The world’s first blockchain-powered elections just happened in Sierra Leone

Curated on Posted on by Stefaan Verhulst

Yomi Kazeem in Quartz: “On Mar. 7, elections in Sierra Leone marked a global landmark: the world’s first ever blockchain-powered presidential elections….

In Sierra Leone’s Western District, the most populous in the country, votes cast were manually recorded by Agora, a Swiss foundation offering digital voting solutions, using a permissioned blockchain. The idea was simple: just like blockchain technology helps ensure transparency with crytpocurrency transactions using public ledgers, by recording each vote on blockchain, Agora ensured transparency with votes cast in the district. While entries on permissioned blockchains can be viewed by everyone, entries can only be validated by authorized persons.

A lack of transparency has plagued many elections around the world, but particularly in some African countries where large sections of the electorate are often suspicions incumbent parties or ethnic loyalties have been responsible for the manipulation of the results in favor of one candidate or another. These suspicions remain even when there is little evidence of manipulation. A more transparent system could help restore trust.

Leonardo Gammar, CEO of Agora, says Sierra Leone’s NEC was “open minded” about the potential of blockchain in its elections after talks began late last year. “I also thought that if we can do it in Sierra Leone, we can do it everywhere else,” he says. That thinking is rooted in Sierra Leone’s developmental challenges which make electoral transparency difficult: poor network connectivity, low literacy levels and frequent electoral violence.

The big picture for Agora is to deploy solutions to automate the entire electoral process with citizens voting electronically using biometric data and personalized cryptographic keys and the votes in turn validated by blockchain. Gammar hopes Agora can replicate its work in other African elections on a larger scale but admits that doing so will require understanding the differing challenges each country faces.

Gammar says blockchain-powered electronic voting will be cheaper for African countries by cutting out the printing cost of paper-based elections but perhaps, more importantly, vastly reduce electoral violence…(More)”.

NASA’s Asteroid Grand Challenge: Strategy, results, and lessons learned

Curated on Posted on by Stefaan Verhulst

Jennifer L. Gustetic et al in Space Policy: “Beginning in 2012, NASA utilized a strategic process to identify broad societal questions, or grand challenges, that are well suited to the aerospace sector and align with national priorities. This effort generated NASA’s first grand challenge, the Asteroid Grand Challenge (AGC), a large-scale effort using multi-disciplinary collaborations and innovative engagement mechanisms focused on finding and addressing asteroid threats to human populations. In April 2010, President Barack Obama announced a mission to send humans to an asteroid by 2025. This resulted in the agency’s Asteroid Redirect Mission (ARM) to leverage and maximize existing robotic and human efforts to capture and reroute an asteroid, with the goal of eventual human exploration. The AGC, initiated in 2013, complemented ARM by expanding public participation, partnerships, and other approaches to find, understand, and overcome these potentially harmful asteroids.

This paper describes a selection of AGC activities implemented from 2013 to 2017 and their results, excluding those conducted by NASA’s Near-Earth Object Observations Program and other organizations. The strategic development of the initiative is outlined as well as initial successes, strengths, and weaknesses resulting from the first four years of AGC activities and approaches. Finally, we describe lesson learned and areas for continued work and study. The AGC lessons learned and strategies could inform the work of other agencies and organizations seeking to conduct a global scientific investigation with matrixed organizational support, multiple strategic partners, and numerous internal and external open innovation approaches and audiences….(More)”.

 

Exploring the Motives of Citizen Reporting Engagement: Self-Concern and Other-Orientation

Curated on Posted on by Stefaan Verhulst

Paper by Gabriel Abu-Tayeh, Oliver Neumann and Matthias Stuermer: “In smart city contexts, voluntary citizen reporting can be a particularly valuable source of information for local authorities. A key question in this regard is what motivates citizens to contribute their data. Drawing on motivation research in social psychology, the paper examines the question of whether self-concern or other-orientation is a stronger driver of citizen reporting engagement.

To test their hypotheses, the authors rely on a sample of users from the mobile application “Zurich as good as new” in Switzerland, which enables citizens to report damages in and other issues with the city’s infrastructure. Data was collected from two different sources: motivation was assessed in an online user survey (n = 650), whereas citizen reporting engagement was measured by the number of reports per user from real platform-use data. The analysis was carried out using negative binomial regression.

The findings suggest that both self-concern and other-orientation are significant drivers of citizen reporting engagement, although the effect of self-concern appears to be stronger in comparison. As such, this study contributes to a better understanding of what motivates citizens to participate in citizen reporting platforms, which are a cornerstone application in many smart cities….(More)”.

Artificial intelligence could identify gang crimes—and ignite an ethical firestorm

Curated on Posted on by Stefaan Verhulst

Matthew Hutson at Science: “When someone roughs up a pedestrian, robs a store, or kills in cold blood, police want to know whether the perpetrator was a gang member: Do they need to send in a special enforcement team? Should they expect a crime in retaliation? Now, a new algorithm is trying to automate the process of identifying gang crimes. But some scientists warn that far from reducing gang violence, the program could do the opposite by eroding trust in communities, or it could brand innocent people as gang members.

That has created some tensions. At a presentation of the new program this month, one audience member grew so upset he stormed out of the talk, and some of the creators of the program have been tight-lipped about how it could be used….

For years, scientists have been using computer algorithms to map criminal networks, or to guess where and when future crimes might take place, a practice known as predictive policing. But little work has been done on labeling past crimes as gang-related.

