Paper by Harry Surden: “Much has been written recently about artificial intelligence (AI) and law. But what is AI, and what is its relation to the practice and administration of law? This article addresses those questions by providing a high-level overview of AI and its use within law. The discussion aims to be nuanced but also understandable to those without a technical background. To that end, I first discuss AI generally. I then turn to AI and how it is being used by lawyers in the practice of law, people and companies who are governed by the law, and government officials who administer the law. A key motivation in writing this article is to provide a realistic, demystified view of AI that is rooted in the actual capabilities of the technology. This is meant to contrast with discussions about AI and law that are decidedly futurist in nature…(More)”.
Law as Data: Computation, Text, and the Future of Legal Analysis
Book edited by Michael A. Livermore and Daniel N. Rockmore: “In recent years, the digitization of legal texts, combined with developments in the fields of statistics, computer science, and data analytics, have opened entirely new approaches to the study of law. This volume explores the new field of computational legal analysis, an approach marked by its use of legal texts as data. The emphasis herein is work that pushes methodological boundaries, either by using new tools to study longstanding questions within legal studies or by identifying new questions in response to developments in data availability and analysis.
By using the text and underlying data of legal documents as the direct objects of quantitative statistical analysis, Law as Data introduces the legal world to the broad range of computational tools already proving themselves relevant to law scholarship and practice, and highlights the early steps in what promises to be an exciting new approach to studying the law….(More)”.
The plan to mine the world’s research papers
Priyanka Pulla in Nature: “Carl Malamud is on a crusade to liberate information locked up behind paywalls — and his campaigns have scored many victories. He has spent decades publishing copyrighted legal documents, from building codes to court records, and then arguing that such texts represent public-domain law that ought to be available to any citizen online. Sometimes, he has won those arguments in court. Now, the 60-year-old American technologist is turning his sights on a new objective: freeing paywalled scientific literature. And he thinks he has a legal way to do it.
Over the past year, Malamud has — without asking publishers — teamed up with Indian researchers to build a gigantic store of text and images extracted from 73 million journal articles dating from 1847 up to the present day. The cache, which is still being created, will be kept on a 576-terabyte storage facility at Jawaharlal Nehru University (JNU) in New Delhi. “This is not every journal article ever written, but it’s a lot,” Malamud says. It’s comparable to the size of the core collection in the Web of Science database, for instance. Malamud and his JNU collaborator, bioinformatician Andrew Lynn, call their facility the JNU data depot.
No one will be allowed to read or download work from the repository, because that would breach publishers’ copyright. Instead, Malamud envisages, researchers could crawl over its text and data with computer software, scanning through the world’s scientific literature to pull out insights without actually reading the text.
The unprecedented project is generating much excitement because it could, for the first time, open up vast swathes of the paywalled literature for easy computerized analysis. Dozens of research groups already mine papers to build databases of genes and chemicals, map associations between proteins and diseases, and generate useful scientific hypotheses. But publishers control — and often limit — the speed and scope of such projects, which typically confine themselves to abstracts, not full text. Researchers in India, the United States and the United Kingdom are already making plans to use the JNU store instead. Malamud and Lynn have held workshops at Indian government laboratories and universities to explain the idea. “We bring in professors and explain what we are doing. They get all excited and they say, ‘Oh gosh, this is wonderful’,” says Malamud.
But the depot’s legal status isn’t yet clear. Malamud, who contacted several intellectual-property (IP) lawyers before starting work on the depot, hopes to avoid a lawsuit. “Our position is that what we are doing is perfectly legal,” he says. For the moment, he is proceeding with caution: the JNU data depot is air-gapped, meaning that no one can access it from the Internet. Users have to physically visit the facility, and only researchers who want to mine for non-commercial purposes are currently allowed in. Malamud says his team does plan to allow remote access in the future. “The hope is to do this slowly and deliberately. We are not throwing this open right away,” he says….(More)”.
What Restaurant Reviews Reveal About Cities
Linda Poon at CityLab: “Online review sites can tell you a lot about a city’s restaurant scene, and they can reveal a lot about the city itself, too.
