This paper provides a restatement and defense of the data/ phenomena distinction introduced by Jim Bogen and me several decades ago (e.g., Bogen and Woodward, The Philosophical Review, 303–352, 1988). Additional motivation for the distinction is introduced, ideas surrounding the distinction are clarified, and an attempt is made to respond to several criticisms.

Healthcare professionals decide wisely about personalized medicine, treatment plans, and resource allocation by utilizing big data analytics and machine learning. To guarantee that algorithmic recommendations are impartial and fair, however, ethical issues relating to prejudice and data privacy must be taken into account. Big data analytics and machine learning have a great potential to disrupt healthcare, and as these technologies continue to evolve, new opportunities to reform healthcare and enhance patient outcomes may arise. In order to investigate (...) the patient’s outcomes with empirical evidence, this research was conducted using an online survey to incorporate healthcare professionals, patient’s reviews, and clinical staff. The data were analyzed using SmartPLS 4.0 to predict the structural model. The findings revealed a direct impact as positive influence of using machine learning on healthcare performance and patient outcomes through big data analytics. Moreover, it is evident that this can lead to personalized treatment plans, early interventions, and improved patient outcomes. Additionally, big data analytics can help healthcare providers optimize resource allocation, improve operational efficiency, and reduce costs. The impact of big data analytics on patient outcome and healthcare performance is expected to continue to grow, making it an important area for investment and research. (shrink)

It has been recently suggested that if the Extended Mind thesis is true, mental privacy might be under serious threat. In this paper, I look into the details of this claim and propose that one way of dealing with this emerging threat requires that data ontology be enriched with an additional kind of data—viz., mental data. I explore how mental data relates to both data and metadata and suggest that, arguably, and by contrast with (...) these existing categories of informational content, mental data should not be merely legally protected. Rather, if we value mental privacy as we know it, technological measures should be employed to ensure that one’s mental data are practically—not just legally—impossible for others to obtain. (shrink)

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. (shrink)

In recent years, neuroscience has begun to transform itself into a “big data” enterprise with the importation of computational and statistical techniques from machine learning and informatics. In addition to their translational applications such as brain-computer interfaces and early diagnosis of neuropathology, these tools promise to advance new solutions to longstanding theoretical quandaries. Here I critically assess whether these promises will pay off, focusing on the application of multivariate pattern analysis (MVPA) to the problem of reverse inference. I argue (...) that MVPA does not inherently provide a new answer to classical worries about reverse inference, and that the method faces pervasive interpretive problems of its own. Further, the epistemic setting of MVPA and other decoding methods contributes to a potentially worrisome shift towards prediction and away from explanation in fundamental neuroscience. (shrink)

The capacity to collect and analyse data is growing exponentially. Referred to as ‘Big Data’, this scientific, social and technological trend has helped create destabilising amounts of information, which can challenge accepted social and ethical norms. Big Data remains a fuzzy idea, emerging across social, scientific, and business contexts sometimes seemingly related only by the gigantic size of the datasets being considered. As is often the case with the cutting edge of scientific and technological progress, understanding of (...) the ethical implications of Big Data lags behind. In order to bridge such a gap, this article systematically and comprehensively analyses academic literature concerning the ethical implications of Big Data, providing a watershed for future ethical investigations and regulations. Particular attention is paid to biomedical Big Data due to the inherent sensitivity of medical information. By means of a meta-analysis of the literature, a thematic narrative is provided to guide ethicists, data scientists, regulators and other stakeholders through what is already known or hypothesised about the ethical risks of this emerging and innovative phenomenon. Five key areas of concern are identified: informed consent, privacy, ownership, epistemology and objectivity, and ‘Big Data Divides’ created between those who have or lack the necessary resources to analyse increasingly large datasets. Critical gaps in the treatment of these themes are identified with suggestions for future research. Six additional areas of concern are then suggested which, although related have not yet attracted extensive debate in the existing literature. It is argued that they will require much closer scrutiny in the immediate future: the dangers of ignoring group-level ethical harms; the importance of epistemology in assessing the ethics of Big Data; the changing nature of fiduciary relationships that become increasingly data saturated; the need to distinguish between ‘academic’ and ‘commercial’ Big Data practices in terms of potential harm to data subjects; future problems with ownership of intellectual property generated from analysis of aggregated datasets; and the difficulty of providing meaningful access rights to individual data subjects that lack necessary resources. Considered together, these eleven themes provide a thorough critical framework to guide ethical assessment and governance of emerging Big Data practices. (shrink)

Recently, it has been argued that the use of Big Data transforms the sciences, making data-driven research possible and studying causality redundant. In this paper, I focus on the claim on causal knowledge by examining the Big Data project EXPOsOMICS, whose research is funded by the European Commission and considered capable of improving our understanding of the relation between exposure and disease. While EXPOsOMICS may seem the perfect exemplification of the data-driven view, I show how causal (...) knowledge is necessary for the project, both as a source for handling complexity and as an output for meeting the project’s goals. Consequently, I argue that data-driven claims about causality are fundamentally flawed and causal knowledge should be considered a necessary aspect of Big Data science. In addition, I present the consequences of this result on other data-driven claims, concerning the role of theoretical considerations. I argue that the importance of causal knowledge and other kinds of theoretical engagement in EXPOsOMICS undermine theory-free accounts and suggest alternative ways of framing science based on Big Data. (shrink)

This essay attempts to provide additional motivation for the data/phenomena framework advocated in Bogen and Woodward, “Saving the Phenomena”.

