This paper interrogates the fundamental logic of data-driven decision-making as it has taken hold in education and argues for a critical analysis of data-driven education via an attitude of historical ontology. Though influenced by Foucault’s understanding of this concept, I center Colin Koopman’s recent analysis of the ‘informational person’ to point attention to the ways in which the very formatting of data may be understood as historically contingent and, thus, more contestable. After examining (...) the background of DDDM and relevant critiques of it, I suggest that investigating the socio-historical formations of data itself are important for indicating its potential dangers within education. Such a critical analysis identifies the doubled politics of data formatting, with its ability to tether subjectivity to data, while potentially baking modes of power into a variety of technological data apparatuses. To conclude, I consider Koopman’s work in terms of how it may extend critical studies of educational datafication. (shrink)

Increasing numbers of decisions about everyday life are made using algorithms. By algorithms we mean predictive models (decision rules) captured from historical data using data mining. Such models often decide prices we pay, select ads we see and news we read online, match job descriptions and candidate CVs, decide who gets a loan, who goes through an extra airport security check, or who gets released on parole. Yet growing evidence suggests that decision making by algorithms (...) may discriminate people, even if the computing process is fair and well-intentioned. This happens due to biased or non-representative learning data in combination with inadvertent modeling procedures. From the regulatory perspective there are two tendencies in relation to this issue: (1) to ensure that data-driven decision making is not discriminatory, and (2) to restrict overall collecting and storing of private data to a necessary minimum. This paper shows that from the computing perspective these two goals are contradictory. We demonstrate empirically and theoretically with standard regression models that in order to make sure that decision models are non-discriminatory, for instance, with respect to race, the sensitive racial information needs to be used in the model building process. Of course, after the model is ready, race should not be required as an input variable for decision making. From the regulatory perspective this has an important implication: collecting sensitive personal data is necessary in order to guarantee fairness of algorithms, and law making needs to find sensible ways to allow using such data in the modeling process. (shrink)

The article examines the impact of the virtual public sphere on how urban spaces are experienced and conceived in our data-driven society. It places particular emphasis on urban scale digital twins, which are virtual replicas of cities that are used to simulate environments and develop scenarios in response to policy problems. The article also investigates the shift from the technical to the socio-technical perspective within the field of smart cities. Despite the aspirations of urban scale digital twins to (...) enhance the participation of citizens in the decision-making processes relayed to urban planning strategies, the fact that they are based on a limited set of variables and processes makes them problematic. The article aims to shed light on the tension between the real and the ideal at stake during this process of abstracting sets of variables and processes in the case of urban scale digital twins. (shrink)

Recent frameworks, models, and reports highlight the critical need to address social determinants of health for achieving health equity in the United States and around the globe. In the United States, data play an important role in better understanding community‐level and population‐level disparities particularly for local health departments. However, data‐driven decision‐making—the use of data for public health activities such as program implementation, policy development, and resource allocation—is often presented theoretically or through case studies in (...) the literature. We sought to develop a preliminary model that identifies the factors that contribute to data‐driven decision‐making in US local health departments and describe relationships between them. Guided by implementation science literature, we examined organizational‐level capacity and individual‐level factors contributing to using data for decision‐making related to social determinants of health and the reduction of county‐level disparities. This model has the potential to improve implementation of public health interventions and programs aimed at upstream structural factors, by elucidating the factors critical to incorporating data in decision‐making. (shrink)

This book deals with the theoretical, methodological, and empirical implications of bounded rationality in the operation of institutions. It focuses on decisions made under uncertainty, and presents a reliable strategy of knowledge acquisition for the design and implementation of decision-support systems. Based on the distinction between the inner and outer environment of decisions, the book explores both the cognitive mechanisms at work when actors decide, and the institutional mechanisms existing among and within organizations that make decisions fairly predictable. While (...) a great deal of work has been done on how organizations act as patterns of events for (boundedly) rational decisions, less effort has been devoted to study under which circumstances organizations cease to act as such reliable mechanisms. Through an empirical strategy on open-ended response data from a survey among junior judges, the work pursues two main goals. The first one is to explore the limits of "institutional rationality" of the Spanish lower courts on-call service, an optimal scenario to observe decision-making under uncertainty. The second aim is to achieve a better understanding of the kind of uncertainty under which inexperienced decision-makers work. This entails exploring the demands imposed by problems and the knowledge needed to deal with them, making this book also a study on expertise achievement in institutional environments. This book combines standard multivariate statistical methods with machine learning techniques such as multidimensional scaling and topic models, treating text as data. Doing so, the book contributes to the collaboration between empirical social scientific approaches and the community of scientists that provide the set of tools and methods to make sense of the fastest growing resource of our time: data. (shrink)

The data-driven approach to psychiatric science leverages large volumes of patient data to construct machine learning models with the goal of optimizing clinical decision making. Advocates claim that this methodology is well-placed to deliver transformative improvements to psychiatric science. I argue that talk of a data-driven revolution in psychiatry is premature. Transformative improvements, cashed out in terms of better patient outcomes, cannot be achieved without addressing patient understanding. That is, how patients understand their (...) own mental illnesses. I conceptualize understanding as the possession of adaptive mental constructs through which experience is mediated. I suggest that this notion of understanding serves as a bottleneck which any prospective approach to psychiatry must address to be efficacious. Subsequently I argue that, though the data-driven approach is undoubtedly powerful, it does not have a straightforward means of unblocking the bottleneck of understanding. I suggest that the data-driven approach must be supplemented with significant theoretical progress if it is to transform psychiatry. (shrink)

