Paper by Diane Coyle and Stephanie Diepeveen: “Data is increasingly recognised as an important economic resource for innovation and growth, but its innate characteristics mean market-based valuations inadequately account for the impact of its use on social welfare. This paper extends the literature on the value of data by providing a framework that takes into account its non-rival nature and integrates its inherent positive and negative externalities. Positive externalities consist of the scope for combining different data sets or enabling innovative uses of existing data, while negative externalities include potential privacy loss. We propose a framework integrating these and explore the policy trade-offs shaping net social welfare through a case study of geospatial data and the transport sector in the UK, where insufficient recognition of the trade-offs has contributed to suboptimal policy outcomes. We conclude by proposing methods for empirical approaches to social data valuation, essential evidence for decisions regarding the policy trade-offs . This article therefore lays important groundwork for novel approaches to the measurement of the net social welfare contribution of data, and hence illuminating opportunities for greater and more equitable creation of value from data in our societies….(More)”
Conceptualizing AI literacy: An exploratory review
Paper by Davy Tsz KitNg, Jac Ka LokLeung, Samuel K.W.Chu, and Maggie QiaoShen: “Artificial Intelligence (AI) has spread across industries (e.g., business, science, art, education) to enhance user experience, improve work efficiency, and create many future job opportunities. However, public understanding of AI technologies and how to define AI literacy is under-explored. This vision poses upcoming challenges for our next generation to learn about AI. On this note, an exploratory review was conducted to conceptualize the newly emerging concept “AI literacy”, in search for a sound theoretical foundation to define, teach and evaluate AI literacy. Grounded in literature on 30 existing peer-reviewed articles, this review proposed four aspects (i.e., know and understand, use, evaluate, and ethical issues) for fostering AI literacy based on the adaptation of classic literacies. This study sheds light on the consolidated definition, teaching, and ethical concerns on AI literacy, establishing the groundwork for future research such as competency development and assessment criteria on AI literacy….(More)”.
‘Anyway, the dashboard is dead’: On trying to build urban informatics
Paper by Jathan Sadowski: “How do the idealised promises and purposes of urban informatics compare to the material politics and practices of their implementation? To answer this question, I ethnographically trace the development of two data dashboards by strategic planners in an Australian city over the course of 2 years. By studying this techno-political process from its origins onward, I uncovered an interesting story of obdurate institutions, bureaucratic momentum, unexpected troubles, and, ultimately, frustration and failure. These kinds of stories, which often go untold in the annals of innovation, contrast starkly with more common framings of technological triumph and transformation. They also, I argue, reveal much more about how techno-political systems are actualised in the world…(More)”.
Public Crowdsourcing: Analyzing the Role of Government Feedback on Civic Digital Platforms
Paper by Lisa Schmidthuber, Dennis Hilgers, and Krithika Randhawa: “Government organizations increasingly use crowdsourcing platforms to interact with citizens and integrate their requests in designing and delivering public services. Government usually provides feedback to individual users on whether the request can be considered. Drawing on attribution theory, this study asks how the causal attributions of the government response affect continued participation in crowdsourcing platforms. To test our hypotheses, we use a 7-year dataset of both online requests from citizens to government and government responses to citizen requests. We focus on citizen requests that are denied by government, and find that stable and uncontrollable attributions of the government response have a negative effect on future participation behavior. Also, a local government’s locus of causality negatively affects continued participation. This study contributes to research on the role of responsiveness in digital interaction between citizens and government and highlights the importance of rationale transparency to sustain citizen participation…(More)”.
Open Data Standard and Analysis Framework: Towards Response Equity in Local Governments
Paper by Joy Hsu, Ramya Ravichandran, Edwin Zhang, and Christine Keung: “There is an increasing need for open data in governments and systems to analyze equity at large scale. Local governments often lack the necessary technical tools to identify and tackle inequities in their communities. Moreover, these tools may not generalize across departments and cities nor be accessible to the public. To this end, we propose a system that facilitates centralized analyses of publicly available government datasets through 1) a US Census-linked API, 2) an equity analysis playbook, and 3) an open data standard to regulate data intake and support equitable policymaking….(More)”.
Open science, data sharing and solidarity: who benefits?
Report by Ciara Staunton et al: “Research, innovation, and progress in the life sciences are increasingly contingent on access to large quantities of data. This is one of the key premises behind the “open science” movement and the global calls for fostering the sharing of personal data, datasets, and research results. This paper reports on the outcomes of discussions by the panel “Open science, data sharing and solidarity: who benefits?” held at the 2021 Biennial conference of the International Society for the History, Philosophy, and Social Studies of Biology (ISHPSSB), and hosted by Cold Spring Harbor Laboratory (CSHL)….(More)”.
