Paper by Mireille Hildebrandt: “Recommendations are meant to increase sales or ad revenue, as these are the first priority of those who pay for them. As recommender systems match their recommendations with inferred preferences, we should not be surprised if the algorithm optimizes for lucrative preferences and thus co-produces the preferences they mine. This relates to the well-known problems of feedback loops, filter bubbles, and echo chambers. In this article, I discuss the implications of the fact that computing systems necessarily work with proxies when inferring recommendations and raise a number of questions about whether recommender systems actually do what they are claimed to do, while also analysing the often-perverse economic incentive structures that have a major impact on relevant design decisions. Finally, I will explain how the choice architectures for data controllers and providers of AI systems as foreseen in the EU’s General Data Protection Regulation (GDPR), the proposed EU Digital Services Act (DSA) and the proposed EU AI Act will help to break through various vicious circles, by constraining how people may be targeted (GDPR, DSA) and by requiring documented evidence of the robustness, resilience, reliability, and the responsible design and deployment of high-risk recommender systems (AI Act)…(More)”.
Responsiveness of open innovation to COVID-19 pandemic: The case of data for good
Paper by Francesco Scotti, Francesco Pierri, Giovanni Bonaccorsi, and Andrea Flori: “Due to the COVID-19 pandemic, countries around the world are facing one of the most severe health and economic crises of recent history and human society is called to figure out effective responses. However, as current measures have not produced valuable solutions, a multidisciplinary and open approach, enabling collaborations across private and public organizations, is crucial to unleash successful contributions against the disease. Indeed, the COVID-19 represents a Grand Challenge to which joint forces and extension of disciplinary boundaries have been recognized as main imperatives. As a consequence, Open Innovation represents a promising solution to provide a fast recovery. In this paper we present a practical application of this approach, showing how knowledge sharing constitutes one of the main drivers to tackle pressing social needs. To demonstrate this, we propose a case study regarding a data sharing initiative promoted by Facebook, the Data For Good program. We leverage a large-scale dataset provided by Facebook to the research community to offer a representation of the evolution of the Italian mobility during the lockdown. We show that this repository allows to capture different patterns of movements on the territory with increasing levels of detail. We integrate this information with Open Data provided by the Lombardy region to illustrate how data sharing can also provide insights for private businesses and local authorities. Finally, we show how to interpret Data For Good initiatives in light of the Open Innovation Framework and discuss the barriers to adoption faced by public administrations regarding these practices…(More)”.
Information aggregation and collective intelligence beyond the wisdom of crowds
Paper by Tatsuya Kameda, Wataru Toyokawa & R. Scott Tindale: “In humans and other gregarious animals, collective decision-making is a robust behavioural feature of groups. Pooling individual information is also fundamental for modern societies, in which digital technologies have exponentially increased the interdependence of individual group members. In this Review, we selectively discuss the recent human and animal literature, focusing on cognitive and behavioural mechanisms that can yield collective intelligence beyond the wisdom of crowds. We distinguish between two group decision-making situations: consensus decision-making, in which a group consensus is required, and combined decision-making, in which a group consensus is not required. We show that in both group decision-making situations, cognitive and behavioural algorithms that capitalize on individual heterogeneity are the key for collective intelligence to emerge. These algorithms include accuracy or expertise-weighted aggregation of individual inputs and implicit or explicit coordination of cognition and behaviour towards division of labour. These mechanisms can be implemented either as ‘cognitive algebra’, executed mainly within the mind of an individual or by some arbitrating system, or as a dynamic behavioural aggregation through social interaction of individual group members. Finally, we discuss implications for collective decision-making in modern societies characterized by a fluid but auto-correlated flow of information and outline some future directions….(More)”.
Citizen science and environmental justice: exploring contradictory outcomes through a case study of air quality monitoring in Dublin
Paper by Fiadh Tubridy et al: “Citizen science is advocated as a response to a broad range of contemporary societal and ecological challenges. However, there are widely varying models of citizen science which may either challenge or reinforce existing knowledge paradigms and associated power dynamics. This paper explores different approaches to citizen science in the context of air quality monitoring in terms of their implications for environmental justice. This is achieved through a case study of air quality management in Dublin which focuses on the role of citizen science in this context. The evidence shows that the dominant interpretation of citizen science in Dublin is that it provides a means to promote awareness and behaviour change rather than to generate knowledge and inform new regulations or policies. This is linked to an overall context of technocratic governance and the exclusion of non-experts from decision-making. It is further closely linked to neoliberal governance imperatives to individualise responsibility and promote market-based solutions to environmental challenges. Last, the evidence highlights that this model of citizen science risks compounding inequalities by transferring responsibility and blame for air pollution to those who have limited resources to address it. Overall, the paper highlights the need for critical analysis of the implications of citizen science in different instances and for alternative models of citizen science whereby communities would contribute to setting objectives and determining how their data is used…(More)”.