BackgroundSystems medicine is the name for an assemblage of scientific strategies and practices that include bioinformatics approaches to human biology ; “big data” statistical analysis; and medical informatics tools. Whereas personalized and precision medicine involve similar analytical methods applied to genomic and medical record data, systems medicine draws on these as well as other sources of data. Given this distinction, the clinical translation of systems medicine poses a number of important ethical and epistemological challenges for researchers working (...) to generate systems medicine knowledge and clinicians working to apply it.DiscussionThis article focuses on three key challenges: First, we will discuss the conflicts in decision-making that can arise when healthcare providers committed to principles of experimental medicine or evidence-based medicine encounter individualized recommendations derived from computer algorithms. We will explore in particular whether controlled experiments, such as comparative effectiveness trials, should mediate the translation of systems medicine, or if instead individualized findings generated through “big data” approaches can be applied directly in clinical decision-making. Second, we will examine the case of the Riyadh Intensive Care Program Mortality Prediction Algorithm, pejoratively referred to as the “death computer,” to demonstrate the ethical challenges that can arise when big-data-driven scoring systems are applied in clinical contexts. We argue that the uncritical use of predictive clinical algorithms, including those envisioned for systems medicine, challenge basic understandings of the doctor-patient relationship. Third, we will build on the recent discourse on secondary findings in genomics and imaging to draw attention to the important implications of secondary findings derived from the joint analysis of data from diverse sources, including data recorded by patients in an attempt to realize their “quantified self.”SummaryThis paper examines possible ethical challenges that are likely to be raised as systems medicine to be translated into clinical medicine. These include the epistemological challenges for clinical decision-making, the use of scoring systems optimized by big data techniques and the risk that incidental and secondary findings will significantly increase. While some ethical implications remain still hypothetical we should use the opportunity to prospectively identify challenges to avoid making foreseeable mistakes when systems medicine inevitably arrives in routine care. (shrink)

Machine learning-driven clinical decision support systems (ML-CDSSs) seem impressively promising for future routine and emergency care. However, reflection on their clinical implementation reveals a wide array of ethical challenges. The preferences, concerns and expectations of professional stakeholders remain largely unexplored. Empirical research, however, may help to clarify the conceptual debate and its aspects in terms of their relevance for clinical practice. This study explores, from an ethical point of view, future healthcare professionals’ attitudes to potential changes of responsibility (...) and decision-making authority when using ML-CDSS. Twenty-seven semistructured interviews were conducted with German medical students and nursing trainees. The data were analysed based on qualitative content analysis according to Kuckartz. Interviewees’ reflections are presented under three themes the interviewees describe as closely related: (self-)attribution of responsibility, decision-making authority and need of (professional) experience. The results illustrate the conceptual interconnectedness of professional responsibility and its structural and epistemic preconditions to be able to fulfil clinicians’ responsibility in a meaningful manner. The study also sheds light on the four relata of responsibility understood as a relational concept. The article closes with concrete suggestions for the ethically sound clinical implementation of ML-CDSS. (shrink)

In this paper we argue the need to reimagine research ethics frameworks to include notions of environmental sustainability. While there have long been calls for healthcareethics frameworks and decision-making to include aspects of sustainability, less attention has focused on howresearchethics frameworks could address this. To do this, we first describe the traditional approach to research ethics, which often relies on individualised notions of risk. We argue that we need to broaden this notion of individual risk to consider issues (...) associated with environmental sustainability. This is because research is associated with carbon emissions and other environmental impacts, both of which cause climate change health hazards. We introduce how bioethics frameworks have considered notions of environmental sustainability and draw on these to help develop a framework suitable for researchers. We provide a case study of data-driven health research to apply our framework. (shrink)

Organizations increasingly rely on algorithm-based HR decision-making to monitor their employees. This trend is reinforced by the technology industry claiming that its decision-making tools are efficient and objective, downplaying their potential biases. In our manuscript, we identify an important challenge arising from the efficiency-driven logic of algorithm-based HR decision-making, namely that it may shift the delicate balance between employees’ personal integrity and compliance more in the direction of compliance. We suggest that critical (...) class='Hi'>data literacy, ethical awareness, the use of participatory design methods, and private regulatory regimes within civil society can help overcome these challenges. Our paper contributes to literature on workplace monitoring, critical data studies, personal integrity, and literature at the intersection between HR management and corporate responsibility. (shrink)

Perceptions of criminality and remorse are critical for legal decision-making. While faces perceived as criminal are more likely to be selected in police lineups and to receive guilty verdicts, faces perceived as remorseful are more likely to receive less severe punishment recommendations. To identify the information that makes a face appear criminal and/or remorseful, we successfully used two different data-driven computational approaches that led to convergent findings: one relying on the use of computer-generated faces, and the (...) other on photographs of people. In addition to visualising and validating the perceived looks of criminality and remorse, we report correlations with earlier face models of dominance, threat, trustworthiness, masculinity/femininity, and sadness. The new face models of criminal and remorseful appearance contribute to our understanding of perceived criminality and remorse. They can be used to study the effects of perceived criminality and remorse on decision-making; research that can ultimately inform legal policies. (shrink)

