals are currently using it — but in many cases, hospitals also formulated their own algorithms.
They built models to predict which patients were most likely to test positive when shortages of swabs and reagents backlogged tests early in the pandemic. Others developed risk-scoring tools to help determine who should be contacted first for monoclonal antibody treatment, or which Covid patients should be enrolled in at-home monitoring programs.
MedCity News also interviewed hospitals on their processes for evaluating software tools to ensure they are accurate and unbiased. Currently, the FDA does not require some clinical decision-support systems to be cleared as medical devices, leaving the developers of these tools and the hospitals that implement them responsible for vetting them.
Among the hospitals that published efficacy data, some of the models were only evaluated through retrospective studies. This can pose a challenge in figuring out how clinicians actually use them in practice, and how well they work in real time. And while some of the hospitals tested whether the models were accurate across different groups of patients — such as people of a certain race, gender or location — this practice wasn’t universal.
As more companies spin up these models, researchers cautioned that they need to be designed and implemented carefully, to ensure they don’t yield biased results.
An ongoing review of more than 200 Covid-19 risk-prediction models found that the majority had a high risk of bias, meaning the data they were trained on might not represent the real world….(More)”.
Responsible Data Science
Book by Peter Bruce and Grant Fleming: “The increasing popularity of data science has resulted in numerous well-publicized cases of bias, injustice, and discrimination. The widespread deployment of “Black box” algorithms that are difficult or impossible to understand and explain, even for their developers, is a primary source of these unanticipated harms, making modern techniques and methods for manipulating large data sets seem sinister, even dangerous. When put in the hands of authoritarian governments, these algorithms have enabled suppression of political dissent and persecution of minorities. To prevent these harms, data scientists everywhere must come to understand how the algorithms that they build and deploy may harm certain groups or be unfair.
Responsible Data Science delivers a comprehensive, practical treatment of how to implement data science solutions in an even-handed and ethical manner that minimizes the risk of undue harm to vulnerable members of society. Both data science practitioners and managers of analytics teams will learn how to:
- Improve model transparency, even for black box models
- Diagnose bias and unfairness within models using multiple metrics
- Audit projects to ensure fairness and minimize the possibility of unintended harm…(More)”
Can Democracy Safeguard the Future?
Book by Graham Smith: “Our democracies repeatedly fail to safeguard the future. From pensions to pandemics, health and social care through to climate, biodiversity and emerging technologies, democracies have been unable to deliver robust policies for the long term.
In this book, Graham Smith asks why. Exploring the drivers of short-termism, he considers ways of reshaping legislatures and constitutions and proposes strengthening independent offices whose overarching goals do not change at every election. More radically, Smith argues that forms of participatory and deliberative politics offer the most effective democratic response to the current political myopia, as well as a powerful means of protecting the interests of generations to come….(More)”.
The Delusions of Crowds: Why People Go Mad in Groups
Book by William J. Bernstein: “…Inspired by Charles Mackay’s 19th-century classic Memoirs of Extraordinary Popular Delusions and the Madness of Crowds, Bernstein engages with mass delusion with the same curiosity and passion, but armed with the latest scientific research that explains the biological, evolutionary, and psychosocial roots of human irrationality. Bernstein tells the stories of dramatic religious and financial mania in western society over the last 500 years—from the Anabaptist Madness that afflicted the Low Countries in the 1530s to the dangerous End-Times beliefs that animate ISIS and pervade today’s polarized America; and from the South Sea Bubble to the Enron scandal and dot com bubbles of recent years. Through Bernstein’s supple prose, the participants are as colorful as their motivation, invariably “the desire to improve one’s well-being in this life or the next.”
As revealing about human nature as they are historically significant, Bernstein’s chronicles reveal the huge cost and alarming implications of mass mania: for example, belief in dispensationalist End-Times has over decades profoundly affected U.S. Middle East policy. Bernstein observes that if we can absorb the history and biology of mass delusion, we can recognize it more readily in our own time, and avoid its frequently dire impact….(More)”.
The lapses in India’s Covid-19 data are a result of decades of callousness towards statistics
Prathamesh Mulye at Quartz: “India is paying a huge price for decades of callous attitude towards data and statistics. For several weeks now, experts have been calling out the Indian government and state heads for suppressing Covid-19 infection and death figures. None of the political leaders have addressed these concerns even as official data reflects a small fraction of what’s playing out at hospitals and cremation grounds.
A major reason why administrations are getting away without an answer is that data lapses are nothing new to India.
Successive regimes in the country have tinkered and twisted figures as per their convenience without much consequences. For years, the country has been criticised for insufficient and poor quality data relating to a range of topics, including GDP, farmer suicide, and even unemployment…
Before the pandemic started, the most prominent data controversy in India was around the GDP numbers, which the Modi government continuously changed and chopped to cover up the slowdown in economic growth. In 2019, the Modi government also chose not to publish an unemployment data report that showed that joblessness in the country was at a nine-year high in 2017-18. And last year, in the middle of the pandemic, the government said it had no data on the number of frontline workers who had lost their lives to Covid-19 or a list of police personnel fatalities due to the disease.
Experts say that India’s statistical machinery has been deliberately weakened over the past few years to protect various governments’ false claims and image.
“The weakened statistical machinery manifests itself in different ways such as delays and questions about data quality. Also, when the results of a survey don’t suit the government in power, it tries to suppress data. This happened, for instance, with nutrition data in previous governments too,” said Reetika Khera, associate professor at the Indian Institute of Technology (IIT), Delhi.
“Think of the economy as a patient: data captures its pulse rate. If you don’t listen to the pulse, you won’t be able to diagnose correctly, let alone cure it,” she added….(More)”
Better Law for a Better World: New Approaches to Law Practice and Education
Book by Liz Curran: “How as a society can we find ways of ensuring the people who are the most vulnerable or have little voice can avail themselves of the protection in law to improve their social, cultural, health and economic outcomes as befits civilised society?
Better Law for a Better World answers this question by looking at innovative practices and developments emerging within law practice and education and shares the skills and techniques that could lead to confidence in the law and its ability to respond. Using recent research from Australia, practice initiatives and information, the book breaks down ways for law students, legal educators and law practitioners (including judicial officers, law administrators, legislators and policy makers) to enhance access to justice and improve outcomes through new approaches to lawyering. These can include: Multi-Disciplinary Practice (including health justice partnerships); integrated justice practice; restorative practice; empowerment modes (community & professional development and policy skills); client-centred approaches and collaborative interdisciplinary practice informed by practical experience. The book contains critical information on what such practice might look like and the elements that will be required in the development of the essential skills and criteria for such practice. It seeks to open up a dialogue about how we can make the law better. This includes making the community more central to the operation of the law and improving client-centred practice so that the Rule of Law can deliver on its claims to serve, protect and ensure equality before the law. It explores practical ways that emerging lawyers can be trained differently to ensure improved communication, collaboration, problem solving, partnership and interpersonal skills. The book explores the challenges of such work. It also gives suggestions on how to reduce professional barriers and variations in practice to effectively, humanely and efficiently make a difference in people’s lives….(More)”.