We devise imperative programming languages for verified real number computation where real numbers are provided as abstract data types such that the users of the languages can express real number computation by considering real numbers as abstract mathematical entities. Unlike other common approaches toward real number computation, based on an algebraic model that lacks implementability or transcendental computation, or finite-precision approximation such as using double precision computation that lacks a formal foundation, our languages are devised based on computable analysis, (...) a foundation of rigorous computation over continuous data. Consequently, the users of the language can easily program real number computation and reason about the behaviours of their programs, relying on their mathematical knowledge of real numbers without worrying about artificial roundoff errors. As the languages are imperative, we adopt precondition–postcondition-style program specification and Hoare-style program verification methodologies. Consequently, the users of the language can easily program a computation over real numbers, specify the expected behaviour of the program, including termination, and prove the correctness of the specification. Furthermore, we suggest extending the languages with other interesting continuous data, such as matrices, continuous real functions, et cetera.Abstract taken directly from the thesis.E-mail: [email protected]: https://sewonpark.com/thesis. (shrink)

As we move our social institutions from paper and ink based operations to the electronic medium, we invisibly create a type of surveillance society, a panopticon society. It is not the traditional surveillance society in which government officials follow citizens around because they are concerned about threats to the political order. Instead it is piecemeal surveillance by public and private organizations. Piecemeal though it is, It creates the potential for the old kind of surveillance on an even grander scale. The (...) panopticon is the prison environment described by Foucault in which prison cells are arranged in a large circle with the side facing the inside of the circle open to view. The guard tower is placed in the middle of the circle so that guards can see everything that goes on in every cell. When we contemplate all the electronic data that is now gathered about each of us as we move through our everyday lives --- intelligent highway systems, consumer transactions, traffic patterns on the internet, medical records, financial records, and so on --- there seems little doubt that we are moving into a panopticon. The social issues that arise from this are too numerous to detail here, but data retention is an important part of it. In the paper-and-ink world, documents are filed, files are boxed, boxes are put away or thrown away. The capacity for data retrieval and manipulation is, thereby, limited by the sheer difficulty and cost of storing, finding, searching, and manipulating large numbers of paper files. This inconvenience functions as a mechanism whereby the system forgets past information, not unlike the way we ourselves forget. However, the story is very different in the digital world; digital information is easy to store, easy to search and manipulate, and inexpensive to keep over extensive periods of time. Digitalized information systems tend, therefore, to collect extensive ancillary information and to retain this information indefinitely. Such lack of forgetfulness is likely to hamper the ability of individuals to shed their past, and start over with a clean slate. Concerns about data retention were expressed in the early literature on the social impact of computing, but for the most part the issue has dropped from sight. Rarely, has the social good of discarding accumulated personal data been addressed. In this paper, we want to make the case by examining diverse cases in which retention of information by either business or governmental institutions hinders the ability of individuals to start over or to act autonomously. We hope our argument for the good of forgetfulness will challenge the standard framework in which such issues have traditionally been debated. The privacy debate exemplifies the traditional framework insofar as it has been characterized as involving an inherent tension between, on the one hand, the needs of organizations and institutions for more accurate and efficient information systems so as to further their goals and, on the other hand, the desire of individuals to have information about them kept private. Regan argues against this framing of the privacy issue in favor of one that recognizes the social importance of personal privacy. We will examine the non-forgetfulness of information systems as a problem threatening not just individual interests but social good as well. Cryptography is often cited as the technology that will give us privacy and mediate against surveillance. One of the uses of cryptography, encryption, will allow us, some hope, to create confidentiality and relationships of trust that will facilitate many of the social arrangements we now have and perhaps make them even more secure than they are now. Electronic cash, for example, could be created in such a form that it would have the anonymity associated now with hard cash Others are less optimistic of the potential for cryptography to re-create relationships of trust in the new medium. One important point that already seems clear is that even if encryption technology will protect the confidentiality and integrity of electronic transactions and data, it will NOT stop the observation of traffic patterns on networks. This seems an important distinction to put on the table. Our patterns of communication will continue to be available, no matter what is encrypted, and an amazing amount of information can be gleaned from this data. In a sense, it means content integrity but no anonymity. This will indubitably impact how we interact and with whom we interact. (shrink)