This paper presents new data on the representation of women who publish in 25 top philosophy journals as ranked by the Philosophical Gourmet Report for the years 2004, 2014, and 2015. It also provides a new analysis of Schwitzgebel’s 1955–2015 journal data. The paper makes four points while providing an overview of the current state of women authors in philosophy. In all years and for all journals, the percentage of female authors was extremely low, in the range of (...) 14–16%. The percentage of women authors is less than the percentage of women faculty in different ranks and at different kinds of institutions. In addition, there is great variation across individual journals, and the discrepancy between women authors and women faculty appears to be different in different subfields. Interestingly, journals which do not practice anonymous review seem to have a higher percentage of women authors than journals which practice double anonymous or triple anonymous review. This paper also argues that we need more data on academic publishing to better understand whether this can explain why there are so few full-time female faculty in philosophy, since full-time hiring and tenuring practices presumably depend on a candidate’s academic publishing. (shrink)

Data and ideas are the capital of research productivity. Is it ethical to preempt the publication of another researcher’s unpublished data or preliminary analysis, perhaps without citation? The long-established answer is ‘certainly not’—but recent ‘open data’ use suggests otherwise. A research competition was held using data from The Systolic Blood Pressure Intervention Trial. This SPRINT Data Analysis Challenge created a novel environment for using open data as data became open early. This allowed third-party (...) researchers the opportunity to assess some of the trial’s outcomes before trialists. Could this infringe on trialists’ right to analyse their data? Simultaneously, trialists had access to analyses from submissions to the competition that were not formally ‘published’ with a typical author credit or citation. Therefore, trialists had the opportunity to view the competition submissions and published on those ideas first without a typical way to cite the source of that idea. Could this infringe on researchers’ right to be credited for their ideas? This is not intended as a criticism of open data, the SPRINT Data Analysis Challenge, or similar systems/ventures, but is an effort to objectively note what may be remediable flaws in the worthwhile, growing and dynamic uses of open data. We offer preliminary analytics to shed more light and provide fodder for additional discussion. (shrink)

Additional arguments for sense‐data begin by defending the claim that perceptual sensations are psychological individuals, examples being phosphenes, after‐images, and the ‘ringings’ of ‘tinnitus’. Five arguments for sense‐data follow. First, that since corresponding to every veridical visual field is a possible non‐veridical visual field of sensations, the latter merely needs a different and regular outer cause to be deemed veridical. Second, since bodily sensation experience is extremely strong evidence for the existence of a matching sensation cause, the (...) experience of ‘ringing’ must be strong evidence that a ‘ringing’ sensation is its cause, and this holds in the veridical hearing of a ringing sound. Third, that it is inconceivable that one has a truly visual experience of a red visual field, and absolutely nothing red‐looking be there before one had noticed. Fourth, that the experience of sound might have had a different developmental history, in which first there was merely experience of auditory sensation, and then over millennia such experience gradually found itself caused by regular outer phenomena, thereby constituting the hearing of sound. And a fifth argument that resembles the first argument. (shrink)

Processing data streams arriving at high speed requires the development of models that can provide fast and accurate predictions. Although deep neural networks are the state-of-the-art for many machine learning tasks, their performance in real-time data streaming scenarios is a research area that has not yet been fully addressed. Nevertheless, much effort has been put into the adaption of complex deep learning (DL) models to streaming tasks by reducing the processing time. The design of the asynchronous dual-pipeline DL (...) framework allows making predictions of incoming instances and updating the model simultaneously, using two separate layers. The aim of this work is to assess the performance of different types of DL architectures for data streaming classification using this framework. We evaluate models such as multi-layer perceptrons, recurrent, convolutional and temporal convolutional neural networks over several time series datasets that are simulated as streams at different speeds. In addition, we evaluate how the different architectures react to concept drifts typically found in evolving data streams. The obtained results indicate that convolutional architectures achieve a higher performance in terms of accuracy and efficiency, but are also the most sensitive to concept drifts. (shrink)

Unprecedented advances in the ability to store, analyze, and retrieve data is the hallmark of the information age. Along with enhanced capability to identify meaningful patterns in large data sets, contemporary data science renders many classical models of privacy protection ineffective. Addressing these issues through privacy-sensitive design is insufficient because advanced data science is mutually exclusive with preserving privacy. The special privacy problem posed by data analysis has so far escaped even leading accounts of informational (...) privacy. Here, I argue that accounts of privacy must include norms about information processing in addition to norms about information flow. Ultimately, users need the resources to control how and when personal information is processed and the knowledge to make information decisions about that control. While privacy is an insufficient design constraint, value-sensitive design around control and transparency can support privacy in the information age. (shrink)

We describe a unification of old and recent ideas for formulating graphical models to explain time series data, including Granger causality, semi-automated search procedures for graphical causal models, modeling of contemporaneous influences in times series, and heuristic generalized additive model corrections to linear models. We illustrate the procedures by finding a structure of exogenous variables and mediating variables among time series of remote geospatial indices of ocean surface temperatures and pressures. The analysis agrees with known exogenous drivers of the (...) indices, not assumed in the analysis. Automated search applied to the residuals after regressing each series on its lags and the lags of its Granger causes yields a graphical model of “comtemporaneous” causal relations identical with the qualitative graphical relations from the time series. A similar analysis produces reasonable results when applied to candidate climate indices obtained by a clustering method from sea surface temperature and sea level pressure data. (shrink)

