Stefaan Verhulst

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)”.

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)”.

Paper by Lothar Determann: “Businesses, policy makers, and scholars are calling for property rights in data. They currently focus particularly on the vast amounts of data generated by connected cars, industrial machines, artificial intelligence, toys and other devices on the Internet of Things (IoT). This data is personal to numerous parties who are associated with a connected device, for example, the driver of a connected car, its owner and passengers, as well as other traffic participants. Manufacturers, dealers, independent providers of auto parts and services, insurance companies, law enforcement agencies and many others are also interested in this data. Various parties are actively staking their claims to data on the Internet of Things, as they are mining data, the fuel of the digital economy.

Stakeholders in digital markets often frame claims, negotiations and controversies regarding data access as one of ownership. Businesses regularly assert and demand that they own data. Individual data subjects also assume that they own data about themselves. Policy makers and scholars focus on how to redistribute ownership rights to data. Yet, upon closer review, it is very questionable whether data is—or should be—subject to any property rights. This article unambiguously answers the question in the negative, both with respect to existing law and future lawmaking, in the United States as in the European Union, jurisdictions with notably divergent attitudes to privacy, property and individual freedoms….

The article begins with a brief review of the current landscape of the Internet of Things notes explosive growth of data pools generated by connected devices, artificial intelligence, big data analytics tools and other information technologies. Part 1 lays the foundation for examining concrete current legal and policy challenges in the remainder of the article. Part 2 supplies conceptual differentiation and definitions with respect to “data” and “information” as the subject of rights and interests. Distinctions and definitional clarity serve as the basis for examining the purposes and reach of existing property laws in Part 3, including real property, personal property and intellectual property laws. Part 4 analyzes the effect of data-related laws that do not grant property rights. Part 5 examines how the interests of the various stakeholders are protected or impaired by the current framework of data-related laws to identify potential gaps that could warrant additional property rights. Part 6 examines policy considerations for and against property rights in data. Part 7 concludes that no one owns data and no one should own data….(More)”.

Paper by Shivam Gupta in Statistics & Probability Letters: “Quality of life (QoL) is tied to the perception of ‘meaning’. The quest for meaning is central to the human condition, and we are brought in touch with a sense of meaning when we reflect on what we have created, loved, believed in or left as a legacy (Barcaccia, 2013). QoL is associated with multi-dimensional issues and features such as environmental pressure, total water management, total waste management, noise and level of air pollution (Eusuf et al., 2014). A significant amount of data is needed to understand all these dimensions. Such knowledge is necessary to realize the vision of a smart city, which involves the use of data-driven approaches to improve the quality of life of the inhabitants and city infrastructures (Degbelo et al., 2016).

Technologies such as Radio-Frequency Identification (RFID) or the Internet of Things (IoT) are producing a large volume of data. Koh et al. (2015) pointed out that approximately 2.5 quintillion bytes of data are generated every day, and 90 percent of the data in the world has been created in the past two years alone. Managing this large amount of data, and analyzing it efficiently can help making more informed decisions while solving many of the societal challenges (e.g., exposure analysis, disaster preparedness, climate change). As discussed in Goodchild (2016), the attractiveness of big data can be summarized in one word, namely spatial prediction – the prediction of both the where and when.

This article focuses on the 5Vs of big data (volume, velocity, variety, value, veracity). The challenges associated with big data in the context of environmental monitoring at a city level are briefly presented in Section 2. Section 3 discusses the use of statistical methods like Land Use Regression (LUR) and Spatial Simulated Annealing (SSA) as two promising ways of addressing the challenges of big data….(More)”.

Sarah Keating at the BBC: “Singapore has grown from almost nothing in 50 years. And this well-regarded society has been built up, partly, thanks to the power of suggestion….But while Singapore still loves a public campaign, it has moved toward a more nuanced approach of influencing the behaviours of its inhabitants.

