Digital divides are lower in Smart Cities

Curated on Posted on by Stefaan Verhulst

Paper by Andrea Caragliu and Chiara F. Del Bo: “Ever since the emergence of digital technologies in the early 1990s, the literature has discussed the potential pitfalls of an uneven distribution of e-skills under the umbrella of the digital divide. To provide a definition of the concept, “Lloyd Morrisett coined the term digital divide to mean “a discrepancy in access to technology resources between socioeconomic groups” (Robyler and Doering, 2014, p. 27)”

Despite digital divide being high on the policy agenda, statistics suggest the persisting relevance of this issue. For instance, focusing on Europe, according to EUROSTAT statistics, in 2021 about 90 per cent of people living in Zeeland, a NUTS2 region in the Netherlands, had ordered at least once in their life goods or services over the internet for private use, against a minimum in the EU27 of 15 per cent (in the region of Yugoiztochen, in Bulgaria). In the same year, while basically all (99 per cent) interviewees in the NUTS2 region of Northern and Western Ireland declared using the internet at least once a week, the same statistic drops to two thirds of the sample in the Bulgarian region of Severozapaden. While over time these territorial divides are converging, they can still significantly affect the potential positive impact of the diffusion of digital technologies.

Over the past three years, the digital divide has been made dramatically apparent by the COVID-19 pandemic outbreak. When, during the first waves of full lockdowns enacted in most Countries, tertiary and schooling activities were moved online, many economic outcomes showed significant worsening. Among these, learning outcomes in pupils and service sectors’ productivity were particularly affected.

A simultaneous development in the scientific literature has discussed the attractive features of planning and managing cities ‘smartly’. Smart Cities have been initially identified as urban areas with a tendency to invest and deploy ICTs. More recently, this notion also started to encompass the context characteristics that make a city capable of reaping the benefits of ICTs – social and human capital, soft and hard institutions.

While mounting empirical evidence suggests a superior economic performance of Cities ticking all these boxes, the Smart City movement did not come without critiques. The debate on urban smartness as an instrument for planning and managing more efficient cities has been recently positing that Smart Cities could be raising inequalities. This effect would be due to the role of driver of smart urban transformations played by multinational corporations, who, in a dystopic view, would influence local policymakers’ agendas.

Given these issues, and our own research on Smart Cities, we started asking ourselves whether the risks of increasing inequalities associated with the Smart City model were substantiated. To this end, we focused on empirically verifying whether cities moving forward along the smart city model were facing increases in income and digital inequalities. We answered the first question in Caragliu and Del Bo (2022), and found compelling evidence that smart city characteristics actually decrease income inequalities…(More)”.

A new way to look at data privacy

Curated on Posted on by Stefaan Verhulst

Article by Adam Zewe: “Imagine that a team of scientists has developed a machine-learning model that can predict whether a patient has cancer from lung scan images. They want to share this model with hospitals around the world so clinicians can start using it in diagnosis.

But there’s a problem. To teach their model how to predict cancer, they showed it millions of real lung scan images, a process called training. Those sensitive data, which are now encoded into the inner workings of the model, could potentially be extracted by a malicious agent. The scientists can prevent this by adding noise, or more generic randomness, to the model that makes it harder for an adversary to guess the original data. However, perturbation reduces a model’s accuracy, so the less noise one can add, the better.

MIT researchers have developed a technique that enables the user to potentially add the smallest amount of noise possible, while still ensuring the sensitive data are protected.

The researchers created a new privacy metric, which they call Probably Approximately Correct (PAC) Privacy, and built a framework based on this metric that can automatically determine the minimal amount of noise that needs to be added. Moreover, this framework does not need knowledge of the inner workings of a model or its training process, which makes it easier to use for different types of models and applications.

In several cases, the researchers show that the amount of noise required to protect sensitive data from adversaries is far less with PAC Privacy than with other approaches. This could help engineers create machine-learning models that provably hide training data, while maintaining accuracy in real-world settings…

A fundamental question in data privacy is: How much sensitive data could an adversary recover from a machine-learning model with noise added to it?

Differential Privacy, one popular privacy definition, says privacy is achieved if an adversary who observes the released model cannot infer whether an arbitrary individual’s data is used for the training processing. But provably preventing an adversary from distinguishing data usage often requires large amounts of noise to obscure it. This noise reduces the model’s accuracy.