The proliferation of big data analytics in criminal justice suggests that there are positive frames and imaginaries legitimising them and depicting them as the panacea for efficient crime control. Criminological and criminal justice scholarship has paid insufficient attention to these frames and their accompanying narratives. To address the gap created by the lack of theoretical and empirical insight in this area, this article draws on a study that systematically reviewed and compared multidisciplinary academic abstracts on the data-driven (...) tools now shaping decision-making across several justice systems. Using insights distilled from the study, the article proposes three frames for understanding how the technologies are portrayed. Inherent in the frames are a set of narratives emphasising their ostensible status as vital crime control mechanisms. These narratives obfuscate the harms of data-driven technologies and evince idealistic imaginaries of their capabilities. The narratives are bolstered by unequal structural arrangements, specifically the unevenly distributed digital capital with which some are empowered to participate in technology development for criminal justice application and other forms of penal governance. In unravelling these issues, the article advances current understanding of the dynamics that sustain the depiction of data-driven technologies as prime crime prevention and law enforcement tools. (shrink)

Data-driven, decision-making technologies used in the justice system to inform decisions about bail, parole, and prison sentencing are biased against historically marginalized groups (Angwin, Larson, Mattu, & Kirchner 2016). But these technologies’ judgments—which reproduce patterns of wrongful discrimination embedded in the historical datasets that they are trained on—are well-evidenced. This presents a puzzle: how can we account for the wrong these judgments engender without also indicting morally permissible statistical inferences about persons? I motivate this puzzle and (...) attempt an answer. (shrink)

The growing uses of algorithm-based decision-making in human resources management have drawn considerable attention from different stakeholders. While prior literature mainly focused on stakeholders directly related to HR decisions (e.g., employees), this paper pertained to a third-party observer perspective and investigated how consumers would respond to companies’ adoption of algorithm-based HR decision-making. Through five experimental studies, we showed that the adoption of algorithm-based (vs. human-based) HR decision-making could induce consumers’ unfavorable ethicality inferences of the (...) company (study 1); because implementing a calculative and data-driven approach (i.e. algorithm-based) to make employee-related decisions violates the deontological principles of respectful employee treatment (study 2). However, this effect was attenuated when consumers had high (vs. low) power distance beliefs (study 3); the algorithm served as assistance (vs. replacement) for human decisions (study 4); or the adoption was framed as employee-oriented (vs. company-oriented) motivated (study 5). Our findings suggested that consumers are aversive to algorithm-based HR decision-making because it is deontologically problematic regardless of its decision quality (i.e. accuracy). This paper contributes to the extant understanding of stakeholders’ responses to algorithm-based HR decision-making and consumers’ attitudes toward algorithm users. (shrink)

This paper draws lessons from the COVID-19 pandemic for the relationship between data-driven decision making and global development. The lessons are that users should keep in mind the shifting value of data during a crisis, and the pitfalls its use can create; predictions carry costs in terms of inertia, overreaction and herding behaviour; data can be devalued by digital and data deluges; lack of interoperability and difficulty reusing data will limit value from (...) data; data deprivation, digital gaps and digital divides are not just a by-product of unequal global development, but will magnify the unequal impacts of a global crisis, and will be magnified in turn by global crises; having more data and even better data analytical techniques, such as artificial intelligence, does not guarantee that development outcomes will improve; decentralised data gathering and use can help to build trust – particularly important for coordination of behaviour. (shrink)

Decision support systems, which incorporate artificial intelligence and big data, are receiving significant attention in the public sector. Decision support systems are sociocultural artefacts that are subject to a mix of technical and political choices, and critical investigation of these choices and the rationales they reflect are paramount since they are inscribed into and may cause harm, violate fundamental rights and reproduce negative social patterns. Applying and merging the concepts of sense-making and translation, this article investigates (...) the rationales, translations and critical reflections that shape the development of a decision support system to support social workers assessing referrals concerning child neglect. It presents findings from a qualitative case study conducted in 2019–2020 at the Citizen Centre Children and Young People, Copenhagen Municipality, Denmark. The analysis shows how key actors through processes of translation construct, negotiate and readjust problem definitions, roles, interests, responsibilities and ideas of ambiguity and accountability. Although technological solutionism is present in these processes, it is not the only rationale invested. Rather, technological and data-driven rationales are adjusted to and merged with rationales of efficiency, return on investment and child welfare. Through continuous renegotiation of roles, responsibilities and problems according to these rationales, the key actors attempt to orchestrate ways of managing the complexity facing child welfare services by projecting images of future potentials of the decision support system that are yet to be realised. (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 idea that socially responsible investments can be viewed in terms of real options is relatively new. We expand on this notion by demonstrating how real option theory, within a Bayesian decision-making framework, can be used by managers to help when making green technology investment decisions. The Bayesian decision framework provides a more flexible approach to investment decision making because it adjusts for new information. Responding to a call for multidisciplinary and multifaceted research in (...) environmental sustainability, this paper integrates ethics, finance, and information technology by viewing investments in environmentally friendly technology as a profit-driven CSR real option. Our model provides managers with the analytic tool needed to make the business case for CSR initiatives providing an opportunity for firms to create economic, social, and ecological solutions that benefit all stakeholders. The practical applicability of our model is demonstrated in an illustrative case scenario based on real data. (shrink)

AI-driven decisions can draw data from virtually any area of your life to make a decision about virtually any other area of your life. That creates fairness issues. Effective regulation to ensure fairness requires that AI systems be transparent. That is, regulators must have sufficient access to the factors that explain and justify the decisions. One approach to transparency is to require that systems be explainable, as that concept is understood in computer science. A system is explainable (...) if one can provide a human-understandable explanation of why it makes any particular prediction. Explainability should not be equated with transparency. To address transparency and characterize its relation to explainability, we define transparency for a regulatory purpose. A system is transparent for a regulatory purpose (r-transparent) when and only when regulators have an explanation, adequate for that purpose, of why it yields the predictions it does. Explainability remains relevant to transparency but turns out to be neither necessary nor sufficient for it. The concepts of explainability and r-transparency combine to yield four possibilities: explainable and either r-transparent or not; and not explainable and either not r-transparent or r-transparent. Combining r-transparency with ideas from the Harvard computer scientist Cynthia Dwork, we propose four requirements on AI systems. (shrink)