Due to the growth of neo-liberalism with its emphasis on “market-driven courses,” the humanities, of which philosophy is a part, find themselves disparaged and under-funded. As a result, some African philosophers have yielded to the temptation to deploy the empirical methodology of the natural and social sciences in a bid to illustrate the practical value of their discipline, thereby eroding philosophy’s distinctive characteristic, namely, reflection. Consequently, drawing from the contemporary discourse on methodology in African philosophy, this article argues that in (...) order to preserve the distinctive character of their discipline, philosophers who inquire into indigenous African thought ought to treat the collection of empirical data on the subject as a preliminary step to the core task of philosophical reflection that deploys such techniques as criticism, linguistic analysis, and systematic speculation. Only then will African philosophy make its proper contribution to the interdisciplinary field of African studies. (shrink)

Cognitive states as well as cognitive commodities play central though distinct roles in our epistemological theories. By being attentive to how a difference in their roles affects our way of referring to them, we can undoubtedly accrue our understanding of the structure and functioning of our main epistemological theories. In this paper we propose an analysis of the dichotomy between states and commodities in terms of the method of abstraction, and more specifically by means of infomorphisms between different ways (...) to classify states of information, information-bases, and evidential situations. (shrink)

In this paper we specialize the notion of abstract computational procedure previously introduced for intensionally presented structures to those which are extensionally given. This is provided by a form of generalized recursion theory which uses schemata for explicit definition, conditional definition and least fixed point recursion in functional of type level 2 over any appropriate structure. It is applied here to the case of potentially infinite streams as an abstract data type.

Gilead et al. state that abstraction supports mental travel, and that mental travel critically relies on abstraction. I propose an important addition to this theoretical framework, namely that mental travel might also support abstraction. Specifically, I argue that spontaneous mental travel, much like data augmentation in machine learning, provides variability in mental content and context necessary for abstraction.

This chapter contains sections titled: Introduction Computer Science as the Master of Its Domain The Concept of Law in Computer Science Computer Science Laws as Invariants The Interplay of Freedom and Constraint Conclusion References.

In this book, Zalta attempts to lay the axiomatic foundations of metaphysics by developing and applying a (formal) theory of abstract objects. The cornerstones include a principle which presents precise conditions under which there are abstract objects and a principle which says when apparently distinct such objects are in fact identical. The principles are constructed out of a basic set of primitive notions, which are identified at the end of the Introduction, just before the theorizing begins. The main reason for (...) producing a theory which defines a logical space of abstract objects is that it may have a great deal of explanatory power. It is hoped that the data explained by means of the theory will be of interest to pure and applied metaphysicians, logicians and linguists, and pure and applied epistemologists. (shrink)

ABSTRACT:Technological advances introduce the possibility that, in the future, firms will be able to use big-data analysis to discover and offer consumers their individual reservation price. This can generate some interesting benefits, such as a better state of affairs in terms of equality of both welfare and resources, as well as increased social welfare. However, these benefits are countered by considerations of relational equality. This article takes up the market-failures approach as its basis to demonstrate what is wrong with (...) using big data to personalize prices. The article offers an improvement to the market-failures approach and argues that what is wrong with using big data to personalize prices is that it unfairly undermines consumers’ ability to benefit from the market, which is the very point of having a market. (shrink)

Data science is not simply a method but an organising idea. Commitment to the new paradigm overrides concerns caused by collateral damage, and only a counterculture can constitute an effective critique. Understanding data science requires an appreciation of what algorithms actually do; in particular, how machine learning learns. The resulting ‘insight through opacity’ drives the observable problems of algorithmic discrimination and the evasion of due process. But attempts to stem the tide have not grasped the nature of (...) class='Hi'>data science as both metaphysical and machinic. Data science strongly echoes the neoplatonism that informed the early science of Copernicus and Galileo. It appears to reveal a hidden mathematical order in the world that is superior to our direct experience. The new symmetry of these orderings is more compelling than the actual results. Data science does not only make possible a new way of knowing but acts directly on it; by converting predictions to pre-emptions, it becomes a machinic metaphysics. The people enrolled in this apparatus risk an abstraction of accountability and the production of ‘thoughtlessness’. Susceptibility to data science can be contested through critiques of science, especially standpoint theory, which opposes the ‘view from nowhere’ without abandoning the empirical methods. But a counterculture of data science must be material as well as discursive. Karen Barad’s idea of agential realism can reconfigure data science to produce both non-dualistic philosophy and participatory agency. An example of relevant praxis points to the real possibility of ‘machine learning for the people’. (shrink)