Researchers at MIT recently found that information about restaurants gathered from popular review sites can be used to uncover a number of socioeconomic factors of a neighborhood, including its employment rates and demographic profiles of the people who live, work, and travel there.
A report published last week in the Proceedings of the National Academy of Sciences explains how the researchers used information found on Dianping—a Yelp-like site in China—to find information that might usually be gleaned from an official government census. The model could prove especially useful for gathering information about cities that don’t have that kind of reliable or up-to-date government data, especially in developing countries with limited resources to conduct regular surveys….
Zheng and her colleagues tested out their machine-learning model using restaurant data from nine Chinese cities of various sizes—from crowded ones like Beijing, with a population of more than 10 million, to smaller ones like Baoding, a city of fewer than 3 million people.
They pulled data from 630,000 restaurants listed on Dianping, including each business’s location, menu prices, opening day, and customer ratings. Then they ran it through a machine-learning model with official census data and with anonymous location and spending data gathered from cell phones and bank cards. By comparing the information, they were able to determine where the restaurant data reflected the other data they had about neighborhoods’ characteristics.
They found that the local restaurant scene can predict, with 95 percent accuracy, variations in a neighborhood’s daytime and nighttime populations, which are measured using mobile phone data. They can also predict, with 90 and 93 percent accuracy, respectively, the number of businesses and the volume of consumer consumption. The type of cuisines offered and kind of eateries available (coffeeshop vs. traditional teahouses, for example), can also predict the proportion of immigrants or age and income breakdown of residents. The predictions are more accurate for neighborhoods near urban centers as opposed to those near suburbs, and for smaller cities, where neighborhoods don’t vary as widely as those in bigger metropolises….(More)”.
Smart Cities in Application: Healthcare, Policy, and Innovation
Book edited by Stan McClellan: “This book explores categories of applications and driving factors surrounding the Smart City phenomenon. The contributing authors provide perspective on the Smart Cities, covering numerous applications and classes of applications. The book uses a top-down exploration of the driving factors in Smart Cities, by including focal areas including “Smart Healthcare,” “Public Safety & Policy Issues,” and “Science, Technology, & Innovation.” Contributors have direct and substantive experience with important aspects of Smart Cities and discuss issues with technologies & standards, roadblocks to implementation, innovations that create new opportunities, and other factors relevant to emerging Smart City infrastructures….(More)”.
Review into bias in algorithmic decision-making
Interim Report by the Centre for Data Ethics and Innovation (UK): The use of algorithms has the potential to improve the quality of decision- making by increasing the speed and accuracy with which decisions are made. If designed well, they can reduce human bias in decision-making processes. However, as the volume and variety of data used to inform decisions increases, and the algorithms used to interpret the data become more complex, concerns are growing that without proper oversight, algorithms risk entrenching and potentially worsening bias.
The way in which decisions are made, the potential biases which they are subject to and the impact these decisions have on individuals are highly context dependent. Our Review focuses on exploring bias in four key sectors: policing, financial services, recruitment and local government. These have been selected because they all involve significant decisions being made about individuals, there is evidence of the growing uptake of machine learning algorithms in the sectors and there is evidence of historic bias in decision-making within these sectors. This Review seeks to answer three sets of questions:
- Data: Do organisations and regulators have access to the data they require to adequately identify and mitigate bias?
- Tools and techniques: What statistical and technical solutions are available now or will be required in future to identify and mitigate bias and which represent best practice?
- Governance: Who should be responsible for governing, auditing and assuring these algorithmic decision-making systems?
Our work to date has led to some emerging insights that respond to these three sets of questions and will guide our subsequent work….(More)”.
