How AI-Driven Insurance Could Reduce Gun Violence


Jason Pontin at WIRED: “As a political issue, guns have become part of America’s endless, arid culture wars, where Red and Blue tribes skirmish for political and cultural advantage. But what if there were a compromise? Economics and machine learning suggest an answer, potentially acceptable to Americans in both camps.

Economists sometimes talk about “negative externalities,” market failures where the full costs of transactions are borne by third parties. Pollution is an externality, because society bears the costs of environmental degradation. The 20th-century British economist Arthur Pigou, who formally described externalities, also proposed their solution: so-called “Pigovian taxes,” where governments charge producers or customers, reducing the quantity of the offending products and sometimes paying for ameliorative measures. Pigovian taxes have been used to fight cigarette smoking or improve air quality, and are the favorite prescription of economists for reducing greenhouse gases. But they don’t work perfectly, because it’s hard for governments to estimate the costs of externalities.

Gun violence is a negative externality too. The choices of millions of Americans to buy guns overflow into uncaptured costs for society in the form of crimes, suicides, murders, and mass shootings. A flat gun tax would be a blunt instrument: It could only reduce gun violence by raising the costs of gun ownership so high that almost no one could legally own a gun, which would swell the black market for guns and probably increase crime. But insurers are very good at estimating the risks and liabilities of individual choices; insurance could capture the externalities of gun violence in a smarter, more responsive fashion.

Here’s the proposed compromise: States should require gun owners to be licensed and pay insurance, just as car owners must be licensed and insured today….

The actuaries who research risk have always considered a wide variety of factors when helping insurers price the cost of a policy. Car, home, and life insurance can vary according to a policy holder’s age, health, criminal record, employment, residence, and many other variables. But in recent years, machine learning and data analytics have provided actuaries with new predictive powers. According to Yann LeCun, the director of artificial intelligence at Facebook and the primary inventor of an important technique in deep learning called convolution, “Deep learning systems provide better statistical models with enough data. They can be advantageously applied to risk evaluation, and convolutional neural nets can be very good at prediction, because they can take into account a long window of past values.”

State Farm, Liberty Mutual, Allstate, and Progressive Insurance have all used algorithms to improve their predictive analysis and to more accurately distribute risk among their policy holders. For instance, in late 2015, Progressive created a telematics app called Snapshot that individual drivers used to collect information on their driving. In the subsequent two years, 14 billion miles of driving data were collected all over the country and analyzed on Progressive’s machine learning platform, H20.ai, resulting in discounts of $600 million for their policy holders. On average, machine learning produced a $130 discount for Progressive customers.

When the financial writer John Wasik popularized gun insurance in a series of posts in Forbes in 2012 and 2013, the NRA’s argument about prior constraints was a reasonable objection. Wasik proposed charging different rates to different types of gun owners, but there were too many factors that would have to be tracked over too long a period to drive down costs for low-risk policy holders. Today, using deep learning, the idea is more practical: Insurers could measure the interaction of dozens or hundreds of factors, predicting the risks of gun ownership and controlling costs for low-risk gun owners. Other, more risky bets might pay more. Some very risky would-be gun owners might be unable to find insurance at all. Gun insurance could even be dynamically priced, changing as the conditions of the policy holders’ lives altered, and the gun owners proved themselves better or worse risks.

Requiring gun owners to buy insurance wouldn’t eliminate gun violence in America. But a political solution to the problem of gun violence is chimerical….(More)”.

Data-Driven Regulation and Governance in Smart Cities


Chapter by Sofia Ranchordas and Abram Klop in Berlee, V. Mak, E. Tjong Tjin Tai (Eds), Research Handbook on Data Science and Law (Edward Elgar, 2018): “This paper discusses the concept of data-driven regulation and governance in the context of smart cities by describing how these urban centres harness these technologies to collect and process information about citizens, traffic, urban planning or waste production. It describes how several smart cities throughout the world currently employ data science, big data, AI, Internet of Things (‘IoT’), and predictive analytics to improve the efficiency of their services and decision-making.

Furthermore, this paper analyses the legal challenges of employing these technologies to influence or determine the content of local regulation and governance. It explores in particular three specific challenges: the disconnect between traditional administrative law frameworks and data-driven regulation and governance, the effects of the privatization of public services and citizen needs due to the growing outsourcing of smart cities technologies to private companies; and the limited transparency and accountability that characterizes data-driven administrative processes. This paper draws on a review of interdisciplinary literature on smart cities and offers illustrations of data-driven regulation and governance practices from different jurisdictions….(More)”.

