Paper by Alexandra Albert: “The growth of citizen science and participatory science, where non-professional scientists voluntarily participate in scientific activities, raises questions around the ownership and interpretation of data, issues of data quality and reliability, and new kinds of data literacy. Citizen social science (CSS), as an approach that bridges these fields, calls into question the way in which research is undertaken, as well as who can collect data, what data can be collected, and what such data can be used for. This article outlines a case study—the Empty Houses Project—to explore how CSS plays out in practice, and to reflect on the opportunities and challenges it presents. The Empty Houses Project was set up to investigate how citizens could be mobilised to collect data about empty houses in their local area, so as to potentially contribute towards tackling a pressing policy issue. The study shows how the possibilities of CSS exceed the dominant view of it as a new means of creating data repositories. Rather, it considers how the data produced in CSS is an epistemology, and a politics, not just a realist tool for analysis….(More)”.
The Ethics and Laws of Medical Big Data
Chapter by Hrefna Gunnarsdottir et al: “The COVID-19 pandemic has highlighted that leveraging medical big data can help to better predict and control outbreaks from the outset. However, there are still challenges to overcome in the 21st century to efficiently use medical big data, promote innovation and public health activities and, at the same time, adequately protect individuals’ privacy. The metaphor that property is a “bundle of sticks”, each representing a different right, applies equally to medical big data. Understanding medical big data in this way raises a number of questions, including: Who has the right to make money off its buying and selling, or is it inalienable? When does medical big data become sufficiently stripped of identifiers that the rights of an individual concerning the data disappear? How have different regimes such as the General Data Protection Regulation in Europe and the Health Insurance Portability and Accountability Act in the US answered these questions differently? In this chapter, we will discuss three topics: (1) privacy and data sharing, (2) informed consent, and (3) ownership. We will identify and examine ethical and legal challenges and make suggestions on how to address them. In our discussion of each of the topics, we will also give examples related to the use of medical big data during the COVID-19 pandemic, though the issues we raise extend far beyond it….(More)”.
Far-right news sources on Facebook more engaging
Study by Laura Edelson, Minh-Kha Nguyen, Ian Goldstein, Oana Goga, Tobias Lauinger, and Damon McCoy: Facebook has become a major way people find news and information in an increasingly politically polarized nation. We analyzed how users interacted with different types of posts promoted as news in the lead-up to and aftermath of the U.S. 2020 elections. We found that politically extreme sources tend to generate more interactions from users. In particular, content from sources rated as far-right by independent news rating services consistently received the highest engagement per follower of any partisan group. Additionally, frequent purveyors of far-right misinformation had on average 65% more engagement per follower than other far-right pages. We found:
- Sources of news and information rated as far-right generate the highest average number of interactions per follower with their posts, followed by sources from the far-left, and then news sources closer to the center of the political spectrum.
- Looking at the far-right, misinformation sources far outperform non-misinformation sources. Far-right sources designated as spreaders of misinformation had an average of 426 interactions per thousand followers per week, while non-misinformation sources had an average of 259 weekly interactions per thousand followers.
- Engagement with posts from far-right and far-left news sources peaked around Election Day and again on January 6, the day of the certification of the electoral count and the U.S. Capitol riot. For posts from all other political leanings of news sources, the increase in engagement was much less intense.
- Center and left partisan categories incur a misinformation penalty, while right-leaning sources do not. Center sources of misinformation, for example, performed about 70% worse than their non-misinformation counterparts. (Note: center sources of misinformation tend to be sites presenting as health news that have no obvious ideological orientation.)…(More)”.
Measuring Commuting and Economic Activity Inside Cities with Cell Phone Records
Paper by Gabriel Kreindler and Yuhei Miyauchi: “We show how to use commuting flows to infer the spatial distribution of income within a city. A simple workplace choice model predicts a gravity equation for commuting flows whose destination fixed effects correspond to wages. We implement this method with cell phone transaction data from Dhaka and Colombo. Model-predicted income predicts separate income data, at the workplace and residential level, and by skill group. Unlike machine learning approaches, our method does not require training data, yet achieves comparable predictive power. We show that hartals (transportation strikes) in Dhaka reduce commuting more for high model-predicted wage and high-skill commuters….(More)”.
Leveraging artificial intelligence to analyze citizens’ opinions on urban green space
Paper by Mohammadhossein Ghahramani, Nadina J.Galle, Fábio Duarte, Carlo Ratti, Francesco Pilla: “Continued population growth and urbanization is shifting research to consider the quality of urban green space over the quantity of these parks, woods, and wetlands. The quality of urban green space has been hitherto measured by expert assessments, including in-situ observations, surveys, and remote sensing analyses. Location data platforms, such as TripAdvisor, can provide people’s opinion on many destinations and experiences, including UGS. This paper leverages Artificial Intelligence techniques for opinion mining and text classification using such platform’s reviews as a novel approach to urban green space quality assessments. Natural Language Processing is used to analyze contextual information given supervised scores of words by implementing computational analysis. Such an application can support local authorities and stakeholders in their understanding of–and justification for–future investments in urban green space….(More)”.
Ethical Machines: The Human-centric Use of Artificial Intelligence
Paper by B.Lepri, N.Oliver, and A.Pentland: “Today’s increased availability of large amounts of human behavioral data and advances in Artificial Intelligence are contributing to a growing reliance on algorithms to make consequential decisions for humans, including those related to access to credit or medical treatments, hiring, etc. Algorithmic decision-making processes might lead to more objective decisions than those made by humans who may be influenced by prejudice, conflicts of interest, or fatigue. However, algorithmic decision-making has been criticized for its potential to lead to privacy invasion, information asymmetry, opacity, and discrimination. In this paper, we describe available technical solutions in three large areas that we consider to be of critical importance to achieve a human-centric AI: (1) privacy and data ownership; (2) accountability and transparency; and (3) fairness. We also highlight the criticality and urgency to engage multi-disciplinary teams of researchers, practitioners, policy makers, and citizens to co-develop and evaluate in the real-world algorithmic decision-making processes designed to maximize fairness, accountability and transparency while respecting privacy….(More)”.
