Lawless Surveillance

Curated on Posted on by Stefaan Verhulst

Paper by Barry Friedman: “Here in the United States, policing agencies are engaging in mass collection of personal data, building a vast architecture of surveillance. License plate readers collect our location information. Mobile forensics data terminals suck in the contents of cell phones during traffic stops. CCTV maps our movements. Cheap storage means most of this is kept for long periods of time—sometimes into perpetuity. Artificial intelligence makes searching and mining the data a snap. For most of us whose data is collected, stored, and mined, there is no suspicion whatsoever of wrongdoing.

This growing network of surveillance is almost entirely unregulated. It is, in short, lawless. The Fourth Amendment touches almost none of it, either because what is captured occurs in public, and so is supposedly “knowingly exposed,” or because of doctrine that shields information collected from third parties. It is unregulated by statutes because legislative bodies—when they even know about these surveillance systems—see little profit in taking on the police.

In the face of growing concern over such surveillance, this Article argues there is a constitutional solution sitting in plain view. In virtually every other instance in which personal information is collected by the government, courts require that a sound regulatory scheme be in place before information collection occurs. The rulings on the mandatory nature of regulation are remarkably similar, no matter under which clause of the Constitution collection is challenged.

This Article excavates this enormous body of precedent and applies it to the problem of government mass data collection. It argues that before the government can engage in such surveillance, there must be a regulatory scheme in place. And by changing the default rule from allowing police to collect absent legislative prohibition, to banning collection until there is legislative action, legislatures will be compelled to act (or there will be no surveillance). The Article defines what a minimally-acceptable regulatory scheme for mass data collection must include, and shows how it can be grounded in the Constitution…(More)”.

Social capital: measurement and associations with economic mobility

Curated on Posted on by Stefaan Verhulst

Paper by Raj Chetty et al: “Social capital—the strength of an individual’s social network and community—has been identified as a potential determinant of outcomes ranging from education to health. However, efforts to understand what types of social capital matter for these outcomes have been hindered by a lack of social network data. Here, in the first of a pair of papers, we use data on 21 billion friendships from Facebook to study social capital. We measure and analyse three types of social capital by ZIP (postal) code in the United States: (1) connectedness between different types of people, such as those with low versus high socioeconomic status (SES); (2) social cohesion, such as the extent of cliques in friendship networks; and (3) civic engagement, such as rates of volunteering. These measures vary substantially across areas, but are not highly correlated with each other. We demonstrate the importance of distinguishing these forms of social capital by analysing their associations with economic mobility across areas. The share of high-SES friends among individuals with low SES—which we term economic connectedness—is among the strongest predictors of upward income mobility identified to date Other social capital measures are not strongly associated with economic mobility. If children with low-SES parents were to grow up in counties with economic connectedness comparable to that of the average child with high-SES parents, their incomes in adulthood would increase by 20% on average. Differences in economic connectedness can explain well-known relationships between upward income mobility and racial segregation, poverty rates, and inequality. To support further research and policy interventions, we publicly release privacy-protected statistics on social capital by ZIP code at https://www.socialcapital.org…(More)”.

Supporting peace negotiations in the Yemen war through machine learning

Curated on Posted on by Stefaan Verhulst

Paper by Miguel Arana-Catania, Felix-Anselm van Lier and Rob Procter: “Today’s conflicts are becoming increasingly complex, fluid, and fragmented, often involving a host of national and international actors with multiple and often divergent interests. This development poses significant challenges for conflict mediation, as mediators struggle to make sense of conflict dynamics, such as the range of conflict parties and the evolution of their political positions, the distinction between relevant and less relevant actors in peace-making, or the identification of key conflict issues and their interdependence. International peace efforts appear ill-equipped to successfully address these challenges. While technology is already being experimented with and used in a range of conflict related fields, such as conflict predicting or information gathering, less attention has been given to how technology can contribute to conflict mediation. This case study contributes to emerging research on the use of state-of-the-art machine learning technologies and techniques in conflict mediation processes. Using dialogue transcripts from peace negotiations in Yemen, this study shows how machine-learning can effectively support mediating teams by providing them with tools for knowledge management, extraction and conflict analysis. Apart from illustrating the potential of machine learning tools in conflict mediation, the article also emphasizes the importance of interdisciplinary and participatory, cocreation methodology for the development of context-sensitive and targeted tools and to ensure meaningful and responsible implementation…(More)”.