In this article, we argue that dominant norms of demographic data are insufficient for accounting for the complexities that characterize many lesbian, gay, bisexual, transgender, and queer lives. Here, we draw from the responses of 178 people who identified as non-heterosexual or non-cisgender to demographic questions we developed regarding gender and sexual orientation. Demographic data commonly imagines identity as fixed, singular, and discrete. However, our findings suggest that, for LGBTQ people, gender and sexual identities are often multiple and (...) in flux. An overwhelming majority of our respondents reported shifting in their understandings of their sexual identities over time. In addition, for many of our respondents, gender identity was made up of overlapping factors, including the relationship between gender and transgender identities. These findings challenge researchers to reconsider how identity is understood as and through data. Drawing from critical data studies, feminist and queer digital media studies, and social justice initiatives like Data for Black Lives, we call for a reimagining of identity-based data as “queer data” or “data for queer lives.” We offer also recommendations for researchers to develop more inclusive survey questions. At the same time, we address the ways that queer perspectives destabilize the underlying logics of data by resisting classification and “capture.” For marginalized people, the stakes of this work extend beyond academia, especially in the era of algorithms and big data when the issue of who is or is not “counted” profoundly affects visibility, access, and power in the digital realm. (shrink)

We describe a unification of old and recent ideas for formulating graphical models to explain time series data, including Granger causality, semi-automated search procedures for graphical causal models, modeling of contemporaneous influences in times series, and heuristic generalized additive model corrections to linear models. We illustrate the procedures by finding a structure of exogenous variables and mediating variables among time series of remote geospatial indices of ocean surface temperatures and pressures. The analysis agrees with known exogenous drivers of the (...) indices, not assumed in the analysis. Automated search applied to the residuals after regressing each series on its lags and the lags of its Granger causes yields a graphical model of “comtemporaneous” causal relations identical with the qualitative graphical relations from the time series. A similar analysis produces reasonable results when applied to candidate climate indices obtained by a clustering method from sea surface temperature and sea level pressure data. (shrink)

A desirable property of a diversity index is strict concavity. This implies that the pooled diversity of a given community sample is greater than or equal to but not less than the weighted mean of the diversity values of the constituting plots. For a strict concave diversity index, such as species richness S, Shannon''s entropy H or Simpson''s index 1-D, the pooled diversity of a given community sample can be partitioned into two non-negative, additive components: average within-plot diversity and between-plot (...) diversity. As a result, species diversity can be summarized at various scales measuring all diversity components in the same units. Conversely, violation of strict concavity would imply the non-interpretable result of a negative diversity among community plots. In this paper, I apply this additive partition model generally adopted for traditional diversity measures to Aczél and Daróczy''s generalized entropy of type . In this way, a parametric measure of -diversity is derived as the ratio between the pooled sample diversity and the average within-plot diversity that represents the parametric analogue of Whittaker''s -diversity for data on species relative abundances. (shrink)

The current behavioral consultation case demonstrates how functional behavioral assessment (FBA) data, basic and applied research, teacher preferences, and contextual variables contribute to the decision making process when developing classroom intervention procedures. A male, African-American, fifth-grade general education student was initially referred for his inappropriate vocalizations duringtime designated for independent seatwork. FBA data suggested that this behavior was being reinforced with teacher attention. Additional data showed that he was failing to complete his assignments. An intervention was (...) implemented where the student was given assignments one a time. He was instructed to solicit teacher attention and his next assignment after completing each assignment. Analysis ofteacher ratings for inappropriate vocalizations and assignment performance data suggest that the intervention was effective in increasing assignment completion and decreasing inappropriate verbalizations. Discussion focuses on how the various data playa role in the development and implementation of classroom intervention procedures. (shrink)

The current behavioral consultation case demonstrates how functional behavioral assessment (FBA) data, basic and applied research, teacher preferences, and contextual variables contribute to the decision making process when developing classroom intervention procedures. A male, African-American, fifth-grade general education student was initially referred for his inappropriate vocalizations duringtime designated for independent seatwork. FBA data suggested that this behavior was being reinforced with teacher attention. Additional data showed that he was failing to complete his assignments. An intervention was (...) implemented where the student was given assignments one a time. He was instructed to solicit teacher attention and his next assignment after completing each assignment. Analysis ofteacher ratings for inappropriate vocalizations and assignment performance data suggest that the intervention was effective in increasing assignment completion and decreasing inappropriate verbalizations. Discussion focuses on how the various data playa role in the development and implementation of classroom intervention procedures. (shrink)

Moving away from the currently prevalent Big Data mindset towards a Small Data approach would help improve the sustainability of AI systems and would additionally have positive implications for fairness, (global) justice, privacy, transparency, and accountability.