In an increasingly data-driven world, the question of data – or metadata – veracity is now a central issue not only in the world of information but also in the legal one. Data veracity describes a closeness to truth on a higher level than a measure such as accuracy does. High veracity data is data that can be relied upon when making decisions, thus reducing the risk of basing choices on untrue information. The (...) article uses epistemic logic to model structured metadata automatically extracted from legal papers, and the tools of metaontology to propose a definition of veracity as truthmaker. (shrink)

In the sacrificial moral dilemma task, participants have to morally judge an action that saves several lives at the cost of killing one person. According to the dual process corrective model of moral judgment suggested by Greene and collaborators (2001; 2004; 2008), cognitive control is necessary to override the intuitive, deontological force of the norm against killing and endorse the utilitarian perspective. However, a conflict model has been proposed more recently to account for part of the evidence in favor of (...) dual process models in moral and social decision making. In this model, conflict, moral responses and reaction times arise from the interplay between individually variable motivational factors and objective parameters intrinsic to the choices offered. To further explore this model in the moral dilemma task, we confronted three different samples with a set of dilemmas representing an objective gradient of utilitarian pull, and collected data on moral judgment and on conflict in a 4-point scale. Collapsing all cases along the gradient, participants in each sample felt less conflicted on average when they gave extreme responses (1 or 4 in the UR scale). They felt less conflicted on average when responding to either the low- or the high-pull cases. The correlation between utilitarian responses and conflict was positive in the low-pull and negative in the high-pull cases. This pattern of data suggests that moral responses to sacrificial dilemmas are driven by decision conflict, which in turn depends on the interplay between an objective gradient of utilitarian pull and the moral motivations which regulate individual responsiveness to this gradient. (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)

In their practice, nurses make daily decisions that are ethically informed. An ethical decision is the result of a complex reasoning process based on knowledge and experience and driven by ethical values. Especially in acute elderly care and more specifically decisions concerning the use of physical restraint require a thoughtful deliberation of the different values at stake. Qualitative evidence concerning nurses’ decision-making in cases of physical restraint provided important insights in the complexity of decision-making (...) as a trajectory. However a nuanced and refined understanding of the reasoning process in terms of ethical values is still lacking. A qualitative interview design, inspired by the Grounded Theory approach, was carried out to explore nurses’ reasoning process in terms of ethical values. We interviewed 21 acute geriatric nurses from 12 hospitals in different regions in Flanders, Belgium in the period October 2009–April 2011. The Qualitative Analysis Guide of Leuven was used to analyse interview data. Nurses’ decision-making is characterized as an ethical deliberation process where different values are identified and where the process of balancing these values forms the essence of ethical deliberation. Ethical decision-making in cases of physical restraint implies that nurses have to choose which values receive priority in the process, which entails that not all values can be respected to the same degree. As a result, decision making can be experienced as difficult, even as a dilemma. Driven by the overwhelming goal of protecting physical integrity, nurses took into account the values of dignity and justice more implicitly and less dominantly. (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)

Background Given that AI-driven decision support systems (AI-DSS) are intended to assist in medical decision making, it is essential that clinicians are willing to incorporate AI-DSS into their practice. This study takes as a case study the use of AI-driven cardiotography (CTG), a type of AI-DSS, in the context of intrapartum care. Focusing on the perspectives of obstetricians and midwives regarding the ethical and trust-related issues of incorporating AI-driven tools in their practice, this paper (...) explores the conditions that AI-driven CTG must fulfill for clinicians to feel justified in incorporating this assistive technology into their decision-making processes regarding interventions in labor. Methods This study is based on semi-structured interviews conducted online with eight obstetricians and five midwives based in England. Participants were asked about their current decision-making processes about when to intervene in labor, how AI-driven CTG might enhance or disrupt this process, and what it would take for them to trust this kind of technology. Interviews were transcribed verbatim and analyzed with thematic analysis. NVivo software was used to organize thematic codes that recurred in interviews to identify the issues that mattered most to participants. Topics and themes that were repeated across interviews were identified to form the basis of the analysis and conclusions of this paper. Results There were four major themes that emerged from our interviews with obstetricians and midwives regarding the conditions that AI-driven CTG must fulfill: (1) the importance of accurate and efficient risk assessments; (2) the capacity for personalization and individualized medicine; (3) the lack of significance regarding the type of institution that develops technology; and (4) the need for transparency in the development process. Conclusions Accuracy, efficiency, personalization abilities, transparency, and clear evidence that it can improve outcomes are conditions that clinicians deem necessary for AI-DSS to meet in order to be considered reliable and therefore worthy of being incorporated into the decision-making process. Importantly, healthcare professionals considered themselves as the epistemic authorities in the clinical context and the bearers of responsibility for delivering appropriate care. Therefore, what mattered to them was being able to evaluate the reliability of AI-DSS on their own terms, and have confidence in implementing them in their practice. (shrink)