Articulating the Role of Artificial Intelligence in Collective Intelligence: A Transactive Systems Framework
Paper by Pranav Gupta and Anita Williams Woolley: “Human society faces increasingly complex problems that require coordinated collective action. Artificial intelligence (AI) holds the potential to bring together the knowledge and associated action needed to find solutions at scale. In order to unleash the potential of human and AI systems, we need to understand the core functions of collective intelligence. To this end, we describe a socio-cognitive architecture that conceptualizes how boundedly rational individuals coordinate their cognitive resources and diverse goals to accomplish joint action. Our transactive systems framework articulates the inter-member processes underlying the emergence of collective memory, attention, and reasoning, which are fundamental to intelligence in any system. Much like the cognitive architectures that have guided the development of artificial intelligence, our transactive systems framework holds the potential to be formalized in computational terms to deepen our understanding of collective intelligence and pinpoint roles that AI can play in enhancing it….(More)”
Helpline data used to monitor population distress in a pandemic
Alexander Tsai in Nature: “An important challenge in addressing mental-health problems is that trends can be difficult to detect because detection relies heavily on self-disclosure. As such, helplines — telephone services that provide crisis intervention to callers seeking help — might serve as a particularly useful source of anonymized data regarding the mental health of a population. This profiling could be especially useful during the COVID-19 pandemic, given the potential emergence or exacerbation of mental-health problems. Together, the threat of disease to oneself and others that is associated with a local epidemic, the restrictiveness of local non-pharmaceutical interventions (such as stay-at-home orders) and the potential associated loss of income could have contributed to a decline in the mental health of a population while at the same time inhibiting or delaying people’s search for help for problems. Writing in Nature, Brülhart et al. present evidence suggesting that helpline-call data can be used to monitor real-time changes in the mental health of a population — including over the course of the COVID-19 pandemic.
More so than in other areas of medicine, the stigma that can be associated with mental illness often prevents people from fully disclosing their experiences and feelings to those in their social networks, or even to licensed mental-health-care professionals. Furthermore, although mental illness contributes immensely to the global disease burden, primary health-care providers are overburdened, mental-health systems are underfunded and access to evidence-based treatment remains poor. For these reasons, helplines have, since their introduction in the United Kingdom by Samaritans in 1953, played a key part in providing low- or no-cost, anonymous support to people with unmet acute and chronic mental-health needs around the world.
Brülhart and colleagues updated and expanded on their previous work looking at helpline calls in one country by assembling data on more than 7 million helpline calls in 19 countries over the course of 2019, 2020 and part of 2021. They found that, within 6 weeks of the start of a country’s initial outbreak (defined as the week in which the cumulative number of reported SARS-CoV-2 infections was higher than 1 in 100,000 inhabitants), call volumes to helplines peaked at 35% higher than pre-pandemic levels (Fig. 1). By examining the changes in the proportion of calls relating to different categories, Brülhart and co-workers attribute these increases to fear, loneliness and concerns about health. The authors also found that suicide-related calls increased in the wake of more-stringent, non-pharmaceutical interventions, but that such calls decreased when income-support policies were introduced. The latter finding is perhaps unsurprising, but is a welcome addition to the evidence base that supports ongoing appeals for financial and other support to mitigate the adverse effects of non-pharmaceutical interventions on uncertainties over employment, income and housing security…(More)”.
AI-tocracy
Paper by Martin Beraja, Andrew Kao, David Y. Yang & Noam Yuchtman: “Can frontier innovation be sustained under autocracy? We argue that innovation and autocracy can be mutually reinforcing when: (i) the new technology bolsters the autocrat’s power; and (ii) the autocrat’s demand for the technology stimulates further innovation in applications beyond those benefiting it directly. We test for such a mutually reinforcing relationship in the context of facial recognition AI in China. To do so, we gather comprehensive data on AI firms and government procurement contracts, as well as on social unrest across China during the last decade. We first show that autocrats benefit from AI: local unrest leads to greater government procurement of facial recognition AI, and increased AI procurement suppresses subsequent unrest. We then show that AI innovation benefits from autocrats’ suppression of unrest: the contracted AI firms innovate more both for the government and commercial markets. Taken together, these results suggest the possibility of sustained AI innovation under the Chinese regime: AI innovation entrenches the regime, and the regime’s investment in AI for political control stimulates further frontier innovation….(More)”.
Institutionalizing deliberative mini-publics? Issues of legitimacy and power for randomly selected assemblies in political systems
Paper by Dimitri Courant: “Randomly selected deliberative mini-publics (DMPs) are on the rise globally. However, they remain ad hoc, opening the door to arbitrary manoeuvre and triggering a debate on their future institutionalization. What are the competing proposals aiming at institutionalizing DMPs within political systems? I suggest three ways for thinking about institutionalization: in terms of temporality, of legitimacy and support, and of power and role within a system. First, I analyze the dimension of time and how this affect DMP institutional designs. Second, I argue that because sortition produces ‘weak representatives’ with ‘humility-legitimacy’, mini-publics hardly ever make binding decisions and need to rely on external sources of legitimacies. Third, I identify four institutional models, relying on opposing views of legitimacy and politics: tamed consultation, radical democracy, representative klerocracy and hybrid polyarchy. They differ in whether mini-publics are interpreted as tools: for legitimizing elected officials; to give power to the people; or as a mean to suppress voting…(More)”.