Paper by Anirban Basu, Noah Hammarlund, Sara Khor & Aasthaa Bansal: “There is growing concern that the increasing use of machine learning and artificial intelligence-based systems may exacerbate health disparities through discrimination. We provide a hierarchical definition of discrimination consisting of algorithmic discrimination arising from predictive scores used for allocating resources and human discrimination arising from allocating resources by human decision-makers conditional on these predictive scores. We then offer an overarching statistical framework of algorithmic discrimination through the lens of measurement errors, which is familiar to the health economics audience. Specifically, we show that algorithmic discrimination exists when measurement errors exist in either the outcome or the predictors, and there is endogenous selection for participation in the observed data. The absence of any of these phenomena would eliminate algorithmic discrimination. We show that although equalized odds constraints can be employed as bias-mitigating strategies, such constraints may increase algorithmic discrimination when there is measurement error in the dependent variable….(More)”.
A Proposal for Researcher Access to Platform Data: The Platform Transparency and Accountability Act
Paper by Nathaniel Persily: “We should not need to wait for whistleblowers to blow their whistles, however, before we can understand what is actually happening on these extremely powerful digital platforms. Congress needs to act immediately to ensure that a steady stream of rigorous research reaches the public on the most pressing issues concerning digital technology. No one trusts the representations made by the platforms themselves, though, given their conflict of interest and understandable caution in releasing information that might spook shareholders. We need to develop an unprecedented system of corporate datasharing, mandated by government for independent research in the public interest.
This is easier said than done. Not only do the details matter, they are the only thing that matters. It is all well and good to call for “transparency” or “datasharing,” as an uncountable number of academics have, but the way government might setup this unprecedented regime will determine whether it can serve the grandiose purposes techcritics hope it will….(More)”.
Evaluating the trade-off between privacy, public health safety, and digital security in a pandemic
Paper by Titi Akinsanmi and Aishat Salami: “COVID-19 has impacted all aspects of everyday normalcy globally. During the height of the pandemic, people shared their (PI) with one goal—to protect themselves from contracting an “unknown and rapidly mutating” virus. The technologies (from applications based on mobile devices to online platforms) collect (with or without informed consent) large amounts of PI including location, travel, and personal health information. These were deployed to monitor, track, and control the spread of the virus. However, many of these measures encouraged the trade-off on privacy for safety. In this paper, we reexamine the nature of privacy through the lens of safety focused on the health sector, digital security, and what constitutes an infraction or otherwise of the privacy rights of individuals in a pandemic as experienced in the past 18 months. This paper makes a case for maintaining a balance between the benefit, which the contact tracing apps offer in the containment of COVID-19 with the need to ensure end-user privacy and data security. Specifically, it strengthens the case for designing with transparency and accountability measures and safeguards in place as critical to protecting the privacy and digital security of users—in the use, collection, and retention of user data. We recommend oversight measures to ensure compliance with the principles of lawful processing, knowing that these, among others, would ensure the integration of privacy by design principles even in unforeseen crises like an ongoing pandemic; entrench public trust and acceptance, and protect the digital security of people…(More)”.
Towards Efficient Information Sharing in Network Markets
Paper by Bertin Martens, Geoffrey Parker, Georgios Petropoulos and Marshall W. Van Alstyne: “Digital platforms facilitate interactions between consumers and merchants that allow the collection of profiling information which drives innovation and welfare. Private incentives, however, lead to information asymmetries resulting in market failures both on-platform, among merchants, and off-platform, among competing platforms. This paper develops two product differentiation models to study private and social incentives to share information within and between platforms. We show that there is scope for ex-ante regulation of mandatory data sharing that improves social welfare better than competing interventions such as barring entry, break-up, forced divestiture, or limiting recommendation steering. These alternate proposals do not make efficient use of information. We argue that the location of data access matters and develop a regulatory framework that introduces a new data right for platform users, the in-situ data right, which is associated with positive welfare gains. By construction, this right enables effective information sharing, together with its context, without reducing the value created by network effects. It also enables regulatory oversight but limits data privacy leakages. We discuss crucial elements of its implementation in order to achieve innovation-friendly and competitive digital markets…(More)”.
Has COVID-19 been the making of Open Science?