Prediction, Judgment and Complexity


NBER Working Paper by Agrawal, Ajay and Gans, Joshua S. and Goldfarb, Avi: “We interpret recent developments in the field of artificial intelligence (AI) as improvements in prediction technology. In this paper, we explore the consequences of improved prediction in decision-making. To do so, we adapt existing models of decision-making under uncertainty to account for the process of determining payoffs. We label this process of determining the payoffs ‘judgment.’ There is a risky action, whose payoff depends on the state, and a safe action with the same payoff in every state. Judgment is costly; for each potential state, it requires thought on what the payoff might be. Prediction and judgment are complements as long as judgment is not too difficult. We show that in complex environments with a large number of potential states, the effect of improvements in prediction on the importance of judgment depend a great deal on whether the improvements in prediction enable automated decision-making. We discuss the implications of improved prediction in the face of complexity for automation, contracts, and firm boundaries….(More)”.

The future of statistics and data science


Paper by Sofia C. Olhede and Patrick J. Wolfe in Statistics & Probability Letters: “The Danish physicist Niels Bohr is said to have remarked: “Prediction is very difficult, especially about the future”. Predicting the future of statistics in the era of big data is not so very different from prediction about anything else. Ever since we started to collect data to predict cycles of the moon, seasons, and hence future agriculture yields, humankind has worked to infer information from indirect observations for the purpose of making predictions.

Even while acknowledging the momentous difficulty in making predictions about the future, a few topics stand out clearly as lying at the current and future intersection of statistics and data science. Not all of these topics are of a strictly technical nature, but all have technical repercussions for our field. How might these repercussions shape the still relatively young field of statistics? And what can sound statistical theory and methods bring to our understanding of the foundations of data science? In this article we discuss these issues and explore how new open questions motivated by data science may in turn necessitate new statistical theory and methods now and in the future.

Together, the ubiquity of sensing devices, the low cost of data storage, and the commoditization of computing have led to a volume and variety of modern data sets that would have been unthinkable even a decade ago. We see four important implications for statistics.

First, many modern data sets are related in some way to human behavior. Data might have been collected by interacting with human beings, or personal or private information traceable back to a given set of individuals might have been handled at some stage. Mathematical or theoretical statistics traditionally does not concern itself with the finer points of human behavior, and indeed many of us have only had limited training in the rules and regulations that pertain to data derived from human subjects. Yet inevitably in a data-rich world, our technical developments cannot be divorced from the types of data sets we can collect and analyze, and how we can handle and store them.

Second, the importance of data to our economies and civil societies means that the future of regulation will look not only to protect our privacy, and how we store information about ourselves, but also to include what we are allowed to do with that data. For example, as we collect high-dimensional vectors about many family units across time and space in a given region or country, privacy will be limited by that high-dimensional space, but our wish to control what we do with data will go beyond that….

Third, the growing complexity of algorithms is matched by an increasing variety and complexity of data. Data sets now come in a variety of forms that can be highly unstructured, including images, text, sound, and various other new forms. These different types of observations have to be understood together, resulting in multimodal data, in which a single phenomenon or event is observed through different types of measurement devices. Rather than having one phenomenon corresponding to single scalar values, a much more complex object is typically recorded. This could be a three-dimensional shape, for example in medical imaging, or multiple types of recordings such as functional magnetic resonance imaging and simultaneous electroencephalography in neuroscience. Data science therefore challenges us to describe these more complex structures, modeling them in terms of their intrinsic patterns.

Finally, the types of data sets we now face are far from satisfying the classical statistical assumptions of identically distributed and independent observations. Observations are often “found” or repurposed from other sampling mechanisms, rather than necessarily resulting from designed experiments….

 Our field will either meet these challenges and become increasingly ubiquitous, or risk rapidly becoming irrelevant to the future of data science and artificial intelligence….(More)”.

What if technology could help improve conversations online?