Towards an international data governance framework

Curated on Posted on by Stefaan Verhulst

Paper by Steve MacFeely et al: “The CCSA argued that a Global Data Compact (GDC) could provide a framework to ensure that data are safeguarded as a global public good and as a resource to achieve equitable and sustainable development. This compact, by promoting common objectives, would help avoid fragmentation where each country or region adopts their own approach to data collection, storage, and use. A coordinated approach would give individuals and enterprises confidence that data relevant to them carries protections and obligations no matter where they are collected or used…

The universal principles and standards should set out the elements of responsible and ethical handling and sharing of data and data products. The compact should also move beyond simply establishing ethical principles and create a global architecture that includes standards and incentives for compliance. Such an architecture could be the foundation for rethinking the data economy, promoting open data, encouraging data exchange, fostering innovation and facilitating international trade. It should build upon the existing canon of international human rights and other conventions, laws and treaties that set out useful principles and compliance mechanisms.

Such a compact will require a new type of global architecture. Modern data ecosystems are not controlled by states alone, so any Compact, Geneva Convention, Commons, or Bretton Woods type agreement will require a multitude of stakeholders and signatories – states, civil society, and the private sector at the very least. This would be very different to any international agreement that currently exists. Therefore, to support a GDC, a new global institution or platform may be needed to bring together the many data communities and ecosystems, that comprise not only national governments, private sector and civil society but also participants in specific fields, such as artificial intelligence, digital and IT services. Participants would maintain and update data standards, oversee accountability frameworks, and support mechanisms to facilitate the exchange and responsible use of data. The proposed Global Digital Compact which has been proposed as part of Our Common Agenda will also need to address the challenges of bringing many different constituencies together and may point the way…(More)”

Quantum Computing

Curated on Posted on by Stefaan Verhulst

Introduction by Roman Rietsche: “Quantum computing promises to be the next disruptive technology, with numerous possible applications and implications for organizations and markets. Quantum computers exploit principles of quantum mechanics, such as superposition and entanglement, to represent data and perform operations on them. Both of these principles enable quantum computers to solve very specific, complex problems significantly faster than standard computers. Against this backdrop, this fundamental gives a brief overview of the three layers of a quantum computer: hardware, system software, and application layer. Furthermore, we introduce potential application areas of quantum computing and possible research directions for the field of information systems…(More)”.

Co-Producing Sustainability Research with Citizens: Empirical Insights from Co-Produced Problem Frames with Randomly Selected Citizens

Curated on Posted on by Stefaan Verhulst

Paper by Mareike Blum: “In sustainability research, knowledge co-production can play a supportive role at the science-policy interface (Norström et al., 2020). However, so far most projects involved stakeholders in order to produce ‘useful knowledge’ for policy-makers. As a novel approach, research projects have integrated randomly selected citizens during the knowledge co-production to make policy advice more reflective of societal perspectives and thereby increase its epistemic quality. Researchers are asked to consider citizens’ beliefs and values and integrate these in their ongoing research. This approach rests on pragmatist philosophy, according to which a joint deliberation on value priorities and anticipated consequences of policy options ideally allows to co-develop sustainable and legitimate policy pathways (Edenhofer & Kowarsch, 2015; Kowarsch, 2016). This paper scrutinizes three promises of involving citizens in the problem framing: (1) creating input legitimacy, (2) enabling social learning among citizens and researchers and (3) resulting in high epistemic quality of the co-produced knowledge. Based on empirical data the first phase of two research projects in Germany were analysed and compared: The Ariadne research project on the German Energy Transition, and the Biesenthal Forest project at the local level in Brandenburg, Germany. We found that despite barriers exist; learning was enabled by confronting researchers with problem perceptions of citizens. The step when researchers interpret and translate problem frames in the follow-up knowledge production is most important to assess learning and epistemic quality…(More)”.