This open access book presents an ethical approach to utilizing personal medical data. It features essays that combine academic argument with practical application of ethical principles. The contributors are experts in ethics and law. They address the challenges in the re-use of medical data of the deceased on a voluntary basis. This pioneering study looks at the many factors involved when individuals and organizations wish to share information for research, policy-making, and humanitarian purposes. -/- Today, it is easy (...) to donate blood or even organs, but it is virtually impossible to donate one’s own medical data. This is seen as ethically unacceptable. Yet, data donation can greatly benefit the welfare of our societies. This collection provides timely interdisciplinary research on biomedical big data. Topics include the ethics of data donation, the legal and regulatory challenges, and the current and future collaborations. -/- Readers will learn about the ethical and regulatory challenges associated with medical data donations. They will also better understand the special nature of using deceased data for research purposes with regard to ethical principles of autonomy, beneficence, and justice. In addition, the contributors identify the key governance issues of such a scheme. The essays also look at what we can learn in terms of best practice from existing medical data schemes. (shrink)

The rapid growth of covert activities via communications network brought about an increasing need to provide an efficient method for data hiding to protect secret information from malicious attacks. One of the options is to combine two approaches, namely steganography and compression. However, its performance heavily relies on three major factors, payload, imperceptibility, and robustness, which are always in trade-offs. Thus, this study aims to hide a large amount of secret message inside a grayscale host image without sacrificing its (...) quality and robustness. To realize the goal, a new two-tier data hiding technique is proposed that integrates an improved exploiting modification direction method and Huffman coding. First, a secret message of an arbitrary plain text of characters is compressed and transformed into streams of bits; each character is compressed into a maximum of 5 bits per stream. The stream is then divided into two parts of different sizes of 3 and 2 bits. Subsequently, each part is transformed into its decimal value, which serves as a secret code. Second, a cover image is partitioned into groups of 5 pixels based on the original EMD method. Then, an enhancement is introduced by dividing the group into two parts, namely k1 and k2, which consist of 3 and 2 pixels, respectively. Furthermore, several groups are randomly selected for embedding purposes to increase the security. Then, for each selected group, each part is embedded with its corresponding secret code by modifying one grayscale value at most to hide the code in a -ary notational system. The process is repeated until a stego-image is eventually produced. Finally, the χ2 test, which is considered one of the most severe attacks, is applied against the stego-image to evaluate the performance of the proposed method in terms of its robustness. The test revealed that the proposed method is more robust than both least significant bit embedding and the original EMD. Additionally, in terms of imperceptibility and capacity, the experimental results have also shown that the proposed method outperformed both the well-known methods, namely original EMD and optimized EMD, with a peak signal-to-noise ratio of 55.92 dB and payload of 52,428 bytes. (shrink)

Natural languages come in two different modalities. The impact of modality on the grammatical structure and linguistic theory has been discussed at great length in the last 20 years. By contrast, the impact of modality on linguistic data elicitation and collection, corpus studies and experimental (psycholinguistic) studies is still underinvestigated (van Herreweghe/Vermeerbergen 2012; Orfanidou et al. 2015). In this paper, we address specific challenges that arise in judgement data elicitation and experimental studies of sign languages. These challenges are (...) related to the socio-linguistic status of the Deaf community and the larger variability across signers within the same community, to the social status of sign languages, to properties of ;he visual-gestural modality and its interface with gesture, to methodological aspects of handling sign language data, and to specific linguistic features of sign languages. While some of these challenges also pertain to (some varieties of) spoken languages, other challenges are more modality-specific. In addition, the special combination of the challenges discussed in this paper seems to be a specific facet empirical research on sign languages is faced with. In addition, we discuss the complementarity of theoretical approaches and experimental studies and show how the interaction of both approaches contributes to a better understanding of sign languages in particular and linguistic structures in general. (shrink)

With the surge in the number of data and datafied governance initiatives, arrangements, and practices across the globe, understanding various types of such initiatives, arrangements, and their structural causes has become a daunting task for scholars, policy makers, and the public. This complexity additionally generates substantial difficulties in considering different data(fied) governances commensurable with each other. To advance the discussion, this study argues that existing scholarship is inclined to embrace an organization-centric perspective that primarily concerns factors and dynamics (...) regarding data and datafication at the organizational level at the expense of macro-level social, political, and cultural factors of both data and governance. To explicate the macro, societal dimension of data governance, this study then suggests the term “social data governance” to bring forth the consideration that data governance not only reflects the society from which it emerges but also (re)produces the policies and practices of the society in question. Drawing on theories of political science and public management, a model of social data governance is proposed to elucidate the ideological and conceptual groundings of various modes of governance from a comparative perspective. This preliminary model, consisting of a two-dimensional continuum, state intervention and societal autonomy for the one, and national cultures for the other, accounts for variations in social data governance across societies as a complementary way of conceptualizing and categorizing data governance beyond the European standpoint. Finally, we conduct an extreme case study of governing digital contact-tracing techniques during the pandemic to exemplify the explanatory power of the proposed model of social data governance. (shrink)