Introduction to “Perspective”: “Discussing things you care about can be difficult. The threat of abuse and harassment online means that many people stop expressing themselves and give up on seeking different opinions….Perspective is an API that makes it easier to host better conversations. The API uses machine learning models to score the perceived impact a comment might have on a conversation. Developers and publishers can use this score to give realtime feedback to commenters or help moderators do their job, or allow readers to more easily find relevant information, as illustrated in two experiments below. We’ll be releasing more machine learning models later in the year, but our first model identifies whether a comment could be perceived as “toxic” to a discussion….(More)”.

Can Crowdsourcing and Collaboration Improve the Future of Human Health?


Ben Wiegand at Scientific American: “The process of medical research has been likened to searching for a needle in a haystack. With the continued acceleration of novel science and health care technologies in areas like artificial intelligence, digital therapeutics and the human microbiome we have tremendous opportunity to search the haystack in new and exciting ways. Applying these high-tech advances to today’s most pressing health issues increases our ability to address the root cause of disease, intervene earlier and change the trajectory of human health.

Global crowdsourcing forums, like the Johnson & Johnson Innovation QuickFire Challenges, can be incredibly valuable tools for searching the “haystack.” An initiative of JLABS—the no-strings-attached incubators of Johnson & Johnson Innovation—these contests spur scientific diversity through crowdsourcing, inspiring and attracting fresh thinking. They seek to stimulate the global innovation ecosystem through funding, mentorship and access to resources that can kick-start breakthrough ideas.

Our most recent challenge, the Next-Gen Baby Box QuickFire Challenge, focused on updating the 80-year-old “Finnish baby box,” a free, government-issued maternity supply kit for new parents containing such essentials as baby clothing, bath and sleep supplies packaged in a sleep-safe cardboard box. Since it first launched, the baby box has, together with increased use of maternal healthcare services early in pregnancy, helped to significantly reduce the Finnish infant mortality rate from 65 in every 1,000 live births in the 1930s to 2.5 per 1,000 today—one of the lowest rates in the world.

Partnering with Finnish innovation and government groups, we set out to see if updating this popular early parenting tool with the power of personalized health technology might one day impact Finland’s unparalleled high rate of type 1 diabetes. We issued the call globally to help create “the Baby Box of the future” as part of the Janssen and Johnson & Johnson Innovation vision to create a world without disease by accelerating science and delivering novel solutions to prevent, intercept and cure disease. The contest brought together entrepreneurs, researchers and innovators to focus on ideas with the potential to promote child health, detect childhood disease earlier and facilitate healthy parenting.

Incentive challenges like this award participants who have most effectively met a predefined objective or task. It’s a concept that emerged well before our time—as far back as the 18th century—from Napoleon’s Food Preservation Prize, meant to find a way to keep troops fed during battle, to the Longitude Prize for improved marine navigation.

Research shows that prize-based challenges that attract talent across a wide range of disciplines can generate greater risk-taking and yield more dramatic solutions….(More)”.

An AI That Reads Privacy Policies So That You Don’t Have To


Andy Greenberg at Wired: “…Today, researchers at Switzerland’s Federal Institute of Technology at Lausanne (EPFL), the University of Wisconsin and the University of Michigan announced the release of Polisis—short for “privacy policy analysis”—a new website and browser extension that uses their machine-learning-trained app to automatically read and make sense of any online service’s privacy policy, so you don’t have to.

In about 30 seconds, Polisis can read a privacy policy it’s never seen before and extract a readable summary, displayed in a graphic flow chart, of what kind of data a service collects, where that data could be sent, and whether a user can opt out of that collection or sharing. Polisis’ creators have also built a chat interface they call Pribot that’s designed to answer questions about any privacy policy, intended as a sort of privacy-focused paralegal advisor. Together, the researchers hope those tools can unlock the secrets of how tech firms use your data that have long been hidden in plain sight….

Polisis isn’t actually the first attempt to use machine learning to pull human-readable information out of privacy policies. Both Carnegie Mellon University and Columbia have made their own attempts at similar projects in recent years, points out NYU Law Professor Florencia Marotta-Wurgler, who has focused her own research on user interactions with terms of service contracts online. (One of her own studies showed that only .07 percent of users actually click on a terms of service link before clicking “agree.”) The Usable Privacy Policy Project, a collaboration that includes both Columbia and CMU, released its own automated tool to annotate privacy policies just last month. But Marotta-Wurgler notes that Polisis’ visual and chat-bot interfaces haven’t been tried before, and says the latest project is also more detailed in how it defines different kinds of data. “The granularity is really nice,” Marotta-Wurgler says. “It’s a way of communicating this information that’s more interactive.”…(More)”.

