Paper by Nardine Alnemr: “Challenges in attaining deliberative democratic ideals – such as inclusion, authenticity and consequentiality – in wider political systems have driven the development of artificially-designed citizen deliberation. These designed deliberations, however, are expert-driven. Whereas they may achieve ‘deliberativeness’, their design and implementation are undemocratic and limit deliberative democracy’s emancipatory goals. This is relevant in respect to the role of facilitation. In online deliberation, algorithms and artificial actors replace the central role of human facilitators. The detachment of such designed settings from wider contexts is particularly troubling from a democratic perspective. Digital technologies in online deliberation are not developed in a manner consistent with democratic ideals and are not being amenable to scrutiny by citizens. I discuss the theoretical and the practical blind spots of algorithmic facilitation. Based on these, I present recommendations to democratise the design and implementation of online deliberation with a focus on chatbots as facilitators….(More)”.
New mathematical idea reins in AI bias towards making unethical and costly commercial choices
The University of Warwick: “Researchers from the University of Warwick, Imperial College London, EPFL (Lausanne) and Sciteb Ltd have found a mathematical means of helping regulators and business manage and police Artificial Intelligence systems’ biases towards making unethical, and potentially very costly and damaging commercial choices—an ethical eye on AI.
Artificial intelligence (AI) is increasingly deployed in commercial situations. Consider for example using AI to set prices of insurance products to be sold to a particular customer. There are legitimate reasons for setting different prices for different people, but it may also be profitable to ‘game’ their psychology or willingness to shop around.
The AI has a vast number of potential strategies to choose from, but some are unethical and will incur not just moral cost but a significant potential economic penalty as stakeholders will apply some penalty if they find that such a strategy has been used—regulators may levy significant fines of billions of Dollars, Pounds or Euros and customers may boycott you—or both.
So in an environment in which decisions are increasingly made without human intervention, there is therefore a very strong incentive to know under what circumstances AI systems might adopt an unethical strategy and reduce that risk or eliminate entirely if possible.
Mathematicians and statisticians from University of Warwick, Imperial, EPFL and Sciteb Ltd have come together to help business and regulators creating a new “Unethical Optimization Principle” and provide a simple formula to estimate its impact. They have laid out the full details in a paper bearing the name “An unethical optimization principle“, published in Royal Society Open Science on Wednesday 1st July 2020….(More)”.
Regulating Electronic Means to Fight the Spread of COVID-19
In Custodia Legis Library of Congress: “It appears that COVID-19 will not go away any time soon. As there is currently no known cure or vaccine against it, countries have to find other ways to prevent and mitigate the spread of this infectious disease. Many countries have turned to electronic measures to provide general information and advice on COVID-19, allow people to check symptoms, trace contacts and alert people who have been in proximity to an infected person, identify “hot spots,” and track compliance with confinement measures and stay-at-home orders.
The Global Legal Research Directorate (GLRD) of the Law Library of Congress recently completed research on the kind of electronic measures countries around the globe are employing to fight the spread of COVID-19 and their potential privacy and data protection implications. We are excited to share with you the report that resulted from this research, Regulating Electronic Means to Fight the Spread of COVID-19. The report covers 23 selected jurisdictions, namely Argentina, Australia, Brazil, China, England, France, Iceland, India, Iran, Israel, Italy, Japan, Mexico, Norway, Portugal, the Russian Federation, South Africa, South Korea, Spain, Taiwan, Turkey, the United Arab Emirates, and the European Union (EU).
The surveys found that dedicated coronavirus apps that are downloaded to an individual’s mobile phone (particularly contact tracing apps), the use of anonymized mobility data, and creating electronic databases were the most common electronic measures. Whereas the EU recommends the use of voluntary apps because of the “high degree of intrusiveness” of mandatory apps, some countries take a different approach and require installing an app for people who enter the country from abroad, people who return to work, or people who are ordered to quarantine.
However, these electronic measures also raise privacy and data protection concerns, in particular as they relate to sensitive health data. The surveys discuss the different approaches countries have taken to ensure compliance with privacy and data protection regulations, such as conducting rights impact assessments before the measures were deployed or having data protection agencies conduct an assessment after deployment.
The map below shows which jurisdictions have adopted COVID-19 contact tracing apps and the technologies they use.