Innovation Indicators
Paper by Fred Gault and Luc Soete: “Innovation indicators support research on innovation and the development of innovation policy. Once a policy has been implemented, innovation indicators can be used to monitor and evaluate the result, leading to policy learning. Producing innovation indicators requires an understanding of what innovation is. There are many definitions in the literature, but innovation indicators are based on statistical measurement guided by international standard definitions of innovation and of innovation activities.
Policymakers are not just interested in the occurrence of innovation but in the outcome. Does it result in more jobs and economic growth? Is it expected to reduce carbon emissions, to advance renewable energy production and energy storage? How does innovation support the Sustainable Development Goals? From the innovation indicator perspective, innovation can be identified in surveys, but that only shows that there is, or there is not, innovation. To meet specific policy needs, a restriction can be imposed on the measurement of innovation. The population of innovators can be divided into those meeting the restriction, such as environmental improvements, and those that do not. In the case of innovation indicators that show a change over time, such as “inclusive innovation,” there may have to be a baseline measurement followed by a later measurement to see if inclusiveness is present, or growing, or not. This may involve social as well as institutional surveys. Once the innovation indicators are produced, they can be made available to potential users through databases, indexes, and scoreboards. Not all of these are based on the statistical measurement of innovation. Some use proxies, such as the allocation of financial and human resources to research and development, or the use of patents and academic publications. The importance of the databases, indexes, and scoreboards is that the findings may be used for the ranking of “innovation” in participating countries, influencing their behavior. While innovation indicators have always been influential, they have the potential to become more so. For decades, innovation indicators have focused on innovation in the business sector, while there have been experiments on measuring innovation in the public (general government sector and public institutions) and the household sectors. Historically, there has been no standard definition of innovation applicable in all sectors of the economy (business, public, household, and non-profit organizations serving households sectors). This changed with the Oslo Manual in 2018, which published a general definition of innovation applicable in all economic sectors. Applying a general definition of innovation has implications for innovation indicators and for the decisions that they influence. If the general definition is applied to the business sector, it includes product innovations that are made available to potential users rather than being introduced on the market. The product innovation can be made available at zero price, which has influence on innovation indicators that are used to describe the digital transformation of the economy. The general definition of innovation, the digital transformation of the economy, and the growing importance of zero price products influence innovation indicators…(More)”.
Guns, Privacy, and Crime
Paper by Alessandro Acquisti & Catherine Tucker: “Open government holds promise of both a more efficient but more accountable and transparent government. It is not clear, however, how transparent information about citizens and their interaction with government, however, affects the welfare of those citizens, and if so in what direction. We investigate this by using as a natural experiment the effect of the online publication of the names and addresses of holders of handgun carry permits on criminals’ propensity to commit burglaries. In December 2008, a Memphis, TN newspaper published a searchable online database of names, zip codes, and ages of Tennessee handgun carry permit holders. We use detailed crime and handgun carry permit data for the city of Memphis to estimate the impact of publicity about the database on burglaries. We find that burglaries increased in zip codes with fewer gun permits, and decreased in those with more gun permits, after the database was publicized….(More)”
The Limitations of Privacy Rights
Paper by Daniel J. Solove: “Individual privacy rights are often at the heart of information privacy and data protection laws. The most comprehensive set of rights, from the European Union’s General Data Protection Regulation (GDPR), includes the right to access, right to rectification (correction), right to erasure, right to restriction, right to data portability, right to object, and right to not be subject to automated decisions. Privacy laws around the world include many of these rights in various forms.
In this article, I contend that although rights are an important component of privacy regulation, rights are often asked to do far more work than they are capable of doing. Rights can only give individuals a small amount of power. Ultimately, rights are at most capable of being a supporting actor, a small component of a much larger architecture. I advance three reasons why rights cannot serve as the bulwark of privacy protection. First, rights put too much onus on individuals when many privacy problems are systematic. Second, individuals lack the time and expertise to make difficult decisions about privacy, and rights cannot practically be exercised at scale with the number of organizations than process people’s data. Third, privacy cannot be protected by focusing solely on the atomistic individual. The personal data of many people is interrelated, and people’s decisions about their own data have implications for the privacy of other people.