This theme issue has the founding ambition of landscaping Data Ethics as a new branch of ethics that studies and evaluates moral problems related to data (including generation, recording, curation, processing, dissemination, sharing, and use), algorithms (including AI, artificial agents, machine learning, and robots), and corresponding practices (including responsible innovation, programming, hacking, and professional codes), in order to formulate and support morally good solutions (e.g. right conducts or right values). Data Ethics builds on the foundation provided by (...) Computer and Information Ethics but, at the same time, it refines the approach endorsed so far in this research field, by shifting the Level of Abstraction of ethical enquiries, from being information-centric to being data-centric. This shift brings into focus the different moral dimensions of all kinds of data, even the data that never translate directly into information but can be used to support actions or generate behaviours, for example. It highlights the need for ethical analyses to concentrate on the content and nature of computational operations — the interactions among hardware, software, and data — rather than on the variety of digital technologies that enables them. And it emphasises the complexity of the ethical challenges posed by Data Science. Because of such complexity, Data Ethics should be developed from the start as a macroethics, that is, as an overall framework that avoids narrow, ad hoc approaches and addresses the ethical impact and implications of Data Science and its applications within a consistent, holistic, and inclusive framework. Only as a macroethics Data Ethics will provide the solutions that can maximise the value of Data Science for our societies, for all of us, and for our environments. (shrink)

Abstract: Laws of computer science are prescriptive in nature but can have descriptive analogs in the physical sciences. Here, we describe a law of conservation of information in network programming, and various laws of computational motion (invariants) for programming in general, along with their pedagogical utility. Invariants specify constraints on objects in abstract computational worlds, so we describe language and data abstraction employed by software developers and compare them to Floridi's concept of levels of abstraction. We also (...) consider Floridi's structural account of reality and its fit for describing abstract computational worlds. Being abstract, such worlds are products of programmers' creative imaginations, so any "laws" in these worlds are easily broken. The worlds of computational objects need laws in the form of self-prescribed invariants, but the suspension of these laws might be creative acts. Bending the rules of abstract reality facilitates algorithm design, as we demonstrate through the example of search trees. (shrink)

The Abstract Conceptual Feature (ACF) framework predicts that word meaning is represented within a high‐dimensional semantic space bounded by weighted contributions of perceptual, affective, and encyclopedic information. The ACF, like latent semantic analysis, is amenable to distance metrics between any two words. We applied predictions of the ACF framework to abstract words using eyetracking via an adaptation of the classical “visual word paradigm” (VWP). Healthy adults (n = 20) selected the lexical item most related to a probe word in a (...) 4‐item written word array comprising the target and three distractors. The relation between the probe and each of the four words was determined using the semantic distance metrics derived from ACF ratings. Eye movement data indicated that the word that was most semantically related to the probe received more and longer fixations relative to distractors. Importantly, in sets where participants did not provide an overt behavioral response, the fixation rates were nonetheless significantly higher for targets than distractors, closely resembling trials where an expected response was given. Furthermore, ACF ratings which are based on individual words predicted eye fixation metrics of probe‐target similarity at least as well as latent semantic analysis ratings which are based on word co‐occurrence. The results provide further validation of Euclidean distance metrics derived from ACF ratings as a measure of one facet of the semantic relatedness of abstract words and suggest that they represent a reasonable approximation of the organization of abstract conceptual space. The data are also compatible with the broad notion that multiple sources of information (not restricted to sensorimotor and emotion information) shape the organization of abstract concepts. While the adapted “VWP” is potentially a more metacognitive task than the classical visual world paradigm, we argue that it offers potential utility for studying abstract word comprehension. (shrink)

Wright claims that “the epistemology of good abstraction principles should be assimilated to that of basic principles of logical inference”. In this paper I follow Wright’s recommendation, but I consider a different epistemology of logic, namely anti-exceptionalism. Anti-exceptionalism’s main contention is that logic is not a priori, and that the choice between rival logics should be based on abductive criteria such as simplicity, adequacy to the data, strength, fruitfulness, and consistency. This paper’s goal is to lay down the (...) foundations for an application of the anti-exceptionalist methodology to abstraction principles. In Sect. 1 I outline my strategy. I show that anti-exceptionalism has a bearing on the so-called Bad Company problem, and explore some versions of anti-exceptionalism about abstraction. I then consider new criteria for choosing between rival abstraction principles, and, in particular, between consistent revisions of Frege’s Basic Law V. I finally consider how the abstractionist can compare between competing criteria, and argue that anti-exceptionalists should be pluralists about abstraction. (shrink)