A chamada corrida espacial remete a um dos mais instigantes eventos da Guerra Fria e da própria história humana. Neste artigo, busca-se explicitar o contexto do pós-Segunda Guerra Mundial em que Estados Unidos e União Soviética decidiram investir em tecnologias para explorar e ocupar o espaço exterior, sobretudo a órbita terrestre, destacando suas estratégias ligadas à obtenção de informações sobre a ação do “inimigo” mediante a constituição de sistemas permanentes de inteligência a um baixo custo político para as tensas relações (...) bipolares. Em meio a demonstrações de superioridade bélico-científica envolvendo o desenvolvimento de mísseis intercontinentais, satélites de pesquisa e os pioneiros voos espaciais tripulados, as superpotências se empenharam para instalar secretos sistemas orbitais de monitoramento dos arsenais nucleares, cuja entrada em operação passou a propiciar tecnologias eficazes para o governo do terror nuclear e para a administração diplomático-militar dos conflitos decorrentes de seu enfrentamento planetário. Além de recuperar informações sobre a Guerra Fria, a problematização da corrida espacial também contribui para o estudo genealógico dos programas sidero-planetários de monitoramento que se afirmam como elementos dos dispositivos de poder das contemporâneas sociedades de controle.Bring data! Space race and Intelligence The so-called space race refers to one of the most exciting events of the Cold War and of the human history. In this article, we try to explain the post-World War II context in which the United States and Soviet Union decided to invest in technologies to exploit and occupy outer space, especially the terrestrial orbit, highlighting their strategies for obtaining information about the action of the "enemy" through the constitution of permanent systems of intelligence at a low political cost for tense bipolar relations. Amidst demonstrations of warlike scientific superiority involving the development of intercontinental missiles, research satellites, and pioneering manned spaceflight, the superpowers worked to install secret orbital systems for monitoring nuclear arsenals, whose entry into operation began to provide effective technologies to the government of nuclear terror and to the diplomatic-military administration of the conflicts arising from its planetary confrontation. In addition to retrieving information about the Cold War, the problematization of the space race also contributes to the genealogical study of the space-planetary programs of monitoring that affirm themselves as elements of the devices of power of the contemporary societies of control. Resumen Bring data! Carrera espacial y inteligencia La carrera espacial lleva a uno de los más estimulantes eventos de la Guerra Fría y de la historia humana. En este artículo, vamos a explicitar el contexto de lo post Segunda Guerra Mundial en lo que Estados Unidos y Unión Soviética decidieron hacer uso de tecnologías para explorar y ocupar el espacio exterior, sobretodo la órbita terrestre, subrayando las estrategias relacionadas a la obtención de información sobre la acción del “enemigo” frente a la constitución de sistema permanentes de inteligencia de bajo coste político para las tensas relaciones bipolares. Entre las demonstraciones de superioridad bélica-científica involucrando el desarrollo de misiles intercontinentales, satélites de investigación y los pioneros vuelos espaciales tripulados, las superpotencias insistieron en instalar secretos sistemas orbitales para monitorear los arsenales nucleares, cuyo inicio de operación proporcionó tecnologías eficaces para el gobierno del terror nuclear y para la administración diplomático-militar de conflictos resultantes del enfrentamiento planetario. Además de recuperar informaciones sobre la Guerra Fría, la problematización de la carrera espacial también contribuye a que el estudio genealógico de programas siderales y planetarios para monitorizar que se afirman como elementos de dispositivos de poder de sociedades de control contemporáneas. (shrink)

Data exclusivity is a temporary exclusive user right on the clinical data that need to be submitted to the regulatory authorities to prove that a new drug is safe and effective. For the pharmaceutical industry, data exclusivity is an important addition to the patent system, as data exclusivity will de facto delay the market entry of generic drugs until after the exclusive user rights on the clinical data have expired. In order to assess the normative (...) legitimacy of the industry’s demand to include minimum standards for data exclusivity in international agreements, this chapter evaluates the three justifications that have frequently been advanced, depicting data exclusivity as an essential policy tool to promote innovation, a mechanism to prevent the generic industry from ‘free-riding,’ and a legal instrument to protect the industry’s property rights in clinical data. (shrink)

It is important to study movement data from individual subjects rather than by averaging data across subjects or trials, because averaged data may follow different laws than those followed by the individual data. This fact can be shown mathematically. In addition, clear assumptions and a thorough understanding of their consequences are a necessary component of any realistic model.

The expansion of research on deep brain stimulation (DBS) and adaptive DBS (aDBS) raises important neuroethics and policy questions related to data sharing. However, there has been little empirical research on the perspectives of experts developing these technologies. We conducted semi-structured, open-ended interviews with aDBS researchers regarding their data sharing practices and their perspectives on ethical and policy issues related to sharing. Researchers expressed support for and a commitment to sharing, with most saying that they were either sharing (...) their data or would share in the future and that doing so was important for advancing the field. However, those who are sharing reported a variety of sharing partners, suggesting heterogeneity in sharing practices and lack of the broad sharing that would reflect principles of open science. Researchers described several concerns and barriers related to sharing, including privacy and confidentiality, the usability of shared data by others, ownership and control of data (including potential commercialization), and limited resources for sharing. They also suggested potential solutions to these challenges, including additional safeguards to address privacy issues, standardization and transparency in analysis to address issues of data usability, professional norms and heightened cooperation to address issues of ownership and control, and streamlining of data transmission to address resource limitations. Researchers also offered a range of views on the sensitivity of neural activity data (NAD) and data related to mental health in the context of sharing. These findings are an important input to deliberations by researchers, policymakers, neuroethicists, and other stakeholders as they navigate ethics and policy questions related to aDBS research. (shrink)