BackgroundThe public and healthcare workers have a high expectation of animal research which they perceive as necessary to predict the safety and efficacy of drugs before testing in clinical trials. However, the expectation is not always realised and there is evidence that the research often fails to stand up to scientific scrutiny and its 'predictive value' is either weak or absent.DiscussionProblems with the use of animals as models of humans arise from a variety of biases and systemic failures including: 1) (...) bias and poor practice in research methodology and data analysis ; 2) lack of transparency in scientific assessment and regulation of the research; 3) long-term denial of weaknesses in cross-species translation; 4) profit-driven motives overriding patient interests; 5) lack of accountability of expenditure on animal research; 6) reductionist-materialism in science which tends to dictate scientific inquiry and control the direction of funding in biomedical research.SummaryBias in animal research needs to be addressed before medical research and healthcare decision-making can be more evidence-based. Research funding may be misdirected on studying 'disease mechanisms' in animals that cannot be replicated outside tightly controlled laboratory conditions, and without sufficient critical evaluation animal research may divert attention away from avenues of research that hold promise for human health. The potential for harm to patients and trial volunteers from reliance on biased animal data1 requires measures to improve its conduct, regulation and analysis. This article draws attention to a few of the many forms of bias in animal research that have come to light in the last decade and offers a strategy incorporating ten recommendations stated at the end of each section on bias. The proposals need development through open debate and subsequent rigorous implementation so that reviewers may determine the value of animal research to human health. The 10Rs + are protected by a Creative Commons Attribution 3.0 Unported License and therefore may be 'shared, remixed or built on, even commercially, so long as attributed by giving appropriate credit with a link to the license, and indicate if changes were made.’. (shrink)

Decision-making assisted by algorithms developed by machine learning is increasingly determining our lives. Unfortunately, full opacity about the process is the norm. Would transparency contribute to restoring accountability for such systems as is often maintained? Several objections to full transparency are examined: the loss of privacy when datasets become public, the perverse effects of disclosure of the very algorithms themselves, the potential loss of companies’ competitive edge, and the limited gains in answerability to be expected since sophisticated algorithms (...) usually are inherently opaque. It is concluded that, at least presently, full transparency for oversight bodies alone is the only feasible option; extending it to the public at large is normally not advisable. Moreover, it is argued that algorithmic decisions preferably should become more understandable; to that effect, the models of machine learning to be employed should either be interpreted ex post or be interpretable by design ex ante. (shrink)

Decision-making assisted by algorithms developed by machine learning is increasingly determining our lives. Unfortunately, full opacity about the process is the norm. Would transparency contribute to restoring accountability for such systems as is often maintained? Several objections to full transparency are examined: the loss of privacy when datasets become public, the perverse effects of disclosure of the very algorithms themselves (“gaming the system” in particular), the potential loss of companies’ competitive edge, and the limited gains in answerability to (...) be expected since sophisticated algorithms usually are inherently opaque. It is concluded that, at least presently, full transparency for oversight bodies alone is the only feasible option; extending it to the public at large is normally not advisable. Moreover, it is argued that algorithmic decisions preferably should become more understandable; to that effect, the models of machine learning to be employed should either be interpreted ex post or be interpretable by design ex ante. (shrink)

Numerous studies have shed some light on the importance of associated factors of collaborative attitudes. However, most previous studies aimed to explore the influence of these factors in isolation. With the strategy of data-driven decision making, the current study applied two data mining methods to elucidate the most significant factors of students' attitudes toward collaboration and group students to draw a concise model, which is beneficial for educators to focus on key factors and make effective (...) interventions at a lower cost. Structural equation model trees and structural equation model forests were applied to the Program for International Student Assessment 2015 dataset. By establishing the most important predictors and the splitting rules, these methods constructed multigroup common factor models of collaborative attitudes. The SEM trees showed that home educational resources, home possessions, mother's education, and gender were the most important predictors among the demographic variables, drawing a 5-group model. Among all the predictors, achievement motivation and sense of belonging at school were the most important, drawing a 6-group model. The SEM forest findings proved the relative importance of these variables. This paper discusses various interpretations of these results and their implications for educators to formulate corresponding interventions. Methodologically, this research provides a data mining approach to discover important information from large-scale educational data, which might be a complementary approach to enhance data-driven decision making in education. (shrink)

How are the contemporary conceptions of the living body and health related to numerical systems? Addressing the contemporary practice of quantification of bodies and health, such a question is bound to arise. As a discipline historically positioned amidst natural sciences, technology, and art, medicine has always been sensitive to theories and apparatuses able to quantify and reshape the living body, as well as to the practical possibility of operating on it. This is why, in the era where telecommunication, algorithmic information (...) technology, and data-driven decision making are reaching their apex, new forms of medical practice, and new alliances between medicine and technology are emerging as a direct consequence of the evolution of technology. This is what was particularly aimed at by the International Congress "The Quantification of Bodies. Organism, Health, and Representation", which took place at the University of Coimbra on 28-29 November 2019, organized by the IEF - Institute for Philosophical Studies. The present book collects some of the papers presented there, selected according to their interdisciplinary approach and/or to their more informative nature, suitable even for non-specialist readers. A collection that reflects, in a fragment, many of the potentialities opened by this new field of studies. (shrink)

Decision-making assisted by algorithms developed by machine learning is increasingly determining our lives. Unfortunately, full opacity about the process is the norm. Would transparency contribute to restoring accountability for such systems as is often maintained? Several objections to full transparency are examined: the loss of privacy when datasets become public, the perverse effects of disclosure of the very algorithms themselves, the potential loss of companies’ competitive edge, and the limited gains in answerability to be expected since sophisticated algorithms (...) usually are inherently opaque. It is concluded that, at least presently, full transparency for oversight bodies alone is the only feasible option; extending it to the public at large is normally not advisable. Moreover, it is argued that algorithmic decisions preferably should become more understandable; to that effect, the models of machine learning to be employed should either be interpreted ex post or be interpretable by design ex ante. (shrink)