When creating theory to understand or implement change at the social and/or organizational level, it is generally accepted that part of the theory building process includes a process of abstraction. While the process of abstraction is well understood, it is not so well understood how abstractions “fit” together to enable the creation of better theory. Starting with a few simple ideas, this paper explores one way we work with abstractions. This exploration challenges the traditionally held importance of abstracting (...) concepts from experience. That traditional focus has been one-sided—pushing science toward the discovery of data without the balancing process that occurs with the integration of the data. Without such balance, the sciences have been pushed toward fragmentation. Instead, in the present paper, new emphasis is placed on the relationship between abstract concepts. Specifically, this paper suggests that a better theory is one that is constructed of concepts that exist on a similar level of abstraction. Suggestions are made for quantifying this claim and using the insights to enable scholars and practitioners to create more effective theory. (shrink)

Questions concerning the epistemological status of computer science are, in this paper, answered from the point of view of the formal verification framework. State space reduction techniques adopted to simplify computational models in model checking are analysed in terms of Aristotelian abstractions and Galilean idealizations characterizing the inquiry of empirical systems. Methodological considerations drawn here are employed to argue in favour of the scientific understanding of computer science as a discipline. Specifically, reduced models gained by Dataion are acknowledged as Aristotelian (...) abstractions that include only data which are sufficient to examine the interested executions. The present study highlights how the need to maximize incompatible properties is at the basis of both Abstraction Refinement, the process of generating a cascade of computational models to achieve a balance between simplicity and informativeness, and the Multiple Model Idealization approach in biology. Finally, fairness constraints, imposed to computational models to allow fair behaviours only, are defined as ceteris paribus conditions under which temporal formulas, formalizing software requirements, acquire the status of law-like statements about the software systems executions. (shrink)

This article investigates dilemmas in the archiving and sharing of qualitative data in educational research, critically engaging with practices and debates from across the social sciences. Ethical, epistemological and methodological challenges are examined in reference to open access agendas, the politics of knowledge production, and transformations in research practices in the era of data management. We first consider practical and interpretive decisions in archiving qualitative data, then map current policy and regulatory frameworks governing research data management, (...) taking Australia as a case-study. We argue that governance and protocols for data sharing have not attended sufficiently to the distinctive ethical and methodological dimensions and knowledge claims of qualitative research. Instead, approaches associated with quantitative data are extrapolated in ways which construct an imaginary of decontextualised data, abstracted from the conditions of its production. We further argue for more critical attention to the double-edged affordances and ambivalent effects of data sharing and openness and to how data archives are imagined, constructed and curated. This includes greater acknowledgement of the affective and temporal dynamics involved in data archiving, understanding them as practices of (re)invention that also curate ‘archives for the future’ and help to foster an historicising imaginary in educational research. (shrink)

TheConceptual Feature framework predicts that word meaning is represented within a high-dimensional semantic space bounded by weighted contributions of perceptual, affective, and encyclopedic information. The ACF, like latent semantic analysis, is amenable to distance metrics between any two words. We applied predictions of the ACF framework to abstract words using eyetracking via an adaptation of the classical “visual word paradigm”. Healthy adults selected the lexical item most related to a probe word in a 4-item written word array comprising the target (...) and three distractors. The relation between the probe and each of the four words was determined using the semantic distance metrics derived from ACF ratings. Eye movement data indicated that the word that was most semantically related to the probe received more and longer fixations relative to distractors. Importantly, in sets where participants did not provide an overt behavioral response, the fixation rates were nonetheless significantly higher for targets than distractors, closely resembling trials where an expected response was given. Furthermore, ACF ratings which are based on individual words predicted eye fixation metrics of probe-target similarity at least as well as latent semantic analysis ratings which are based on word co-occurrence. The results provide further validation of Euclidean distance metrics derived from ACF ratings as a measure of one facet of the semantic relatedness of abstract words and suggest that they represent a reasonable approximation of the organization of abstract conceptual space. The data are also compatible with the broad notion that multiple sources of information shape the organization of abstract concepts. While the adapted “VWP” is potentially a more metacognitive task than the classical visual world paradigm, we argue that it offers potential utility for studying abstract word comprehension. (shrink)