Map shows COVID-19 contact tracing apps in selected jurisdictions. Created by Susan Taylor, Law Library of Congress, based on surveys in “Regulating Electronic Means to Fight the Spread of COVID-19” (Law Library of Congress, June 2020). This map does not cover other COVID-19 apps that use GPS/geolocation….(More)”.
Data Journeys in the Sciences
Book edited by Sabina Leonelli and Niccolò Tempini: “This groundbreaking, open access volume analyses and compares data practices across several fields through the analysis of specific cases of data journeys. It brings together leading scholars in the philosophy, history and social studies of science to achieve two goals: tracking the travel of data across different spaces, times and domains of research practice; and documenting how such journeys affect the use of data as evidence and the knowledge being produced.
The volume captures the opportunities, challenges and concerns involved in making data move from the sites in which they are originally produced to sites where they can be integrated with other data, analysed and re-used for a variety of purposes. The in-depth study of data journeys provides the necessary ground to examine disciplinary, geographical and historical differences and similarities in data management, processing and interpretation, thus identifying the key conditions of possibility for the widespread data sharing associated with Big and Open Data.
The chapters are ordered in sections that broadly correspond to different stages of the journeys of data, from their generation to the legitimisation of their use for specific purposes. Additionally, the preface to the volume provides a variety of alternative “roadmaps” aimed to serve the different interests and entry points of readers; and the introduction provides a substantive overview of what data journeys can teach about the methods and epistemology of research….(More)”.
Are Food Labels Good?
Paper by Cass Sunstein: “Do people from benefit from food labels? When? By how much? Public officials face persistent challenges in answering these questions. In various nations, they use four different approaches: they refuse to do so on the ground that quantification is not feasible; they engage in breakeven analysis; they project end-states, such as economic savings or health outcomes; and they estimate willingness-to-pay for the relevant information. Each of these approaches runs into strong objections. In principle, the willingness-to-pay question has important advantages. But for those who has that question, there is a serious problem. In practice, people often lack enough information to give a sensible answer to the question how much they would be willing to pay for (more) information. People might also suffer from behavioral biases (including present bias and optimistic bias). And when preferences are labile or endogenous, even an informed and unbiased answer to the willingness to pay question may fail to capture the welfare consequences, because people may develop new tastes and values as a result of information….(More)”.
Trust and its determinants: Evidence from the Trustlab experiment
OECD Working Paper : This paper describes the results of an international initiative on trust (Trustlab) run in six OECD countries between November 2016 and November 2017 (France, Germany, Italy, Korea, Slovenia and the United States). Trustlab combines cutting-edge techniques drawn from behavioural science and experimental economics with an extensive survey on the policy and contextual determinants of trust in others and trust in institutions, administered to representative samples of participants.
The main results are as follows: 1) Self-reported measures of trust in institutions are validated experimentally, 2) Self-reported measures of trust in others capture a belief about trustworthiness (as well as altruistic preferences), whereas experimental measures rather capture willingness to cooperate and one’s own trustworthiness. Therefore, both measures are loosely related, and should be considered complementary rather than substitutes; 3) Perceptions of institutional performance strongly correlate with both trust in government and trust in others; 4) Perceived government integrity is the strongest determinant of trust in government; 5) In addition to indicators associated with social capital, such as neighbourhood connectedness and attitudes towards immigration, perceived satisfaction with public services, social preferences and expectations matter for trust in others; 6) There is a large scope for policy action, as an increase in all significant determinants of trust in government by one standard deviation may be conducive to an increase in trust by 30 to 60%….(More)”.
Harnessing the collective intelligence of stakeholders for conservation
Paper by Steven Gray et al: ” Incorporating relevant stakeholder input into conservation decision making is fundamentally challenging yet critical for understanding both the status of, and human pressures on, natural resources. Collective intelligence (CI ), defined as the ability of a group to accomplish difficult tasks more effectively than individuals, is a growing area of investigation, with implications for improving ecological decision making. However, many questions remain about the ways in which emerging internet technologies can be used to apply CI to natural resource management. We examined how synchronous social‐swarming technologies and asynchronous “wisdom of crowds” techniques can be used as potential conservation tools for estimating the status of natural resources exploited by humans.
Using an example from a recreational fishery, we show that the CI of a group of anglers can be harnessed through cyber‐enabled technologies. We demonstrate how such approaches – as compared against empirical data – could provide surprisingly accurate estimates that align with formal scientific estimates. Finally, we offer a practical approach for using resource stakeholders to assist in managing ecosystems, especially in data‐poor situations….(More)”.