Computer models and simulations have become, since the 1960s, an essential instrument for scientific inquiry and political decision making in several fields, from climate to life and social sciences. Philosophical reflection has mainly focused on the ontological status of the computational modeling, on its epistemological validity and on the research practices it entails. But in computational sciences, the work on models and simulations are only two steps of a longer and richer process where operations on data are (...) as important as, and even more time and energy-consuming than modeling itself. Drawing on two study cases—computational embryology and computational epidemiology—this article contributes to filling the gap by focusing on the operations of producing and re-using data in computational sciences. The different phases of the scientific and artisanal work of modelers include data collection, aggregation, homogenization, assemblage, analysis and visualization. The article deconstructs the ideas that data are self-evident informational aggregates and that data-driven approaches are exempted from theoretical work. More importantly, the paper stresses the fact that data are constructed and theory laden not only in their fabrication, but also in their reusing. (shrink)

This article provides an ethnographic account of how Big Data biology is produced, interpreted, debated, and translated in a Big Data-driven cancer clinical trial, entitled “Personalized OncoGenomics,” in Vancouver, Canada. We delve into epistemological differences between clinical judgment, pathological assessment, and bioinformatic analysis of cancer. To unpack these epistemological differences, we analyze a set of gazes required to produce Big Data biology in cancer care: clinical gaze, molecular gaze, and informational gaze. We are concerned with the (...) interactions of these bodily gazes and their interdependence on each other to produce Big Data biology and translate it into clinical knowledge. To that end, our central research questions ask: How do medical practitioners and data scientists interact, contest, and collaborate to produce and translate Big Data into clinical knowledge? What counts as actionable and reliable data in cancer decision-making? How does the explicability or translatability of genomic Big Data come to redefine or contradict medical practice? The article contributes to current debates on whether Big Data engenders new questions and approaches to biology, or Big Data biology is merely an extension of early modern natural history and biology. This ethnographic account will highlight how genomic Big Data, which underpins the mechanism of personalized medicine, allows oncologists to understand and diagnose cancer in a different light, but it does not revolutionize or disrupt medical oncology on an institutional level. Rather, personalized medicine is interdependent on different styles of thought, gaze, and practice to be produced and made intelligible. (shrink)

Adolescents present a puzzle. There are foundational unclarities about how they should be regarded as decision-makers. Although superficially adolescents may appear to have mature decisional capacity, their decision-making is in many ways unlike that of adults. Despite this seemingly obvious fact, a concern for the claims of autonomy has led to the development of the legal doctrine of the mature minor. This legal construct considers adolescents, as far as possible, as equivalent to adults for the purpose of (...) medical decision-making. The movement to support independent decision-making by adolescents through providing information to them and securing their consent apart from their parents is encouraged by those legal understandings that hold that unemancipated minors should generally be considered as possessing effective decisional capacity. Such legal structures, however, do not adequately take account of the wide variations in adolescent capacities, the immaturity of most adolescent decision-makers, or the important contributions made by parents to the development of their adolescents through parental partnering in the adolescent’s decision-making. The data available indicate that in general adolescents should be regarded as apprentice decision-makers who should make decisions in collaboration with their parents until at least the age of 18. Steps should not be taken pre-emptively to isolate adolescents from the guidance of their parents. As a general rule, what Piker has referred to as “collaborative paternalism” appears most likely both to protect adolescents from their own untoward choices, while also very importantly helping them with parental guidance to develop into mature decision-makers with the capacity to make medical choices on their own. (shrink)

First Published in 2005. Routledge is an imprint of Taylor & Francis, an informa company.

Data‐driven algorithms are widely used to make or assist decisions in sensitive domains, including healthcare, social services, education, hiring, and criminal justice. In various cases, such algorithms have preserved or even exacerbated biases against vulnerable communities, sparking a vibrant field of research focused on so‐called algorithmic biases. This research includes work on identification, diagnosis, and response to biases in algorithm‐based decision‐making. This paper aims to facilitate the application of philosophical analysis to these contested issues by providing (...) an overview of three key topics: What is algorithmic bias? Why and how can it occur? What can and should be done about it? Throughout, we highlight connections—both actual and potential—with philosophical ideas and concerns. (shrink)

Health information technology in hospitals can be approached as a tool to reduce health care costs and improve hospital efficiency and profitability, increase the quality of healthcare services, and make the transition to patient-centered healthcare. A hospital’s efficiency and profitability depends on linking IT with the knowledge and motivation of medical personnel. It is important to design and execute a knowledge management strategy as a part of the implementation of IT in hospital management. A Diagnosis-Related Groups system was introduced in (...) Poland in 2008 as a basis for settlements between hospitals and the National Health Fund. The importance and role of a DRG system in management of healthcare entities was emphasized based on a survey of medical professionals from two hospitals in the Lubelskie province. The goal of a survey is to assess the knowledge of medical professionals about the DRG system and how the medical personnel uses the DRG system in order to achieve the strategic goals of the organization. A newly developed survey was used to assess the medical personnel’s knowledge of DRG, using 12 closed and 5 open questions. The survey was conducted on 160 medical employees from two hospitals in the Lubelskie province. In conclusion, medical personnel’s DRG knowledge unambiguously contributes to reducing hospital costs and increasing profitability. The DRG related knowledge enables personnel to obtain value from data by applying DRG data-driven decisions. (shrink)