Artificial Intelligence and Foreign Policy


Paper by Ben ScottStefan Heumann and Philppe Lorenz: “The plot-lines of the development of Artificial Intelligence (AI) are debated and contested. But it is safe to predict that it will become one of the central technologies of the 21st century. It is fashionable these days to speak about data as the new oil. But if we want to “refine” the vast quantities of data we are collecting today and make sense of it, we will need potent AI. The consequences of the AI revolution could not be more far reaching. Value chains will be turned upside down, labor markets will get disrupted and economic power will shift to those who control this new technology. And as AI is deeply embedded in the connectivity of the Internet, the challenge of AI is global in nature. Therefore it is striking that AI is almost absent from the foreign policy agenda.

This paper seeks to provide a foundation for planning a foreign policy strategy that responds effectively to the emerging power of AI in international affairs. The developments in AI are so dynamic and the implications so wide-ranging that ministries need to begin engaging immediately. That means starting with the assets and resources at hand while planning for more significant changes in the future. Many of the tools of traditional diplomacy can be adapted to this new field. While the existing toolkit can get us started, this pragmatic approach does not preclude thinking about more drastic changes that the technological changes might require for our foreign policy institutions and instruments.

The paper approaches this challenge, drawing on the existing foreign policy toolbox and reflecting on the past lessons of adapting this toolbox to the Internet revolution. The paper goes on to make suggestions on how the tools could be applied to the international challenges that the AI revolution will bring about. The toolbox includes policy making, public diplomacy, bilateral and multilateral engagement, actions through international and treaty organizations, convenings and partnerships, grant-making and information-gathering and analysis. The analysis of the international challenges of the AI transformation are divided into three topical areas. Each of the three sections includes concrete suggestions how instruments from the tool box could be applied to address the challenges AI will bring about in international affairs….(More)“.

Artificial intelligence and privacy


Report by the The Norwegian Data Protection Authority (DPA): “…If people cannot trust that information about them is being handled properly, it may limit their willingness to share information – for example with their doctor, or on social media. If we find ourselves in a situation in which sections of the population refuse to share information because they feel that their personal integrity is being violated, we will be faced with major challenges to our freedom of speech and to people’s trust in the authorities.

A refusal to share personal information will also represent a considerable challenge with regard to the commercial use of such data in sectors such as the media, retail trade and finance services.

About the report

This report elaborates on the legal opinions and the technologies described in the 2014 report «Big Data – privacy principles under pressure». In this report we will provide greater technical detail in describing artificial intelligence (AI), while also taking a closer look at four relevant AI challenges associated with the data protection principles embodied in the GDPR:

  • Fairness and discrimination
  • Purpose limitation
  • Data minimisation
  • Transparency and the right to information

This represents a selection of data protection concerns that in our opinion are most relevance for the use of AI today.

The target group for this report consists of people who work with, or who for other reasons are interested in, artificial intelligence. We hope that engineers, social scientists, lawyers and other specialists will find this report useful….(More) (Download Report)”.

Earth Observation Open Science and Innovation


Open Access book edited by Pierre-Philippe Mathieu and Christoph Aubrecht: “Over  the  past  decades,  rapid developments in digital and sensing technologies, such  as the Cloud, Web and Internet of Things, have dramatically changed the way we live and work. The digital transformation is revolutionizing our ability to monitor our planet and transforming the  way we access, process and exploit Earth Observation data from satellites.

This book reviews these megatrends and their implications for the Earth Observation community as well as the wider data economy. It provides insight into new paradigms of Open Science and Innovation applied to space data, which are characterized by openness, access to large volume of complex data, wide availability of new community tools, new techniques for big data analytics such as Artificial Intelligence, unprecedented level of computing power, and new types of collaboration among researchers, innovators, entrepreneurs and citizen scientists. In addition, this book aims to provide readers with some reflections on the future of Earth Observation, highlighting through a series of use cases not just the new opportunities created by the New Space revolution, but also the new challenges that must be addressed in order to make the most of the large volume of complex and diverse data delivered by the new generation of satellites….(More)”.