ABSTRACT In ethics, the use of empirical data has become more and more popular, leading to a distinct form of applied ethics, namely empirical ethics. This ‘empirical turn’ is especially visible in bioethics. There are various ways of combining empirical research and ethical reflection. In this paper we discuss the use of empirical data in a special form of Reflective Equilibrium (RE), namely the Network Model with Third Person Moral Experiences. In this model, the empirical data consist (...) of the moral experiences of people in a practice. Although inclusion of these moral experiences in this specific model of RE can be well defended, their use in the application of the model still raises important questions. What precisely are moral experiences? How to determine relevance of experiences, in other words: should there be a selection of the moral experiences that are eventually used in the RE? How much weight should the empirical data have in the RE? And the key question: can the use of RE by empirical ethicists really produce answers to practical moral questions? In this paper we start to answer the above questions by giving examples taken from our research project on understanding the norm of informed consent in the field of pediatric oncology. We especially emphasize that incorporation of empirical data in a network model can reduce the risk of self‐justification and bias and can increase the credibility of the RE reached. (shrink)

Traditionally, the investigation of truth has been anchored in a priori reasoning. Cognitive science deviates from this tradition by adding empirical data on how people understand and use concepts. Building on psychophysics and machine learning methods, we introduce conceptual scaling, an approach to map people's understanding of abstract concepts. This approach, allows computing participant-specific conceptual maps from obtained ordinal comparison data, thereby quantifying perceived similarities among abstract concepts. Using this approach, we investigated individual's alignment with philosophical theories on (...) truth and the predictive capacity of conceptual maps. The obtained results indicated that, while people's understanding of truth is multifaceted and encapsulates notions of coherence and authenticity, alignment is best for the correspondence theory of truth. Furthermore, conceptual maps allowed predicting individual outcomes with an accuracy of roughly 70%. This research demonstrates that conceptual scaling offers accurate descriptions of individual's understanding of abstract concepts, behavioral predictions, and quantification of alignment with theoretical perspectives. (shrink)

Methodological innovations have a special status in disciplinary histories, because they can be widely adopted and anonymised. In the 1950s, this occurred to Georges Laplace’s innovative use of 3-dimensional metric Cartesian coordinate system to record the positions of archaeological objects. This paper proposes a conceptual and social history of this process, with a focus on its spatial context, the Pyrenean region (Spain, Basque Country, and France). Main results of this research based on archives, publications, and bibliometric data, include: 1) (...) a critical discussion of the notions concerning authorship of such methodological innovations; 2) a presentation of the lesser-known aspects of Laplace’s method, showing its contribution to the abstraction and formalisation of archaeological observations and data recording; and 3) the identification of an international Pyrenean intellectual lineage of innovation regarding stratigraphy and excavation methods, from the late 19th century to the early 21th century. (shrink)

When creating theory to understand or implement change at the social and/or organizational level, it is generally accepted that part of the theory building process includes a process of abstraction. While the process of abstraction is well understood, it is not so well understood how abstractions “fit” together to enable the creation of better theory. Starting with a few simple ideas, this paper explores one way we work with abstractions. This exploration challenges the traditionally held importance of abstracting (...) concepts from experience. That traditional focus has been one-sided—pushing science toward the discovery of data without the balancing process that occurs with the integration of the data. Without such balance, the sciences have been pushed toward fragmentation. Instead, in the present paper, new emphasis is placed on the relationship between abstract concepts. Specifically, this paper suggests that a better theory is one that is constructed of concepts that exist on a similar level of abstraction. Suggestions are made for quantifying this claim and using the insights to enable scholars and practitioners to create more effective theory. (shrink)

Abstract:For Dewey, growth in the educative process means education that enriches and expands one’s experience as it prepares students for not only a vocation but also entry into and transaction with the world. In few places can we see growth, generally understood, to be occurring as fast as in big data technology. This essay begins with an overview of what big data is, specifically what big data looks like in education as understood through learning management system platforms (...) but also data as understood generally on a college campus through dataveillance, or data surveillance understood as the monitoring or possibility of monitoring of one’s data or online activity. In so doing, this essay acknowledges a selection of privacy concerns and educational challenges resulting from big data. I then proceed to the employment of intelligent technology in education with particular emphasis on algorithms and “algorithmic nudging.” I conclude with a Deweyan analysis of technology as continuous with culture and a discussion about the educational needs for technological environments to aid in the proper cultivation of individuals. (shrink)