AbstractAdvances in precision agriculture, driven by big data technologies and machine learning algorithms can transform agriculture by enhancing crop and livestock productivity and supporting faster and more accurate on and off-farm decision making. However, little is known about how PA can influence farmers’ sense of self, their skills and competencies, and the meanings that farmers ascribe to farming. This study is animated by scholarly commitment to social identity research, and draws from socio-cyber-physical systems research, domestication theory, (...) and activity theory. This conceptualization of PA within these theoretical perspectives helps to render visible how big agricultural data and machine learning algorithms can affect meaning, doing, and being for US farmers. Through analysis of data from six focus group discussions and follow-up surveys with stakeholders across the PA value chain, this paper shows that PA tools can necessitate farmers to learn and develop new competencies such as flying drones and interpreting yield maps. At the same time, PA can shape new meaning of farm work and new expectation about a ‘good farmer’, changing what it means to be a ‘successful’ farmer from someone who is not only a data observer or data gatherer but also validators of PA models by using their local knowledge of agronomic and environmental phenomenon. We conclude that PA can alter social expectations about farming by reorienting the role of farmers. Policymakers and agriculture extension and outreach programmers can develop more socially relevant PA knowledge and innovation if they can attend to both new and traditional ‘good farmer’ identities. (shrink)

. Network data analysis helps in capturing node usage behavior. Existing algorithms use reduced feature set to manage high runtime complexity. Ignoring features may increase classification errors. This paper presents a model, allowing classification of network traffic, while considering all the relevant features. Learning phase partitions training sample on values of the respective features. This creates equivalence classes related to m features. During classification, each feature value of the test instance results in picking one set from equivalence class generated (...) during learning. Algorithm captures new behavior in semi-supervised incremental learning mode. For problems having m features and n training samples the model has incremental learning complexity of and average classification complexity is of the order. (shrink)

This article explores the trend of increasing automation in law enforcement and criminal justice settings through three use cases: predictive policing, machine evidence and recidivism algorithms. The focus lies on artificial-intelligence-driven tools and technologies employed, whether at pre-investigation stages or within criminal proceedings, in order to decode human behaviour and facilitate decision-making as to whom to investigate, arrest, prosecute, and eventually punish. In this context, this article first underlines the existence of a persistent dilemma between the goal (...) of increasing the operational efficiency of police and judicial authorities and that of safeguarding fundamental rights of the affected individuals. Subsequently, it shifts the focus onto key principles of criminal procedure and the presumption of innocence in particular. Using Article 6 ECHR and the Directive (EU) 2016/343 as a starting point, it discusses challenges relating to the protective scope of presumption of innocence, the burden of proof rule and the _in dubio pro reo_ principle as core elements of it. Given the transformations law enforcement and criminal proceedings go through in the era of algorithms, big data and artificial intelligence, this article advocates the adoption of specific procedural safeguards that will uphold rule of law requirements, and particularly transparency, fairness and explainability. In doing so, it also takes into account EU legislative initiatives, including the reform of the EU data protection _acquis_, the E-evidence Proposal, and the Proposal for an EU AI Act. Additionally, it argues in favour of revisiting the protective scope of key fundamental rights, considering, _inter alia_, the new dimensions suspicion has acquired. (shrink)

Background Big data and AI applications now play a major role in many health contexts. Much research has already been conducted on ethical and social challenges associated with these technologies. Likewise, there are already some studies that investigate empirically which values and attitudes play a role in connection with their design and implementation. What is still in its infancy, however, is the comparative investigation of the perspectives of different stakeholders. Methods To explore this issue in a multi-faceted manner, we (...) conducted semi-structured interviews as well as focus group discussions with patients and clinicians. These empirical methods were used to gather interviewee’s views on the opportunities and challenges of medical AI and other data-intensive applications. Results Different clinician and patient groups are exposed to medical AI to differing degrees. Interviewees expect and demand that the purposes of data processing accord with patient preferences, and that data are put to effective use to generate social value. One central result is the shared tendency of clinicians and patients to maintain individualistic ascriptions of responsibility for clinical outcomes. Conclusions Medical AI and the proliferation of data with import for health-related inferences shape and partially reconfigure stakeholder expectations of how these technologies relate to the decision-making of human agents. Intuitions about individual responsibility for clinical outcomes could eventually be disrupted by the increasing sophistication of data-intensive and AI-driven clinical tools. Besides individual responsibility, systemic governance will be key to promote alignment with stakeholder expectations in AI-driven and data-intensive health settings. (shrink)

In patient centred care, shared decision making is a central feature and widely referred to as a norm for patient centred medical consultation. However, it is far from clear how to distinguish SDM from standard models and ideals for medical decision making, such as paternalism and patient choice, and e.g., whether paternalism and patient choice can involve a greater degree of the sort of sharing involved in SDM and still retain their essential features. In the article, (...) different versions of SDM are explored, versions compatible with paternalism and patient choice as well as versions that go beyond these traditional decision making models. Whenever SDM is discussed or introduced it is of importance to be clear over which of these different versions are being pursued, since they connect to basic values and ideals of health care in different ways. It is further argued that we have reason to pursue versions of SDM involving, what is called, a high level dynamics in medical decision-making. This leaves four alternative models to choose between depending on how we balance between the values of patient best interest, patient autonomy, and an effective decision in terms of patient compliance or adherence: Shared Rational Deliberative Patient Choice, Shared Rational Deliberative Paternalism, Shared Rational Deliberative Joint Decision, and Professionally Driven Best Interest Compromise. In relation to these models it is argued that we ideally should use the Shared Rational Deliberative Joint Decision model. However, when the patient and professional fail to reach consensus we will have reason to pursue the Professionally Driven Best Interest Compromise model since this will best harmonise between the different values at stake: patient best interest, patient autonomy, patient adherence and a continued care relationship. (shrink)