Article by Lonni Besançon, Corentin Segalas and Clémence Leyrat: “Although many concepts fall under the umbrella of Open Science, some of its key concepts are: Open Access, Open Data, Open Source, and Open Peer Review. How far these four principles were embraced by researchers during the pandemic and where there is room for improvement, is what we, as early career researchers, set out to assess by looking at data on scientific articles published during the Covid-19 pandemic….Open Source and Open Data practices consist in making all the data and materials used to gather or analyse data available on relevant repositories. While we can find incredibly useful datasets shared publicly on COVID-19 (for instance those provided by the European Centre for Disease Control), they remain the exception rather than the norm. A spectacular example of this were the papers utilising data from the company Surgisphere, that led to retracted papers in The Lancet and The New England Journal of Medicine. In our paper, we highlight 4 papers that could have been retracted much earlier (and perhaps would never have been accepted) had the data been made accessible from the time of publication. As we argue in our paper, this presents a clear case for making open data and open source the default, with exceptions for privacy and safety. While some journals already have such policies, we go further in asking that, when data cannot be shared publicly, editors/publishers and authors/institutions should agree on a third party to check the existence and reliability/validity of the data and the results presented. This not only would strengthen the review process, but also enhance the reproducibility of research and further accelerate the production of new knowledge through data and code sharing…(More)”.
The AI Localism Canvas: A Framework to Assess the Emergence of Governance of AI within Cities
Paper by Verhulst, Stefaan, Andrew Young, and Mona Sloane: “AI Localism focuses on governance innovation surrounding the use of AI on a local level….As it stands, however, the decision-making processes involved in the local governance of AI systems are not very systematized or well understood. Scholars and local decision-makers lack an adequate evidence base and analytical framework to help guide their thinking. In order to address this shortcoming, we have developed the below “AI Localism Canvas” which can help identify, categorize and assess the different areas of AI Localism specific to a city or region, in the process aid decision-makers in weighing risk and opportunity. The overall goal of the canvas is to rapidly assess and iterate local governance innovation about AI to ensure citizens’ interests and rights are respected….(More)”.
Information Disorder in the Glam Sector: The Challenges of Crowd Sourced Contributions
Paper by Saima Qutab, Michael David Myers and Lesley Gardner: “For some years information systems researchers have looked at crowdsourcing as a way for individuals, organizations and institutions to co-create content and generate value. Although there are many potential benefits of crowdsourcing, the quality control of crowd contributions stands out as one of the most significant challenges. Crowds can create the information contents but at the same time can facilitate information disorder: misinformation, disinformation and mal-information.
Crowd created information is a dominant element in what is sometimes called the post-truth era. A small piece of misleading information can constitute significant challenges to the information sharing group or society. This misinformation can reshape in various ways how information-driven communities make sense of their world. As information disorder and its effects have recently started to be recognised as a potential problem in IS research, we need to explore this phenomenon in more detail, to understand how it happens and why. This multiple case study is aimed at appraising information disorder through crowd-created contents in the knowledge and cultural heritage organisations such as Galleries, Libraries, Archives and Museums (GLAM). We intend to investigate the quality control mechanisms that might be used to prevent and minimise the effects of information disorder from crowdsourced contributions….(More)”.
Quantifying collective intelligence in human groups
Paper by Christoph Riedl et al: “Collective intelligence (CI) is critical to solving many scientific, business, and other problems, but groups often fail to achieve it. Here, we analyze data on group performance from 22 studies, including 5,279 individuals in 1,356 groups. Our results support the conclusion that a robust CI factor characterizes a group’s ability to work together across a diverse set of tasks. We further show that CI is predicted by the proportion of women in the group, mediated by average social perceptiveness of group members, and that it predicts performance on various out-of-sample criterion tasks. We also find that, overall, group collaboration process is more important in predicting CI than the skill of individual members….(More)”.
Open data in digital strategies against COVID-19: the case of Belgium
Paper by Robert Viseur: “COVID-19 has highlighted the importance of digital in the fight against the pandemic (control at the border, automated tracing, creation of databases…). In this research, we analyze the Belgian response in terms of open data. First, we examine the open data publication strategy in Belgium (a federal state with a sometimes complex functioning, especially in health), second, we conduct a case study (anatomy of the pandemic in Belgium) in order to better understand the strengths and weaknesses of the main COVID-19 open data repository. And third, we analyze the obstacles to open data publication. Finally, we discuss the Belgian COVID-19 open data strategy in terms of data availability, data relevance and knowledge management. In particular, we show how difficult it is to optimize the latter in order to make the best use of governmental, private and academic open data in a way that has a positive impact on public health policy….(More)”.
How Could Smart Cities Use Data? – Towards a Taxonomy of Data-Driven Smart City Projects
Paper by Babett Kühne and Kai Heidel: “The process of urbanization has caused a huge growth in cities all over the world. This development makes the organization and infrastructure of an individual city increasingly important. In this context, the idea of a smart city is growing and smart city projects are beginning to appear. As the amount of data is growing with connected technologies, such projects rely on data as a key resource. However, current research does not provide an overview on these projects and which constructs are involved in data-driven smart city projects. Therefore, this research begins the building of a taxonomy on such projects through the establishment of a common language among researchers in this new field through eleven dimensions. Additionally, it develops a concrete conceptualization of data-driven smart city projects for practitioners as an initial guidance for the field of smart cities….(More)”.