This paper is divided into two parts. In the first, I shall briefly analyse the phenomenon of “big data”, and argue that the real epistemological challenge posed by the zettabyte era is small patterns. The valuable undercurrents in the ocean of data that we are accumulating are invisible to the computationally-naked eye, so more and better technology will help. However, because the problem with big data is small patterns, ultimately, the game will be won by those who (...) “know how to ask and answer questions” (Plato, Cratylus, 390c). This introduces the second part, concerning information quality (IQ): which data may be useful and relevant, and so worth collecting, curating, and querying, in order to exploit their valuable (small) patterns? I shall argue that the standard way of seeing IQ in terms of fit-for-purpose is correct but needs to be complemented by a methodology of abstraction, which allows IQ to be indexed to different purposes. This fundamental step can be taken by adopting a bi-categorical approach. This means distinguishing between purpose/s for which some information is produced (P-purpose) and purpose/s for which the same information is consumed (C-purpose). Such a bi-categorical approach in turn allows one to analyse a variety of so-called IQ dimensions, such as accuracy, completeness, consistency, and timeliness. I shall show that the bi-categorical approach lends itself to simple visualisations in terms of radar charts. (shrink)

In lieu of an abstract, here is a brief excerpt of the content:Feminist Studies 45, no. 1. © 2019 by Feminist Studies, Inc. 129 Miriam Kienle Dear Data: Feminist Information Design’s Resistance to Self-Quantification Every Sunday for one year, information designers Giorgia Lupi and Stefanie Posavec sent each other a hand-drawn postcard that featured a data visualization of their week as it pertained to a single aspect of their daily lives: doors opened, clocks checks, sounds heard, smells perceived, (...) and so on (figs. 1–4). With this series of postcards exchanged between Brooklyn and London, Lupi and Posavec gained intimate knowledge of one another through their small/slow data, and at the same time, they produced a critical examination of the capture, interpretation, and visualization of their daily data from a uniquely feminist perspective. Although the field of information design prizes clear, efficient, and seamless presentations of quantified activity, Lupi and Posavec’s Dear Data (2015) is comprised of visualizations that underscore complexity over clarity, present questions rather simply display information, and expose the instability of data itself.1 Against the quantification of embodied experience into seemingly objective and all-encompassing datasets, Dear Data instead looks at data from a feminist perspective, as tied to concrete bodies, localities, and temporalities and visualized in a manner 1. The approach to infographics that prioritizes efficiency, accuracy, and elegance, and thus suppresses the constructed and interpretative nature of both data and its visualization, is perhaps most attributed to Edward Tufte. For Tufte’s aims and methods, see Edward R. Tufte, The Visual Display of Quantitative Information (Cheshire, CT: Graphics Press, 2001). 130 Miriam Kienle that is incomplete, contingent, and constructed.2 This paper will analyze Lupi and Posavec’s designs as providing a model for a feminist approach to data visualization—a model imperative in an age in which the quantitative information (much like a binary understanding of gender) is too often treated as a self-evident fact rather than what feminist philosopher Donna Haraway famously called “situated knowledges.”3 Recently acquired by MoMA’s architecture and design collection, the Dear Data project not only stands out in the collection as the product of two female designers working in the male-dominated fields of technology and design, but also as a project that stresses feminist knowledge production by underscoring how data voids shape datasets and debunking the myth that the world is data that can be captured and visualized with neutral, distanced, and all-encompassing technologies of vision. As Catherine D’Ignazio and Lauren Klein describe in Data Feminism, data visualization from a feminist perspective understands that “bar charts might seem neutral and objective, but are in fact the result of very human and necessarily imperfect design processes.”4 Such a perspective is evident across Dear Data. Take, for example, “Week 42: A week of laughter” (fig. 5). On the key in Posavec’s visualization, she notes: “I tried to capture my laughs which was really hard + got in the way of the enjoying life, hence the data voids. / For a card about laughter I am sad about how this card turned out.”5 This willingness to show the gaps in her data, the complexities of her process, and the imperfections of her visualizations contrasts with the seamless and straightforward displays of information 2. For a feminist approach to data visualization, see Catherine D’Ignazio and Lauren Klein, Data Feminism (Cambridge, MA: MIT Press, 2019), ebook; Catherine D’Ignazio, “What Would Feminist Data Visualization Look Like?” Center for Civic Media. Massachusetts Institute of Technology (blog), December 20, 2015, https://civic.mit.edu/feminist-data-visualization; and Johanna Drucker, Graphesis: Visual Forms of Knowledge Production (Cambridge, MA: Harvard University Press, 2014). 3. Donna Haraway, “Situated Knowledges: The Science Question in Feminism and the Privilege of Partial Perspective,” Feminist Studies 14, no. 3 (1988): 575–99. 4. D’Ignazio and Klein, introduction to their Data Feminism. 5. Giorgia Lupi and Stefanie Posavec, Dear Data (New York: Princeton Architectural Press, 2016), 231. Miriam Kienle 131 or what infographic guru Edward Tufte describes as “Designs so good that they are invisible.”6 Counter to the invisible and seemingly objective visualizations of our personal data to which we have become accustomed (on calorie counters, credit card statements, etc... (shrink)