Most people are completely oblivious to the danger that their medical data undergoes as soon as it goes out into the burgeoning world of big data. Medical data is financially valuable, and your sensitive data may be shared or sold by doctors, hospitals, clinical laboratories, and pharmacies—without your knowledge or consent. Medical data can also be found in your browsing history, the smartphone applications you use, data from wearables, your shopping list, and more. At (...) best, data about your health might end up in the hands of researchers on whose good will we depend to avoid abuses of power.2 Most likely, it will end up with data brokers who might sell it to a future employer, or an insurance company, or the government. At worst, your medical data may end up in the hands of criminals eager to commit extortion or identity theft. In addition to data harms related to exposure and discrimination, the collection of sensitive data by powerful corporations risks the creation of data monopolies that can dominate and condition access to health care. -/- This chapter aims to explore the challenge that big data brings to medical privacy. Section I offers a brief overview of the role of privacy in medical settings. I define privacy as having one’s personal information and one’s personal sensorial space (what I call autotopos) unaccessed. Section II discusses how the challenge of big data differs from other risks to medical privacy. Section III is about what can be done to minimise those risks. I argue that the most effective way of protecting people from suffering unfair medical consequences is by having a public universal healthcare system in which coverage is not influenced by personal data (e.g., genetic predisposition, exercise habits, eating habits, etc.). (shrink)

Is the sense‐data theory, otherwise known as indirect realism, a form of representationalism? This question has been under‐explored in the extant literature, and to the extent that there is discussion, contemporary authors disagree. There are many different variants of representationalism, and differences between these variants that some people have taken to be inconsequential turn out to be key factors in whether the sense‐data theory is a form of representationalism. Chief among these are whether a representationalist takes the phenomenal (...) character of an experience to be explicable in virtue of the properties of an experience that represent something or explicable in virtue of that which gets represented. Another is whether representationalists hold a non‐reductionist, or naturalistically or non‐naturalistically reductionist variant of representationalism. In addition, subtle differences in what one takes phenomenal character to be on the sense‐data theory – either awareness of sense‐data or the sense‐data themselves – together with one's account of representation, are crucial factors in determining whether sense‐data theory is compatible with representationalism. This paper explores these relationships and makes manifest the complexities of the metaphysics of two central theories of perception. 1 Many thanks are due to Clare Batty, Derek Brown, and James Stazicker for discussion and feedback on earlier drafts, which made this essay better than it would otherwise have been. This work was supported by two grants from the Arts and Humanities Research Council (grant numbers AH/1027509/1 and AH/L007053/1). (shrink)

Thirty years after the appearance of Wolf Leslau’s Comparative Dictionary of Geʿez, the present study aims at correcting and updating some of the entries of this major tool of Semitic etymology. New data from Ugaritic, Akkadian, and especially Modern South Arabian are prominent among the additions.

There is increasing concern about “surveillance capitalism,” whereby for-profit companies generate value from data, while individuals are unable to resist (Zuboff 2019). Non-profits using data-enabled surveillance receive less attention. Higher education institutions (HEIs) have embraced data analytics, but the wide latitude that private, profit-oriented enterprises have to collect data is inappropriate. HEIs have a fiduciary relationship to students, not a narrowly transactional one (see Jones et al, forthcoming). They are responsible for facets of student life beyond (...) education. In addition to classrooms, learning management systems, and libraries, HEIs manage dormitories, gyms, dining halls, health facilities, career advising, police departments, and student employment. HEIs collect and use student data in all of these domains, ostensibly to understand learner behaviors and contexts, improve learning outcomes, and increase institutional efficiency through “learning analytics” (LA). ID card swipes and Wi-Fi log-ins can track student location, class attendance, use of campus facilities, eating habits, and friend groups. Course management systems capture how students interact with readings, video lectures, and discussion boards. Application materials provide demographic information. These data are used to identify students needing support, predict enrollment demands, and target recruiting efforts. These are laudable aims. However, current LA practices may be inconsistent with HEIs’ fiduciary responsibilities. HEIs often justify LA as advancing student interests, but some projects advance primarily organizational welfare and institutional interests. Moreover, LA advances a narrow conception of student interests while discounting privacy and autonomy. Students are generally unaware of the information collected, do not provide meaningful consent, and express discomfort and resigned acceptance about HEI data practices, especially for non-academic data (see Jones et al. forthcoming). The breadth and depth of student information available, combined with their fiduciary responsibility, create a duty that HEIs exercise substantial restraint and rigorous evaluation in data collection and use. (shrink)

Science and technology studies and the emerging field of data science share surprising elective affinities. At the growing intersections of these fields, there will be many opportunities and not a few thorny difficulties for STS scholars. First, I discuss how both fields frame the rollout of data science as a simultaneously social and technical endeavor, even if in distinct ways and for diverging purposes. Second, I discuss the logic of domains in contemporary computer, information, and data science (...) circles. While STS is often agnostic about the borders between the sciences or with industry and state—occasionally taking those boundaries as an object of study—data science takes those boundaries as its target to overcome. These two elective affinities present analytic and practical challenges for STS but also opportunities for engagement. Overall, in addition to these typifications, I urge STS scholars to strategically position themselves to investigate and contribute to the breadth of transformations that seek to touch virtually every science and newly bind spheres of academy, industry, and state. (shrink)

Is the sense-data theory, otherwise known as indirect realism, a form of representationalism? This question has been underexplored in the extant literature, and to the extent that there is discussion, contemporary authors disagree. There are many different variants of representationalism, and differences between these variants that some people have taken to be inconsequential turn out to be key factors in whether the sense-data theory is a form of representationalism. Chief among these are whether a representationalist takes the phenomenal (...) character of an experience to be explicable in virtue of the properties of an experience that represent something or explicable in virtue of that which gets represented. Another is whether representationalists hold a non-reductionist, or naturalistically or non-naturalistically reductionist variant of representationalism. In addition, subtle differences in what one takes phenomenal character to be on the sense-data theory – either awareness of sense-data or the sense-data themselves – together with one's account of representation, are crucial factors in determining whether sense-data theory is compatible with representationalism. This paper explores these relationships and makes manifest the complexities of the metaphysics of two central theories of perception. (shrink)