COVID-19 from the Margins: What We Have Learned So Far
Blog by Silvia Masiero, Stefania Milan and Emiliano Treré: “Since the World Health Organisation declared the outbreak of COVID-19 a pandemic on 11 March 2020, narratives of the virus outbreak centred on counting and measuring have became dominant in public discourse. Enumerating and comparing cases and locations, victims or the progressive occupancy of intensive care units, policymakers and experts alike have turned data into the condition of existence of the first pandemic of the datafied society. However, many communities at the margins—from workers in the informal economy to low-income countries to victims of domestic violence—were left in the dark.
This is why our attention of researchers of datafication across the many Souths inhabiting the globe turned into the untold stories of the pandemic. We decided to make space for narratives from those individuals, communities, countries and regions that have thus far remained at the margins of global news reports and relief efforts. The multilingual blog COVID-19 from the Margins, launched on 4 May 2020, hosts stories of invisibility, including from migrants and communities living in countries and regions with limited statistical capacity or in cities and slums where pre-existing inequality and vulnerability have been augmented by the pandemic. In entering the third month of this initiative, a reflection on the main threads emerged from the 28 articles published so far is in order to devise our look to the future. In what follows, we identify four threads that have informed discussions on this blog so far, namely data visualisation, perpetuated vulnerabilities and inequalities, datafied social policies, and digital activism at the time of the pandemic…(More)”.
The AI Powered State: What can we learn from China’s approach to public sector innovation?
Essay collection edited by Nesta: “China is striding ahead of the rest of the world in terms of its investment in artificial intelligence (AI), rate of experimentation and adoption, and breadth of applications. In 2017, China announced its aim of becoming the world leader in AI technology by 2030. AI innovation is now a key national priority, with central and local government spending on AI estimated to be in the tens of billions of dollars.
While Europe and the US are also following AI strategies designed to transform the public sector, there has been surprisingly little analysis of what practical lessons can be learnt from China’s use of AI in public services. Given China’s rapid progress in this area, it is important for the rest of the world to pay attention to developments in China if it wants to keep pace.
This essay collection finds that examining China’s experience of public sector innovation offers valuable insights for policymakers. Not everything is applicable to a western context – there are social, political and ethical concerns that arise from China’s use of new technologies in public services and governance – but there is still much that can be learned from its experience while also acknowledging what should be criticized and avoided….(More)”.
The European data market
European Commission: “It was the first European Data Market study (SMART 2013/0063) contracted by the European Commission in 2013 that made a first attempt to provide facts and figures on the size and trends of the EU data economy by developing a European data market monitoring tool.
The final report of the updated European Data Market (EDM) study (SMART 2016/0063) now presents in detail the results of the final round of measurement of the updated European Data Market Monitoring Tool contracted for the 2017-2020 period.
Designed along a modular structure, as a first pillar of the study, the European Data Market Monitoring Tool is built around a core set of quantitative indicators to provide a series of assessments of the emerging market of data at present, i.e. for the years 2018 through 2020, and with projections to 2025.
The key areas covered by the indicators measured in this report are:
- The data professionals and the balance between demand and supply of data skills;
- The data companies and their revenues;
- The data user companies and their spending for data technologies;
- The market of digital products and services (“Data market”);
- The data economy and its impacts on the European economy.
- Forecast scenarios of all the indicators, based on alternative market trajectories.
Additionally, as a second major work stream, the study also presents a series of descriptive stories providing a complementary view to the one offered by the Monitoring Tool (for example, “How Big Data is driving AI” or “The Secondary Use of Health Data and Data-driven Innovation in the European Healthcare Industry”), adding fresh, real-life information around the quantitative indicators. By focusing on specific issues and aspects of the data market, the stories offer an initial, indicative “catalogue” of good practices of what is happening in the data economy today in Europe and what is likely to affect the development of the EU data economy in the medium term.
Finally, as a third work stream of the study, a landscaping exercise on the EU data ecosystem was carried out together with some community building activities to bring stakeholders together from all segments of the data value chain. The map containing the results of the landscaping of the EU data economy as well as reports from the webinars organised by the study are available on the www.datalandscape.eu website….(More)”.