Georgios Pentzaropoulos continues his investigation undertaken in 'Generating Stable Knowledge via Reduction in Entropy' (Philosophy Pathways Issue 167, 28 November 2011) looking at knowledge not as something we 'possess' -- the knowledge contained in a book, for example -- but rather as an active, or interactive process of increasing understanding through the judicious sifting and processing of raw information. As Macmurray argued in his Gifford Lectures, 'All knowledge is for the sake of action.' Instead of seeking to define some abstract (...) notion of knowledge in a vacuum, what philosophers should be getting to grips with is the notion of actionable knowledge. - Geoffrey Klempner, Editor. (shrink)

This experimental study provides further support for a theory of meaning first put forward by Bar-Hillel and Carnap in 1953 and foreshadowed by Asimov in 1951. The theory is the Popperian notion that the meaningfulness of a proposition is its a priori falsity. We tested this theory in the first part of this paper by translating to logical form a long, tightly written, published text and computed the meaningfulness of each proposition using the a priori falsity measure. We then selected (...) the top propositions—by a priori falsity—and strung them together to form ad hoc abstracts and compared these abstracts with the published summary. The results were startling: translation to logical form, followed by application of the Asimovian idea and Bar-Hillel/Carnap mathematics as elaborated into an AI/NLP proposal in Fulda (1986, 1988), produced excellent abstracts, thereby providing a proof-of-concept that merely by knowing the logical form of long text passages, one can produce reasonable abstracts of them—without actually understanding the text. We here report on a second experiment analyzing, in the exact same manner, the correspondence that followed the published text of the first experiment. While the results of this confirming experiment are less startling, they nevertheless provide additional confidence in the promise of the technique. In other words, were the results of these two experiments to generalize, that would show that logical form captures much more semantics than has heretofore been considered likely. Far from (as is commonly supposed) being merely the syntactical rewrite of text into formal notation, translation to logical form, even when undertaken with almost no knowledge about the particular predicates, individual constants, or other objects referred to in that form, might capture the core of the meaning in some important sense. -/- Note: There is a key difference in the /style/ of the texts analyzed in I and II in this long paper, divided into two only for practical reasons—space; the first text analyzed is written in a tight mathematical style, while the second is written in the discursive style of philosophy. -/- Finally, the unusual (for me) length of this two-part paper is justified by its being data-driven. (shrink)

Background Sharing anonymized/de-identified clinical trial data and publishing research outcomes in scientific journals, or presenting them at conferences, is key to data-driven scientific exchange. However, when data from scientific publications are linked to other publicly available personal information, the risk of reidentification of trial participants increases, raising privacy concerns. Therefore, we defined a set of criteria allowing us to determine and minimize the risk of data reidentification. We also implemented a review process at Takeda for clinical (...) publications prior to submission for publication in journals or presentation at medical conferences. Methods Abstracts, manuscripts, posters, and oral presentations containing study participant information were reviewed and the potential impact on study participant privacy was assessed. Our focus was on direct and indirect identifiers, such as sex, age or geographical indicators in rare disease studies or studies with small sample size treatment groups. Risk minimization was sought using a generalized presentation of identifier-relevant information and decision-making on data sharing for further research. Additional risk identification was performed based on study participant/personnel parameters present in materials destined for the public domain. The potential for participant/personnel identification was then calculated to facilitate presentation of meaningful but de-identified information. Results The potential for reidentification was calculated using a risk ratio of the exposed versus available individuals, with a value above the threshold of 0.09 deemed an unacceptable level of reidentification risk. We found that in 13% of Takeda clinical trial publications reviewed, either individuals could potentially be reidentified or inappropriate data sharing plans could pose a data privacy risk to study participants. In 1/110 abstracts, 58/275 manuscripts, 5/87 posters and 3/58 presentations, changes were necessary due to data privacy concerns/rules. Despite the implementation of risk-minimization measures prior to release, direct and indirect identifiers were found in 11% and 34% of the analysed documents, respectively. Conclusions Risk minimization using de-identification of clinical trial data presented in scientific publications and controlled data sharing conditions improved privacy protection for study participants. Our results also suggest that additional safeguards should be implemented to ensure that higher data privacy standards are met. (shrink)

The dual-code proposal of number representation put forward by Cohen Kadosh & Walsh (CK&W) accounts for only a fraction of the many modes of numerical abstraction. Contrary to their proposal, robust data from human infants and nonhuman animals indicate that abstract numerical representations are psychologically primitive. Additionally, much of the behavioral and neural data cited to support CK&W's proposal is, in fact, neutral on the issue of numerical abstraction.