In the new work, researchers developed a system that can identify a crime as gang-related based on only four pieces of information: the primary weapon, the number of suspects, and the neighborhood and location (such as an alley or street corner) where the crime took place. Such analytics, which can help characterize crimes before they’re fully investigated, could change how police respond, says Doug Haubert, city prosecutor for Long Beach, California, who has authored strategies on gang prevention.

To classify crimes, the researchers invented something called a partially generative neural network. A neural network is made of layers of small computing elements that process data in a way reminiscent of the brain’s neurons. A form of machine learning, it improves based on feedback—whether its judgments were right. In this case, researchers trained their algorithm using data from the Los Angeles Police Department (LAPD) in California from 2014 to 2016 on more than 50,000 gang-related and non–gang-related homicides, aggravated assaults, and robberies.

The researchers then tested their algorithm on another set of LAPD data. The network was “partially generative,” because even when it did not receive an officer’s narrative summary of a crime, it could use the four factors noted above to fill in that missing information and then use all the pieces to infer whether a crime was gang-related. Compared with a stripped-down version of the network that didn’t use this novel approach, the partially generative algorithm reduced errors by close to 30%, the team reported at the Artificial Intelligence, Ethics, and Society (AIES) conference this month in New Orleans, Louisiana. The researchers have not yet tested their algorithm’s accuracy against trained officers.

It’s an “interesting paper,” says Pete Burnap, a computer scientist at Cardiff University who has studied crime data. But although the predictions could be useful, it’s possible they would be no better than officers’ intuitions, he says. Haubert agrees, but he says that having the assistance of data modeling could sometimes produce “better and faster results.” Such analytics, he says, “would be especially useful in large urban areas where a lot of data is available.”…(More).

Informed Diet Selection: Increasing Food Literacy through Crowdsourcing

Curated on Posted on by Stefaan Verhulst

Paper by Niels van Berkel et al: “The obesity epidemic is one of the greatest threats to health and wellbeing throughout much of the world. Despite information on healthy lifestyles and eating habits being more accessible than ever before, the situation seems to be growing worse  And for a person who wants to lose weight there are practically unlimited options and temptations to choose from. Food, or dieting, is a booming business, and thousands of companies and vendors want their cut by pitching their solutions, particularly online (Google) where people first turn to find weight loss information. In our work, we have set to harness the wisdom of crowds in making sense of available diets, and to offer a direct way for users to increase their food literacy during diet selection.  The Diet Explorer is a crowd-powered online knowledge base that contains an arbitrary number of weight loss diets that are all assessed in terms of an arbitrary set of criteria…(More)”.

Citicafe: conversation-based intelligent platform for citizen engagement

Curated on Posted on by Stefaan Verhulst

Paper by Amol Dumrewal et al in the Proceedings of the ACM India Joint International Conference on Data Science and Management of Data: “Community civic engagement is a new and emerging trend in urban cities driven by the mission of developing responsible citizenship. The recognition of civic potential in every citizen goes a long way in creating sustainable societies. Technology is playing a vital role in helping this mission and over the last couple of years, there have been a plethora of social media avenues to report civic issues. Sites like Twitter, Facebook, and other online portals help citizens to report issues and register complaints. These complaints are analyzed by the public services to help understand and in-turn address these issues. However, once the complaint is registered, often no formal or informal feedback is given back from these sites to the citizens. This de-motivates citizens and may deter them from registering further complaints. In addition, these sites offer no holistic information about a neighborhood to the citizens. It is useful for people to know whether there are similar complaints posted by other people in the same area, the profile of all complaints and a know-how of how and when these complaints will be addressed.

In this paper, we create a conversation-based platform CitiCafe for enhancing citizen engagement front-ended by a virtual agent with a Twitter interface. This platform back-end stores and processes information pertaining to civic complaints in a city. A Twitter based conversation service allows citizens to have a direct correspondence with CitiCafe via “tweets” and direct messages. The platform also helps citizens to (a) report problems and (b) gather information related to civic issues in different neighborhoods. This can also help, in the long run, to develop civic conversations among citizens and also between citizens and public services….(More)”.

Epistemic Public Reason: A Formal Model of Strategic Communication and Deliberative Democracy

Curated on Posted on by Stefaan Verhulst

Paper by Brian Kogelmann and Benjamin Ogden: “Epistemic democrats argue that democratic institutions are uniquely suited to select optimal or good policies. Part of why this is so is due to the role deliberation plays in a well-functioning democracy. Yet deliberative democrats disagree about how democratic discourse ought to proceed. Thus, it is unclear what kind of deliberation the epistemic democratic thinks will aid in the selection of good policies.

This paper remedies this lacuna by developing a game theoretic model of competing theories of deliberative democracy found in the literature – what we broadly call shared discourse and open discourse. The model finds that there is a genuine trade-off between the two theories. Open discourse gives too much power to the (potentially arbitrary) first mover, while closed discourse has a tendency to over-implement potentially unjust reforms. We believe these results ought to shift where deliberative democrats focus their attention when debating which theory of democratic discourse is best…(More)”.

Data-Driven Regulation and Governance in Smart Cities

Curated on Posted on by 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)”.

Prediction, Judgment and Complexity

Curated on Posted on by Stefaan Verhulst

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)”.

No One Owns Data

Curated on Posted on by Stefaan Verhulst

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)”.