Nudging the population isn’t uniquely Singaporean; more than 150 governments across the globe have tried nudging as a better choice. A medical centre in Qatar, for example, managed to increase the uptake of diabetes screening by offering to test people during Ramadan. People were fasting anyway so the hassle of having to not eat before your testing was removed. It was convenient and timely, two key components to a successful nudge.

Towns in Iceland, India and China have trialed ‘floating zebra crossings’ – 3D optical illusions which make the crossings look like they are floating above the ground designed to urge drivers to slow down. And in order to get people to pay their taxes in the UK, people were sent a letter saying that the majority of taxpayers pay their taxes on time which has had very positive results. Using social norms make people want to conform.

In Singapore some of the nudges you come across are remarkably simple. Rubbish bins are placed away from bus stops to separate smokers from other bus users. Utility bills display how your energy consumption compares to your neighbours. Outdoor gyms have been built near the entrances and exits of HDB estates so they are easy to use, available and prominent enough to consistently remind you. Train stations have green and red arrows on the platform indicating where you should stand so as to speed up the alighting process. If you opt to travel at off-peak times (before 0700), your fare is reduced.

And with six out of 10 Singaporeans eating at food courts four or more times a week, getting people to eat healthier is also a priority. As well as the Healthier Dining Programme, some places make it cheaper to take the healthy option. If you’re determined to eat that Fried Bee Hoon at Khoo Teck Puat Hospital, for example, you’re going to have to pay more for it.

The National Steps Challenge, which encourages participants to get exercising using free step counters in exchange for cash and prizes, has been so successful that the programme name has been trademarked. This form of gamifying is one of the more successful ways of engaging users in achieving objectives. Massive queues to collect the free fitness tracker demonstrated the programme’s popularity.

And it’s not just in tangible ways that nudges are being rolled out. Citizens pay into a mandatory savings programme called the Central Provident Fund at a high rate. This can be accessed for healthcare, housing and pensions as a way to get people to save long-term because evidence has shown that people are too short-sighted when it comes to financing their future

And as the government looks to increase the population 30% by 2030, the city-state’s ageing population and declining birth rate is a problem. The Baby Bonus Scheme goes some way to encouraging parents to have more children by offering cash incentives. Introduced in 2001, the scheme means that all Singapore citizens who have a baby get a cash gift as well as a money into a Child Development Account (CDA) which can be used to pay for childcare and healthcare. The more children you have, the more money you get – since March 2016 you get a cash gift of $8,000 SGD (£4,340) for your first child and up to $10,000 (£5,430) for the third and any subsequent children, as well as money into your CDA.

So do people like being nudged? Is there any cultural difference in the way people react to being swayed toward a ‘better’ choice or behaviour? Given the breadth of the international use of behavioural insights, there is relatively little research done into whether people are happy about it….(More)”.

David Serwadda et al in Science: “A growing number of government agencies, funding organizations, and publishers are endorsing the call for increased data sharing, especially in biomedical research, many with an ultimate goal of open data. Open data is among the least restrictive forms of data sharing, in contrast to managed access mechanisms, which typically have terms of use and in some cases oversight by the data generators themselves. But despite an ethically sound rationale and growing support for open data sharing in many parts of the world, concerns remain, particularly among researchers in low- and middle-income countries (LMICs) in Africa, Latin America, and parts of Asia and the Middle East that comprise the Global South. Drawing on our perspective as researchers and ethicists working in the Global South, we see opportunities to improve community engagement, raise awareness, and build capacity, all toward improving research and data sharing involving researchers in LMICs…African scientists have expressed concern that open data compromises national ownership and reopens the gates for “parachute-research” (i.e., Northern researchers absconding with data to their home countries). Other LMIC researchers have articulated fears over free-riding scientists using the data collected by others for their own career advancement …(More)”

A Sandia National Laboratories Report by Zoe N. Gastelum, Meili C. Swanson, Kari Sentz and Christina Rinaudo : “Recent advances in information technology have led to an expansion of crowdsourcing activities that utilize the “power of the people” harnessed via online games, communities of interest, and other platforms to collect, analyze, verify, and provide technological solutions for challenges from a multitude of domains. To related this surge in popularity, the research team developed a taxonomy of crowdsourcing activities as they relate to international nuclear safeguards, evaluated the potential legal and ethical issues surrounding the use of crowdsourcing to support safeguards, and proposed experimental designs to test the capabilities and prospect for the use of crowdsourcing to support nuclear safeguards verification….(More)”.