I propose a framework that explicates and distinguishes the epistemic roles of data and models within empirical inquiry through consideration of their use in scientific practice. After arguing that Suppes’ characterization of data models falls short in this respect, I discuss a case of data processing within exploratory research in plant phenotyping and use it to highlight the difference between practices aimed to make data usable as evidence and practices aimed to use data to represent (...) a specific phenomenon. I then argue that whether a set of objects functions as data or models does not depend on intrinsic differences in their physical properties, level of abstraction or the degree of human intervention involved in generating them, but rather on their distinctive roles towards identifying and characterizing the targets of investigation. The paper thus proposes a characterization of data models that builds on Suppes’ attention to data practices, without however needing to posit a fixed hierarchy of data and models or a highly exclusionary definition of data models as statistical constructs. (shrink)

Abstract When new methods of generating information about individuals leave the confined space of research application the possibility of morally dubious application arises. The current propagation of neuroscientific diagnostics leads to new possibilities of misuse and accordingly new needs for the protection of individual privacy emerge. While most current privacy discussion focuses on sensationalist applications which aim/claim to gather information about psychological traits or even the content of thoughts, the more sober but much more realistic endeavour to gather health (...) class='Hi'>data from research or medical imaging studies is widely neglected. I will try to answer the question if and in how far data from neuroscientific imaging technologies require special protection. Two developments form the background of the ethical discussion: the increased diagnostic power of neuroimaging techniques and the wider distribution of this technology beyond specialized medical offices and clinics. The first development is likely to broaden the scope of data, which are considered relevant for health care and related decisions. The latter is likely to widen the scope of persons who might have access to diagnostic results without at the time taking the role of a doctor towards the person diagnosed. I will argue that neuroimaging data are currently primarily medical data and that the associated standards of consent and confidentiality are worth protecting. Even nonmedical applications of neuroimaging technology inherit too much of the diagnostic power for which they were originally invented, for it to be advisable to drop the accompanying consent and confidentiality requirements. (shrink)

It is illegitimate to read any ontology about "race" off of biological theory or data. Indeed, the technical meaning of "genetic variation" is fluid, and there is no single theoretical agreed-upon criterion for defining and distinguishing populations (or groups or clusters) given a particular set of genetic variation data. Thus, by analyzing three formal senses of "genetic variation"—diversity, differentiation, and heterozygosity—we argue that the use of biological theory for making epistemic claims about "race" can only seem plausible when (...) it relies on the user’s own assumptions about race; the move from biological measures to claims about “race” inevitably amounts to a pernicious reification. We also excavate assumptions in the history of the technical discourse over the concept of "race" (e.g., Livingstone's and Dobzhansky's 1962 exchange, Edwards' 2003 response to Lewontin 1972, as well as contemporary discussions of cladistic "race", and "races" as clusters). We show that claims about the existence (or non-existence) of "race" are underdetermined by biological facts, methods, and theories. Biological theory does not force the concept of "race" upon us; our social discourse, social ontology, and social expectations do. We become prisoners of our abstractions at our own hands, and at our own expense. (shrink)

I propose a framework that explicates and distinguishes the epistemic roles of data and models within empirical inquiry through consideration of their use in scientific practice. After arguing that Suppes’ characterization of data models falls short in this respect, I discuss a case of data processing within exploratory research in plant phenotyping and use it to highlight the difference between practices aimed to make data usable as evidence and practices aimed to use data to represent (...) a specific phenomenon. I then argue that whether a set of objects functions as data or models does not depend on intrinsic differences in their physical properties, level of abstraction or the degree of human intervention involved in generating them, but rather on their distinctive roles towards identifying and characterizing the targets of investigation. The paper thus proposes a characterization of data models that builds on Suppes’ attention to data practices, without however needing to posit a fixed hierarchy of data and models or a highly exclusionary definition of data models as statistical constructs. (shrink)