The Public Good and Public Attitudes Toward Data Sharing Through IoT

Curated on Posted on by Stefaan Verhulst

Paper by Karen Mossberger, Seongkyung Cho and Pauline Cheong: “The Internet of Things has created a wealth of new data that is expected to deliver important benefits for IoT users and for society, including for the public good. Much of the literature has focused on data collection through individual adoption of IoT devices, and big data collection by companies with accompanying fears of data misuse. While citizens also increasingly produce data as they move about in public spaces, less is known about citizen support for data collection in smart city environments, or for data sharing for a variety of public-regarding purposes. Through a nationally representative survey of over 2,000 respondents as well as interviews, we explore the willingness of citizens to share their data with different parties and in various circumstances, using the contextual integrity framework, the literature on the ‘publicness’ of organizations, and public value creation. We describe the results of the survey across different uses, for data sharing from devices and for data collection in public spaces. We conduct multivariate regression to predict individual characteristics that influence attitudes toward use of IoT data for public purposes. Across different contexts, from half to 2/3 of survey respondents were willing to share data from their own IoT devices for public benefits, and 80-93% supported the use of sensors in public places for a variety of collective benefits. Yet government is less trusted with this data than other organizations with public purposes, such as universities, nonprofits and health care institutions. Trust in government, among other factors, was significantly related to data sharing and support for smart city data collection. Cultivating trust through transparent and responsible data stewardship will be important for future use of IoT data for public good…(More)”.

Trust Based Resolving of Conflicts for Collaborative Data Sharing in Online Social Networks

Curated on Posted on by Stefaan Verhulst

Paper by Nisha P. Shetty et al: “Twenty-first century, the era of Internet, social networking platforms like Facebook and Twitter play a predominant role in everybody’s life. Ever increasing adoption of gadgets such as mobile phones and tablets have made social media available all times. This recent surge in online interaction has made it imperative to have ample protection against privacy breaches to ensure a fine grained and a personalized data publishing online. Privacy concerns over communal data shared amongst multiple users are not properly addressed in most of the social media. The proposed work deals with effectively suggesting whether or not to grant access to the data which is co-owned by multiple users. Conflicts in such scenario are resolved by taking into consideration the privacy risk and confidentiality loss observed if the data is shared. For secure sharing of data, a trust framework based on the user’s interest and interaction parameters is put forth. The proposed work can be extended to any data sharing multiuser platform….(More)”.

Can Artificial Intelligence Improve Gender Equality? Evidence from a Natural Experiment

Curated on Posted on by Stefaan Verhulst

Paper by Zhengyang Bao and Difang Huang: “Difang HuangGender stereotypes and discriminatory practices in the education system are important reasons for women’s under-representation in many fields. How to create a gender-neutral learning environment when teachers’ gender composition and mindset are slow to change? Artificial intelligence (AI)’s recent development provides a way to achieve this goal. Engineers can make AI trainers appear gender neutral and not take gender-related information as input. We use data from a natural experiment where AI trainers replace some human teachers for a male-dominated strategic board game to test the effectiveness of such AI training. The introduction of AI improves boys’ and girls’ performance faster and reduces the pre-existing gender gap. Class recordings suggest that AI trainers’ gender-neutral emotional status can partly explain the improvement in gender quality. We provide the first evidence demonstrating AI’s potential to promote equality for society…(More)”.

Policy Choice and the Wisdom of Crowds

Curated on Posted on by Stefaan Verhulst

Paper by Nicholas Otis: “Using data from seven large-scale randomized experiments, I test whether crowds of academic experts can forecast the relative effectiveness of policy interventions. Eight-hundred and sixty-three academic experts provided 9,295 forecasts of the causal effects from these experiments, which span a diverse set of interventions (e.g., information provision, psychotherapy, soft-skills training), outcomes (e.g., consumption, COVID-19 vaccination, employment), and locations (Jordan, Kenya, Sweden, the United States). For each policy comparisons (a pair of policies and an outcome), I calculate the percent of crowd forecasts that correctly rank policies by their experimentally estimated treatment effects. While only 65% of individual experts identify which of two competing policies will have a larger causal effect, the average forecast from bootstrapped crowds of 30 experts identifies the better policy 86% of the time, or 92% when restricting analysis to pairs of policies who effects differ at the p < 0.10 level. Only 10 experts are needed to produce an 18-percentage point (27%) improvement in policy choice…(More)”.