Do conversations end when people want them to?
Paper by Adam M. Mastroianni et al: “Do conversations end when people want them to? Surprisingly, behavioral science provides no answer to this fundamental question about the most ubiquitous of all human social activities. In two studies of 932 conversations, we asked conversants to report when they had wanted a conversation to end and to estimate when their partner (who was an intimate in Study 1 and a stranger in Study 2) had wanted it to end. Results showed that conversations almost never ended when both conversants wanted them to and rarely ended when even one conversant wanted them to and that the average discrepancy between desired and actual durations was roughly half the duration of the conversation. Conversants had little idea when their partners wanted to end and underestimated how discrepant their partners’ desires were from their own. These studies suggest that ending conversations is a classic “coordination problem” that humans are unable to solve because doing so requires information that they normally keep from each other. As a result, most conversations appear to end when no one wants them to….(More)”.
Narratives and Counternarratives on Data Sharing in Africa
Paper by Rediet Abebe et al: “As machine learning and data science applications grow ever more prevalent, there is an increased focus on data sharing and open data initiatives, particularly in the context of the African continent. Many argue that data sharing can support research and policy design to alleviate poverty, inequality, and derivative effects in Africa. Despite the fact that the datasets in question are often extracted from African communities, conversations around the challenges of accessing and sharing African data are too often driven by non-African stakeholders. These perspectives frequently employ a deficit narratives, often focusing on lack of education, training, and technological resources in the continent as the leading causes of friction in the data ecosystem.
We argue that these narratives obfuscate and distort the full complexity of the African data sharing landscape. In particular, we use storytelling via fictional personas built from a series of interviews with African data experts to complicate dominant narratives and to provide counternarratives. Coupling these personas with research on data practices within the continent, we identify recurring barriers to data sharing as well as inequities in the distribution of data sharing benefits. In particular, we discuss issues arising from power imbalances resulting from the legacies of colonialism, ethno-centrism, and slavery, disinvestment in building trust, lack of acknowledgement of historical and present-day extractive practices, and Western-centric policies that are ill-suited to the African context. After outlining these problems, we discuss avenues for addressing them when sharing data generated in the continent….(More)”.
Artificial Intelligence as an Anti-Corruption Tool (AI-ACT)
Paper by Nils Köbis, Christopher Starke, and Iyad Rahwan: “Corruption continues to be one of the biggest societal challenges of our time. New hope is placed in Artificial Intelligence (AI) to serve as an unbiased anti-corruption agent. Ever more available (open) government data paired with unprecedented performance of such algorithms render AI the next frontier in anti-corruption. Summarizing existing efforts to use AI-based anti-corruption tools (AI-ACT), we introduce a conceptual framework to advance research and policy. It outlines why AI presents a unique tool for top-down and bottom-up anti-corruption approaches. For both approaches, we outline in detail how AI-ACT present different potentials and pitfalls for (a) input data, (b) algorithmic design, and (c) institutional implementation. Finally, we venture a look into the future and flesh out key questions that need to be addressed to develop AI-ACT while considering citizens’ views, hence putting “society in the loop”….(More)”.
My Data, My Choice? – German Patient Organizations’ Attitudes towards Big Data-Driven Approaches in Personalized Medicine. An Empirical-Ethical Study
Paper by Carolin Martina Rauter, Sabine Wöhlke & Silke Schicktanz: “Personalized medicine (PM) operates with biological data to optimize therapy or prevention and to achieve cost reduction. Associated data may consist of large variations of informational subtypes e.g. genetic characteristics and their epigenetic modifications, biomarkers or even individual lifestyle factors. Present innovations in the field of information technology have already enabled the procession of increasingly large amounts of such data (‘volume’) from various sources (‘variety’) and varying quality in terms of data accuracy (‘veracity’) to facilitate the generation and analyzation of messy data sets within a short and highly efficient time period (‘velocity’) to provide insights into previously unknown connections and correlations between different items (‘value’). As such developments are characteristics of Big Data approaches, Big Data itself has become an important catchphrase that is closely linked to the emerging foundations and approaches of PM. However, as ethical concerns have been pointed out by experts in the debate already, moral concerns by stakeholders such as patient organizations (POs) need to be reflected in this context as well. We used an empirical-ethical approach including a website-analysis and 27 telephone-interviews for gaining in-depth insight into German POs’ perspectives on PM and Big Data. Our results show that not all POs are stakeholders in the same way. Comparing the perspectives and political engagement of the minority of POs that is currently actively involved in research around PM and Big Data-driven research led to four stakeholder sub-classifications: ‘mediators’ support research projects through facilitating researcher’s access to the patient community while simultaneously selecting projects they preferably support while ‘cooperators’ tend to contribute more directly to research projects by providing and implemeting patient perspectives. ‘Financers’ provide financial resources. ‘Independents’ keep control over their collected samples and associated patient-related information with a strong interest in making autonomous decisions about its scientific use. A more detailed terminology for the involvement of POs as stakeholders facilitates the adressing of their aims and goals. Based on our results, the ‘independents’ subgroup is a promising candidate for future collaborations in scientific research. Additionally, we identified gaps in PO’s knowledge about PM and Big Data. Based on these findings, approaches can be developed to increase data and statistical literacy. This way, the full potential of stakeholder involvement of POs can be made accessible in discourses around PM and Big Data….(More)”.