The main goal of providing privacy rights aims to provide individuals with control over their personal data. However, effective privacy protection involves not just facilitating individual control, but also bringing the collection, processing, and transfer of personal data under control. Privacy rights are not designed to achieve the latter goal; and they fail at the former goal.
After discussing these overarching reasons why rights are insufficient for the oversized role they currently play in privacy regulation, I discuss the common privacy rights and why each falls short of providing significant privacy protection. For each right, I propose broader structural measures that can achieve its underlying goals in a more systematic, rigorous, and less haphazard way…(More)”.
Transparency of open data ecosystems in smart cities: Definition and assessment of the maturity of transparency in 22 smart cities
Paper by Martin Lnenicka et al: “This paper focuses on the issue of the transparency maturity of open data ecosystems seen as the key for the development and maintenance of sustainable, citizen-centered, and socially resilient smart cities. This study inspects smart cities’ data portals and assesses their compliance with transparency requirements for open (government) data. The expert assessment of 34 portals representing 22 smart cities, with 36 features, allowed us to rank them and determine their level of transparency maturity according to four predefined levels of maturity – developing, defined, managed, and integrated. In addition, recommendations for identifying and improving the current maturity level and specific features have been provided. An open data ecosystem in the smart city context has been conceptualized, and its key components were determined. Our definition considers the components of the data-centric and data-driven infrastructure using the systems theory approach. We have defined five predominant types of current open data ecosystems based on prevailing data infrastructure components. The results of this study should contribute to the improvement of current data ecosystems and build sustainable, transparent, citizen-centered, and socially resilient open data-driven smart cities…(More)”.
Governing AI to Advance Shared Prosperity
Chapter by Ekaterina Klinova: “This chapter describes a governance approach to promoting AI research and development that creates jobs and advances shared prosperity. Concerns over the labor-saving focus of AI advancement are shared by a growing number of economists, technologists, and policymakers around the world. They warn about the risk of AI entrenching poverty and inequality globally. Yet, translating those concerns into proactive governance interventions that would steer AI away from generating excessive levels of automation remains difficult and largely unattempted. Key causes of this difficulty arise from two types of sources: (1) insufficiently deep understanding of the full composition of factors giving AI R&D its present emphasis on labor-saving applications; and (2) lack of tools and processes that would enable AI practitioners and policymakers to anticipate and assess the impact of AI technologies on employment, wages and job quality. This chapter argues that addressing (2) will require creating worker-participatory means of differentiating between genuinely worker-benefiting AI and worker-displacing or worker-exploiting AI. To contribute to tackling (1), this chapter reviews AI practitioners’ motivations and constraints, such as relevant laws, market incentives, as well as less tangible but still highly influential constraining and motivating factors, including explicit and implicit norms in the AI field, visions of future societal order popular among the field’s members and ways that AI practitioners define goals worth pursuing and measure success. I highlight how each of these factors contributes meaningfully to giving AI advancement its excessive labor-saving emphasis and describe opportunities for governance interventions that could correct that over emphasis….(More)”.
Using ANPR data to create an anonymized linked open dataset on urban bustle
Paper by Brecht Van de Vyvere & Pieter Colpaert: “ANPR cameras allow the automatic detection of vehicle license plates and are increasingly used for law enforcement. However, also statistical data generated by ANPR cameras are a potential source of urban insights. In order for this data to reach its full potential for policy-making, we research how this data can be shared in digital twins, with researchers, for a diverse set of machine learning models, and even Open Data portals. This article’s key objective is to find a way to anonymize and aggregate ANPR data in a way that it still can provide useful visualizations for local decision making. We introduce an approach to aggregate the data with geotemporal binning and publish it by combining nine existing data specifications. We implemented the approach for the city of Kortrijk (Belgium) with 43 ANPR cameras, developed the ANPR Metrics tool to generate the statistical data and dashboards on top of the data, and tested whether mobility experts from the city could deduct valuable insights. We present a couple of insights that were found as a result, as a proof that anonymized ANPR data complements their currently used traffic analysis tools, providing a valuable source for data-driven policy-making…(More)”.