Significant criticisms have been raised regarding the ethical and psychological basis of living wills. Various solutions to address these criticisms have been advanced, such as the use of surrogate decision makers alone or data science‐driven algorithms. These proposals share a fundamental weakness: they focus on resolving the problems of living wills, and, in the process, lose sight of the underlying ethical principle of advance care planning, autonomy. By suggesting that the same sweeping solutions, without opportunities for choice, (...) be applied to all, individual patients are treated as population‐level groups—as a theoretical patient who represents a population, not the specific patient crafting his or her individualized future care plans. Instead, advance care planning can be improved through a multimodal approach that both mitigates cognitive biases and allows for customization of the decision‐making process by allowing for the incorporation of a variety of methods of advance care planning. (shrink)

When making a decision, humans consider two types of information: information they have acquired through their prior experience of the world, and further information they gather to support the decision in question. Here, we present evidence that data from search engines such as Google can help us model both sources of information. We show that statistics from search engines on the frequency of content on the Internet can help us estimate the statistical structure of prior experience; (...) and, specifically, we outline how such statistics can inform psychological theories concerning the valuation of human lives, or choices involving delayed outcomes. Turning to information gathering, we show that search query data might help measure human information gathering, and it may predict subsequent decisions. Such data enable us to compare information gathered across nations, where analyses suggest, for example, a greater focus on the future in countries with a higher per capita GDP. We conclude that search engine data constitute a valuable new resource for cognitive scientists, offering a fascinating new tool for understanding the human decision-making process. (shrink)

Data mining, as an essential part of artificial intelligence, is a powerful digital technology, which makes businesses predict future trends and alleviate the process of decision-making and enhancing customer experience along their digital transformation journey. This research provides a practical implication – a case study - to provide guidance on analyzing information and predicting repairs in home appliances after sales services business. The main benefit of this practical comparative study of various classification algorithms, by using the Weka (...) tool, is the analysis of information and the prediction of repairs in the home appliances after sales services business. The comparison of algorithms is performed considering different parameters, such as the mean absolute error, root mean square error, relative absolute error and root relative squared error, receiver operating characteristic area, accuracy, Matthews’s correlation coefficient, precision-recall curve, precision, F-measure, recall and statistical criteria. Five classification algorithms such as the Naive Bayes, J48, random forest, K-Nearest Neighbor, and logistic regression were implemented in the dataset. J48 has proved to provide the best accuracy and the lowest error among the other examined algorithms applied to a home appliances after sales services dataset to predict repairs based on product guarantee period. The extracted information and results of an after sales services business by using data mining techniques prove to alleviate the process of streamlining decision-making and provide reliable predictions, especially for the customers, as well as increase businesses’ efficiency along their digital transformation journey. (shrink)

Data-driven predictive algorithms are widely used to automate and guide high-stake decision making such as bail and parole recommendation, medical resource distribution, and mortgage allocation. Nevertheless, harmful outcomes biased against vulnerable groups have been reported. The growing research field known as 'algorithmic fairness' aims to mitigate these harmful biases. Its primary methodology consists in proposing mathematical metrics to address the social harms resulting from an algorithm's biased outputs. The metrics are typically motivated by -- or substantively (...) rooted in -- ideals of distributive justice, as formulated by political and legal philosophers. The perspectives of feminist political philosophers on social justice, by contrast, have been largely neglected. Some feminist philosophers have criticized the local scope of the paradigm of distributive justice and have proposed corrective amendments to surmount its limitations. The present paper brings some key insights of feminist political philosophy to algorithmic fairness. The paper has three goals. First, I show that algorithmic fairness does not accommodate structural injustices in its current scope. Second, I defend the relevance of structural injustices -- as pioneered in the contemporary philosophical literature by Iris Marion Young -- to algorithmic fairness. Third, I take some steps in developing the paradigm of 'responsible algorithmic fairness' to correct for errors in the current scope and implementation of algorithmic fairness. I close by some reflections of directions for future research. (shrink)

The pupils of the eyes reflexively constrict in light and dilate in dark to optimize retinal illumination. Non-visual cognitive factors, like attention, arousal, decision-making, etc., also influence pupillary light response. During passive viewing, the eccentricity of a stimulus modulates the pupillary aperture size driven by spatially weighted corneal flux density, which is the product of luminance and the area of the stimulus. Whether the scope of attention also influences PLR remains unclear. In this study, we contrasted the (...) pupil dynamics between diffused and focused attentional conditions during decision-making, while the global CFD remained the same in the two conditions. A population of 20 healthy humans participated in a pair of forced choice tasks. They distributed attention to the peripheral decision cue in one task, and concentrated at the center in the other to select the target from four alternatives for gaze orientation. The location of this cue did not influence participants’ reaction time. However, the magnitude of constriction was significantly less in the task that warranted attention to be deployed at the center than on the periphery. We observed similar pupil dynamics when participants either elicited or canceled a saccadic eye movement, which ruled out pre-saccadic obligatory attentional orientation contributing to PLR. We further addressed how the location of attentional deployment might have influenced PLR. We simulated a biomechanical model of PLR with visual stimulation of different strengths as inputs corresponding to the two attentional conditions. In this homeomorphic model, the computational characteristic of each element was derived from the physiological and/or mechanical properties of the corresponding biological element. The simulation of this model successfully mimicked the observed data. In contrast to common belief that the global ambient luminosity drives pupillary response, the results of our study suggest that the effective CFD determined via the luminance multiplied by the size of the stimulus at the location of deployed attention in the visual space is critical for the magnitude of pupillary constriction. (shrink)