Stuart W. Shulman et al at Data Driven Journalism: “The rise of political bots brings into sharp focus the role of automated social media accounts in today’s democratic civil society. Events during the Brexit referendum and the 2016 U.S. Presidential election revealed the scale of this issue for the first time to the majority of citizens and policy-makers. At the same time, the deployment of Russian-linked bots designed to promote pro-gun laws in the aftermath of the Florida school shooting demonstrates the state-sponsored, real-time readiness to shape, through information warfare, the dominant narratives on platforms such as Twitter. The regular news reports on these issues lead us to conclude that the foundations of democracy have become threatened by the presence of aggressive and socially disruptive bots, which aim to manipulate online political discourse.

While there is clarity on the various functions that bot accounts can be scripted to perform, as described below, the task of accurately defining this phenomenon and identifying bot accounts remains a challenge. At Texifter, we have endeavoured to bring nuance to this issue through a research project which explores the presence of automated accounts on Twitter. Initially, this project concerned itself with an attempt to identify bots which participated in online conversations around the prevailing cryptocurrency phenomenon. This article is the first in a series of three blog posts produced by the researchers at Texifter that outlines the contemporary phenomenon of Twitter bots….

Bots in their current iteration have a relatively short, albeit rapidly evolving history. Initially constructed with non-malicious intentions, it wasn’t until the late 1990s with the advent of Web 2.0 when bots began to develop a more negative reputation. Although bots have been used maliciously in denial-of-service (DDoS) attacks, spam emails, and mass identity theft, their purpose is not explicitly to incite mayhem.

Before the most recent political events, bots existed in chat rooms, operated as automated customer service agents on websites, and were a mainstay on dating websites. This familiar form of the bot is known to the majority of the general population as a “chatbot” – for instance, CleverBot was and still is a popular platform to talk to an “AI”. Another prominent example was Microsoft’s failed Twitter Chatbot Tay which made headlines in 2016 when “her” vocabulary and conversation functions were manipulated by Twitter users until “she” espoused neo-nazi views when “she” was subsequently deleted.

Image: XKCD Comic #632.

A Twitter bot is an account controlled by an algorithm or script, which is typically hosted on a cloud platform such as Heroku. They are typically, though not exclusively, scripted to conduct repetitive tasks.  For example, there are bots that retweet content containing particular keywords, reply to new followers, and direct messages to new followers; although they can be used for more complex tasks such as participating in online conversations. Bot accounts make up between 9 and 15% of all active accounts on Twitter; however, it is predicted that they account for a much greater percentage of total Twitter traffic. Twitter bots are generally not created with malicious intent; they are frequently used for online chatting or for raising the professional profile of a corporation – but their ability to pervade our online experience and shape political discourse warrants heightened scrutiny….(More)”.

Yaniv Reingewertz and Carmela Lutmar at Harvard Business Review: “Are academic journals impartial? While many would suggest that academic journals work for the advancement of knowledge and science, we show this is not always the case. In a recent study, we find that two international relations (IR) journals favor articles written by authors who share the journal’s institutional affiliation. We term this phenomenon “academic in-group bias.”

In-group bias is a well-known phenomenon that is widely documented in the psychological literature. People tend to favor their group, whether it is their close family, their hometown, their ethnic group, or any other group affiliation. Before our study, the evidence regarding academic in-group bias was scarce, with only one studyfinding academic in-group bias in law journals. Studies from economics found mixedresults. Our paper provides evidence of academic in-group bias in IR journals, showing that this phenomenon is not specific to law. We also provide tentative evidence which could potentially resolve the conflict in economics, suggesting that these journals might also exhibit in-group bias. In short, we show that academic in-group bias is general in nature, even if not necessarily large in scope….(More)”.