PAC Privacy looks at the problem a bit differently. It characterizes how hard it would be for an adversary to reconstruct any part of randomly sampled or generated sensitive data after noise has been added, rather than only focusing on the distinguishability problem…(More)”

Questions as a Device for Data Responsibility: Toward a New Science of Questions to Steer and Complement the Use of Data Science for the Public Good in a Polycentric Way

Curated on Posted on by Stefaan Verhulst

Paper by Stefaan G. Verhulst: “We are at an inflection point today in our search to responsibly handle data in order to maximize the public good while limiting both private and public risks. This paper argues that the way we formulate questions should be given more consideration as a device for modern data responsibility. We suggest that designing a polycentric process for co-defining the right questions can play an important role in ensuring that data are used responsibly, and with maximum positive social impact. In making these arguments, we build on two bodies of knowledge—one conceptual and the other more practical. These observations are supplemented by the author’s own experience as founder and lead of “The 100 Questions Initiative.” The 100 Questions Initiative uses a unique participatory methodology to identify the world’s 100 most pressing, high-impact questions across a variety of domains—including migration, gender inequality, air quality, the future of work, disinformation, food sustainability, and governance—that could be answered by unlocking datasets and other resources. This initiative provides valuable practical insights and lessons into building a new “science of questions” and builds on theoretical and practical knowledge to outline a set of benefits of using questions for data responsibility. More generally, this paper argues that, combined with other methods and approaches, questions can help achieve a variety of key data responsibility goals, including data minimization and proportionality, increasing participation, and enhancing accountability…(More)”.

What types of health evidence persuade actors in a complex policy system?

Curated on Posted on by Stefaan Verhulst

Article by Geoff Bates, Sarah Ayres, Andrew Barnfield, and Charles Larkin: “Good quality urban environments can help to prevent non-communicable diseases such as cardiovascular diseases, mental health conditions and diabetes that account for three quarters of deaths globally (World Health Organisation, 2022). More commonly however, poor quality living conditions contribute to poor health and widening inequalities (Adlakha & John, 2022). Consequently, many public health advocates hope to convince and bring together the stakeholders who shape urban development to help create healthier places.

Evidence is one tool that can be used to convince these stakeholders from outside the health sector to think more about health outcomes. Most of the literature on the use of evidence in policy environments has focused on the public sector, such as politicians and civil servants (e.g., Crow & Jones, 2018). However, urban development decision-making processes involve many stakeholders across sectors with different needs and agendas (Black et al., 2021). While government sets policy and regulatory frameworks, private sector organisations such as property developers and investors drive urban development and strongly influence policy agendas.

In our article recently published in Policy & Politics, What types of evidence persuade actors in a complex policy system?, we explore the use of evidence to influence different groups across the urban development system to think more about health outcomes in their decisions…

The key findings of the research were that:

  1. Evidence-based narratives have wide appeal. Narratives based on real-world and lived experiences help stakeholders to form an emotional connection with evidence and are effective for drawing attention to health problems. Powerful outcomes such as child health and mortality data are particularly persuasive. This builds on literature promoting the use of storytelling approaches for public sector actors by demonstrating its applicability within the private and third sectors….(More)”

Design in the Civic Space: Generating Impact in City Government

Curated on Posted on by Stefaan Verhulst

Paper by Stephanie Wade and Jon Freach: “When design in the private sector is used as a catalyst for innovation, it can produce insight into human experience, awareness of equitable and inequitable conditions, and clarity about needs and wants. But when we think of applying design in a government complex, the complicated nature of the civic arena means that public servants need to learn and apply design in ways that are specific to the intricate and expansive ecosystem of long-standing social challenges they face, and learn new mindsets, methods, and ways of working that challenge established practices in a bureaucratic environment. Design offers tools to help navigate the ambiguous boundaries of these complex problems and improve the city’s organizational culture so that it delivers better services to residents and the communities in which they live.

For the new practitioner in government, design can seem exciting, inspiring, hopeful, and fun because over the past decade it has quickly become a popular and novel way to approach city policy and service design. In the early part of the learning process, people often report that using design helps visualize their thoughts, spark meaningful dialogue, and find connections between problems, data, and ideas. But for some, when the going gets tough—when the ambiguity of overlapping and long-standing complex civic problems, a large number of stakeholders, causes, and effects begin to surface—design practices can seem slow and confusing.

In this article we explore the growth and impact of using design in city government and best practices when introducing it into city hall to tackle complex civic sector challenges along with the highs and lows of using design in local government to help cities innovate. The authors, who have worked together to conceive, create, and deliver design training to over 100 global cities, the US federal government, and higher education, share examples from their fieldwork supported by the experiences of city staff members who have applied design methods in their jobs….(More)”.

Just Citation

Curated on Posted on by Stefaan Verhulst

Paper by Amanda Levendowski: “Contemporary citation practices are often unjust. Data cartels, like Google, Westlaw, and Lexis, prioritize profits and efficiency in ways that threaten people’s autonomy, particularly that of pregnant people and immigrants. Women and people of color have been legal scholars for more than a century, yet colleagues consistently under-cite and under-acknowledge their work. Other citations frequently lead to materials that cannot be accessed by disabled people, poor people or the public due to design, paywalls or link rot. Yet scholars and students often understand citation practices as “just” citation and perpetuate these practices unknowingly. This Article is an intervention. Using an intersectional feminist framework for understanding how cyberlaws oppress and liberate oppressed, an emerging movement known as feminist cyberlaw, this Article investigates problems posed by prevailing citation practices and introduces practical methods that bring citation into closer alignment with the feminist values of safety, equity, and accessibility. Escaping data cartels, engaging marginalized scholars, embracing free and public resources, and ensuring that those resources remain easily available represent small, radical shifts that promote just citation. This Article provides powerful, practical tools for pursuing all of them…(More)”.