This article describes some potential uses of Bayesian estimation for time-series and panel data models by incorporating information from prior probabilities in addition to observed data. Drawing on econometrics and other literatures we illustrate the use of informative “shrinkage” or “small variance” priors while extending prior work on the general cross-lagged panel model. Using a panel dataset of national income and subjective well-being we describe three key benefits of these priors. First, they shrink parameter estimates toward zero or (...) toward each other for time-varying parameters, which lends additional support for an income → SWB effect that is not supported with maximum likelihood. This is useful because, second, these priors increase model parsimony and the stability of estimates and thus improve out-of-sample predictions and interpretability, which means estimated effect should also be more trustworthy than under ML. Third, these priors allow estimating otherwise under-identified models under ML, allowing higher-order lagged effects and time-varying parameters that are otherwise impossible to estimate using observed data alone. In conclusion we note some of the responsibilities that come with the use of priors which, departing from typical commentaries on their scientific applications, we describe as involving reflection on how best to apply modeling tools to address matters of worldly concern. (shrink)

Digital farming technologies promise to help farmers make well-informed decisions that improve the quality and quantity of their production, with less labour and less impact on the environment. This future, however, can only become a reality if farmers are willing to share their data with agribusinesses that develop digital technologies. To foster trust in data sharing, in Europe the EU Code of Conduct for agricultural data sharing by contractual agreement was launched in 2018 which encourages transparency about (...) data use. This article looks at the EU Code through the lens of literature on trust and contract agreements. We agree with the makers of the EU Code that a contract can make an important contribution to trust relationships as it is needed to mitigate the detrimental effects of power relationships between experts and non-experts. Building on Onora O’Neills perspective to trust, however, we argued that a contract can only be successful in fostering trust when information is comprehended by the more vulnerable party in this relationship who has to sign the contract, the more powerful partner takes responsibility to provide that information, and information is tailored to the information needs of the party signing the contract, even when data are re-used over a longer period. In addition, we think that differences between trust relationships and relationships of accountability, give reason to add to informed consent other more substantive ethical components in a more encompassing code of conduct. (shrink)

A key ethical issue arising in data linkage research relates to consent requirements. Patients’ consent preferences in the context of health research have been explored but their consent preferences regarding data linkage specifically have been under-explored. In addition, the views on data linkage are often those of patient groups. As a result, little is known about lay people’s views and their preferences about consent requirements in the context of data linkage. This study explores lay people’s views (...) and justifications regarding the acceptability of conducting data linkage research without obtaining consent. (shrink)

The paper by Dupras and Bunnik is a useful addition to the literature on privacy in regards to datasets of human tissue/materials. In particular, the paper addresses import...

Ambiguous data obtained by deception say nothing about social behavior. A balanced social psychology requires separating statistical hypotheses from substantive hypotheses. Neither statistical norms nor moral rules are psychological theories. Explanatory substantive theories stipulate the structures and processes underlying behavior. The Bayesian approach is incompatible with the requirement that all to-be-tested theories be given the benefit of the doubt.

BackgroundIncreasingly, hospitals and research institutes are developing technical solutions for sharing patient data in a privacy preserving manner. Two of these technical solutions are homomorphic encryption and distributed ledger technology. Homomorphic encryption allows computations to be performed on data without this data ever being decrypted. Therefore, homomorphic encryption represents a potential solution for conducting feasibility studies on cohorts of sensitive patient data stored in distributed locations. Distributed ledger technology provides a permanent record on all transfers and (...) processing of patient data, allowing data custodians to audit access. A significant portion of the current literature has examined how these technologies might comply with data protection and research ethics frameworks. In the Swiss context, these instruments include the Federal Act on Data Protection and the Human Research Act. There are also institutional frameworks that govern the processing of health related and genetic data at different universities and hospitals. Given Switzerland’s geographical proximity to European Union (EU) member states, the General Data Protection Regulation (GDPR) may impose additional obligations.MethodsTo conduct this assessment, we carried out a series of qualitative interviews with key stakeholders at Swiss hospitals and research institutions. These included legal and clinical data management staff, as well as clinical and research ethics experts. These interviews were carried out with two series of vignettes that focused on data discovery using homomorphic encryption and data erasure from a distributed ledger platform.ResultsFor our first set of vignettes, interviewees were prepared to allow data discovery requests if patients had provided general consent or ethics committee approval, depending on the types of data made available. Our interviewees highlighted the importance of protecting against the risk of reidentification given different types of data. For our second set, there was disagreement amongst interviewees on whether they would delete patient data locally, or delete data linked to a ledger with cryptographic hashes. Our interviewees were also willing to delete data locally or on the ledger, subject to local legislation.ConclusionOur findings can help guide the deployment of these technologies, as well as determine ethics and legal requirements for such technologies. (shrink)