Studying Crime and Place with the Crime Open Database
M. P. J. Ashby in Research Data Journal for the Humanities and Social Sciences: “The study of spatial and temporal crime patterns is important for both academic understanding of crime-generating processes and for policies aimed at reducing crime. However, studying crime and place is often made more difficult by restrictions on access to appropriate crime data. This means understanding of many spatio-temporal crime patterns are limited to data from a single geographic setting, and there are few attempts at replication. This article introduces the Crime Open Database (code), a database of 16 million offenses from 10 of the largest United States cities over 11 years and more than 60 offense types. Open crime data were obtained from each city, having been published in multiple incompatible formats. The data were processed to harmonize geographic co-ordinates, dates and times, offense categories and location types, as well as adding census and other geographic identifiers. The resulting database allows the wider study of spatio-temporal patterns of crime across multiple US cities, allowing greater understanding of variations in the relationships between crime and place across different settings, as well as facilitating replication of research….(More)”.
Governing Smart Data in the Public Interest: Lessons from Ontario’s Smart Metering Entity
Paper by Teresa Scassa and Merlynda Vilain: “The collection of vast quantities of personal data from embedded sensors is increasingly an aspect of urban life. This type of data collection is a feature of so-called smart cities, and it raises important questions about data governance. This is particularly the case where the data may be made available for reuse by others and for a variety of purposes.
This paper focuses on the governance of data captured through “smart” technologies and uses Ontario’s smart metering program as a case study. Ontario rolled out mandatory smart metering for electrical consumption in the early 2000s largely to meet energy conservation goals. In doing so, it designed a centralized data governance system overseen by the Smart Metering Entity to manage smart meter data and to protect consumer privacy. As interest in access to the data grew among third parties, and as new potential applications for the data emerged, the regulator sought to develop a model for data sharing that would protect privacy in relation to these new uses and that would avoid uses that might harm the public interest…(More)”.
Stop Surveillance Humanitarianism
Mark Latonero at The New York Times: “A standoff between the United Nations World Food Program and Houthi rebels in control of the capital region is threatening the lives of hundreds of thousands of civilians in Yemen.
Alarmed by reports that food is being diverted to support the rebels, the aid program is demanding that Houthi officials allow them to deploy biometric technologies like iris scans and digital fingerprints to monitor suspected fraud during food distribution.
The Houthis have reportedly blocked food delivery, painting the biometric effort as an intelligence operation, and have demanded access to the personal data on beneficiaries of the aid. The impasse led the aid organization to the decision last month to suspend food aid to parts of the starving population — once thought of as a last resort — unless the Houthis allow biometrics.
With program officials saying their staff is prevented from doing its essential jobs, turning to a technological solution is tempting. But biometrics deployed in crises can lead to a form of surveillance humanitarianism that can exacerbate risks to privacy and security.
By surveillance humanitarianism, I mean the enormous data collection systems deployed by aid organizations that inadvertently increase the vulnerability of people in urgent need….(More)”.
The Governance Turn in Information Privacy Law
Paper by Jane K. Winn: “The governance turn in information privacy law is a turn away from a model of bureaucratic administration of individual control rights and toward a model of collaborative governance of shared interests in information. Collaborative information governance has roots in the American pragmatic philosophy of Peirce, James and Dewey and the 1973 HEW Report that rejected unilateral individual control rights, recognizing instead the essential characteristic of mutuality of shared purposes that are mediated through information governance. America’s current information privacy law regime consists of market mechanisms supplemented by sector-specific, risk-based laws designed to foster a culture of compliance. Prior to the GDPR, data protection law compliance in Europe was more honored in the breach than the observance, so the EU’s strengthening of its bureaucratic individual control rights model reveals more about the EU’s democratic deficit than a commitment to compliance.
The conventional “Europe good, America bad” wisdom about information privacy law obscures a paradox: if the focus shifts from what “law in the books” says to what “law in action” does, it quickly becomes apparent that American businesses lead the world with their efforts to comply with information privacy law, so “America good, Europe bad” might be more accurate. Creating a federal legislative interface through which regulators and voluntary, consensus standards organizations can collaborate could break the current political stalemate triggered by California’s 2018 EU-style information privacy law. Such a pragmatic approach to information governance can safeguard Americans’ continued access to the benefits of innovation and economic growth as well as providing risk-based protection from harm. America can preserve its leadership of the global information economy by rejecting EU-style information privacy laws and building instead a flexible, dynamic framework of information governance capable of addressing both privacy and disclosure issues simultaneously….(More)”.