Combining Human Expertise with Artificial Intelligence: Experimental Evidence from Radiology

Curated on Posted on by Stefaan Verhulst

Paper by Nikhil Agarwal, Alex Moehring, Pranav Rajpurkar & Tobias Salz: “While Artificial Intelligence (AI) algorithms have achieved performance levels comparable to human experts on various predictive tasks, human experts can still access valuable contextual information not yet incorporated into AI predictions. Humans assisted by AI predictions could outperform both human-alone or AI-alone. We conduct an experiment with professional radiologists that varies the availability of AI assistance and contextual information to study the effectiveness of human-AI collaboration and to investigate how to optimize it. Our findings reveal that (i) providing AI predictions does not uniformly increase diagnostic quality, and (ii) providing contextual information does increase quality. Radiologists do not fully capitalize on the potential gains from AI assistance because of large deviations from the benchmark Bayesian model with correct belief updating. The observed errors in belief updating can be explained by radiologists’ partially underweighting the AI’s information relative to their own and not accounting for the correlation between their own information and AI predictions. In light of these biases, we design a collaborative system between radiologists and AI. Our results demonstrate that, unless the documented mistakes can be corrected, the optimal solution involves assigning cases either to humans or to AI, but rarely to a human assisted by AI…(More)”.

How Good Are Privacy Guarantees? Platform Architecture and Violation of User Privacy

Curated on Posted on by Stefaan Verhulst

Paper by Daron Acemoglu, Alireza Fallah, Ali Makhdoumi, Azarakhsh Malekian & Asuman Ozdaglar: “Many platforms deploy data collected from users for a multitude of purposes. While some are beneficial to users, others are costly to their privacy. The presence of these privacy costs means that platforms may need to provide guarantees about how and to what extent user data will be harvested for activities such as targeted ads, individualized pricing, and sales to third parties. In this paper, we build a multi-stage model in which users decide whether to share their data based on privacy guarantees. We first introduce a novel mask-shuffle mechanism and prove it is Pareto optimal—meaning that it leaks the least about the users’ data for any given leakage about the underlying common parameter. We then show that under any mask-shuffle mechanism, there exists a unique equilibrium in which privacy guarantees balance privacy costs and utility gains from the pooling of user data for purposes such as assessment of health risks or product development. Paradoxically, we show that as users’ value of pooled data increases, the equilibrium of the game leads to lower user welfare. This is because platforms take advantage of this change to reduce privacy guarantees so much that user utility declines (whereas it would have increased with a given mechanism). Even more strikingly, we show that platforms have incentives to choose data architectures that systematically differ from those that are optimal from the user’s point of view. In particular, we identify a class of pivot mechanisms, linking individual privacy to choices by others, which platforms prefer to implement and which make users significantly worse off…(More)”.

Non-traditional data sources in obesity research: a systematic review of their use in the study of obesogenic environments

Curated on Posted on by Stefaan Verhulst

Paper by Julia Mariel Wirtz Baker, Sonia Alejandra Pou, Camila Niclis, Eugenia Haluszka & Laura Rosana Aballay: “The field of obesity epidemiology has made extensive use of traditional data sources, such as health surveys and reports from official national statistical systems, whose variety of data can be at times limited to explore a wider range of determinants relevant to obesity. Over time, other data sources began to be incorporated into obesity research, such as geospatial data (web mapping platforms, satellite imagery, and other databases embedded in Geographic Information Systems), social network data (such as Twitter, Facebook, Instagram, or other social networks), digital device data and others. The data revolution, facilitated by the massive use of digital devices with hundreds of millions of users and the emergence of the “Internet of Things” (IoT), has generated huge volumes of data from everywhere: customers, social networks and sensors, in addition to all the traditional sources mentioned above. In the research area, it offers fruitful opportunities, contributing in ways that traditionally sourced research data could not.

An international expert panel in obesity and big data pointed out some key factors in the definition of Big Data, stating that “it is always digital, has a large sample size, and a large volume or variety or velocity of variables that require additional computing power, as well as specialist skills in computer programming, database management and data science analytics”. Our interpretation of non-traditional data sources is an approximation to this definition, assuming that they are sources not traditionally used in obesity epidemiology and environmental studies, which can include digital devices, social media and geospatial data within a GIS, the latter mainly based on complex indexes that require advanced data analysis techniques and expertise.

Beyond the still discussed limitations, Big Data can be assumed as a great opportunity to improve the study of obesogenic environments, since it has been announced as a powerful resource that can provide new knowledge about human behaviour and social phenomena. Besides, it can contribute to the formulation and evaluation of policies and the development of interventions for obesity prevention. However, in this field of research, the suitability of these novel data sources is still a subject of considerable discussion, and their use has not been investigated from the obesogenic environment approach…(More)”.