From the end of 2018 in China, the Big-data Driven Price Discrimination (BDPD) of online consumption raised public debate on social media. To study the consumers’ attitude about the BDPD, this study constructed a semantic recognition frame to deconstruct the Affection-Behavior-Cognition (ABC) consumer attitude theory using machine learning models inclusive of the Labeled Latent Dirichlet Allocation (LDA), Long Short-Term Memory (LSTM), and Snow Natural Language Processing (NLP), based on social media comments text dataset. Similar to the questionnaires published results, (...) this article verified that 61% of consumers expressed negative sentiment toward BDPD in general. Differently, on a finer scale, this study further measured the negative sentiments that differ significantly among different topics. The measurement results show that the topics “Regular Customers Priced High” (69%) and “Usage Intention” (67%) occupy the top two places of negative sentiment among consumers, and the topic “Precision Marketing” (42%) is at the bottom. Moreover, semantic recognition results that 49% of consumers’ comments involve multiple topics, indicating that consumers have a pretty clear cognition of the complex status of the BDPD. Importantly, this study found some topics that had not been focused on in previous studies, such as more than 8% of consumers calling for government and legal departments to regulate BDPD behavior, which indicates that quite enough consumers are losing confidence in the self-discipline of the platform enterprises. Another interesting result is that consumers who pursue solutions to the BDPD belong to two mutually exclusive groups: government protection and self-protection. The significance of this study is that it reminds the e-commerce platforms to pay attention to the potential harm for consumers’ psychology while bringing additional profits through the BDPD. Otherwise, the negative consumer attitudes may cause damage to brand image, business reputation, and the sustainable development of the platforms themselves. It also provides the government supervision departments an advanced analysis method reference for more effective administration to protect social fairness. (shrink)

Neuroscience has become increasingly reliant on multi-subject research in addition to studies of unusual single patients. This research has brought with it a challenge: how are data from different human brains to be combined? The dominant strategy for aggregating data across brains is what I call the ‘cartographic approach’, which involves mapping data from individuals to a spatial template. Here I characterize the cartographic approach and argue that one of its key steps, registration, should be carried out (...) in a way that is sensitive to the target of investigation. Because registration aims to align homologous brain locations, but not all homologous locations can be simultaneously aligned, a multiplicity of registration methods is required to meet the needs of researchers investigating different phenomena. I call this position ‘registration pluralism’. Registration pluralism has potential implications for neuroscientific practice, three of which I discuss here. This work shows the importance of reflecting more carefully on data aggregation methods, especially in light of the substantial individual differences that exist between brains. (shrink)

While teachers' knowledge is widely viewed as a key aspect of professional development in the new era, little research attention has been paid to one of its key components: teacher data literacy. Accordingly, this study aimed to combine teacher data literacy with TPACK, a widely-used framework for understanding and assessing teachers' knowledge. We first used qualitative methods to develop this integrated framework, then distributed a quantitative self-report survey based on the framework to teachers, and analyzed the resulting (...) class='Hi'>data. The qualitative phase highlighted five types of teachers' knowledge required in an integrated core knowledge system incorporating data literacy and provided insights for reflecting on teaching and learning in smart learning environments. The quantitative analysis of data from the TDL-TPACK questionnaire indicated that most teachers were competent practitioners but had some areas for improvement. Experienced teachers in their 30s and 40s performed at higher levels, while some of those aged over 50 displayed incremental decreases in performance. Other factors such as the age, experience, academic qualifications, and role of teachers may affect different aspects of their knowledge, including their data literacy. The research findings provide useful insights for additional teacher training and development programs in the context of smart education. (shrink)

The issue of the theory-ladenness of observation has long troubled philosophers of science, largely because it seems to threaten the objectivity of science. However, the way in which prior beliefs influence the perception of data is in part an empirical issue that can be investigated by cognitive psychology. This point is illustrated through an experimental analogue of scientific data-interpretation tasks in which subjects judging the covariation between personality variables based their judgments on pure data, their theoretical intuitions (...) about the variables, or both data and prior theoretical beliefs. Results showed that the perceived magnitude of correlations was greatest when subjects relied solely on theoretical intuitions; that data-based judgments were drawn in the direction of those prior beliefs; but that exposure to data nonetheless moderated the strength of the prior theories. In addition, prior beliefs were found to influence judgments only after a brief priming interval, suggesting that subjects needed time to retrieve their theoretical intuitions from memory. These results suggest ways to investigate the processes mediating theory-laden observation, and, contrary to the fears of positivist philosophers, imply that the theory-ladenness of observation does not entail that theoretical beliefs are immune to data. (shrink)

Although big data analytics capability leads to competitive performance, the mechanism of the relationship is still unclear. To narrow the research gap, this paper investigates the mediating roles of two forms of resource integration in the relationship between two forms of BDAC [big data analytics management capability and BDA technology capability] and competitive performance. Supported by Partial Least Squares-Structural Equation Modeling and the cross-sectional survey data from 219 Chinese enterprises, the results show that the resource bricolage plays (...) a significantly mediating role in the relationships between BDA management capability and competitive performance as well as in the relationship between BDA technology capability and competitive performance. Furthermore, the mediating effect in the former relationship is stronger than that in the latter relationship. Additionally, BDA technology capability only has a direct effect on resource bricolage, while BDA management capability has a stronger effect on resource optimization than that on resource bricolage. Finally, resource bricolage has a stronger impact on competitive performance than resource optimization. These findings contribute to understanding how enterprises could apply different forms of BDAC to other kinds of resource integration to achieve outstanding competitive performance. (shrink)

The article by Dupras and Bunnik. makes a fundamental contribution in the context of the current boom in personalized medicine. We propose an additional crit...