In the past three years, Heather Ford—an ethnographer and now a PhD student—has worked on ad hoc collaborative projects around Wikipedia sources with two data scientists from Minnesota, Dave Musicant and Shilad Sen. In this essay, she talks about how the three met, how they worked together, and what they gained from the experience. Three themes became apparent through their collaboration: that data scientists and ethnographers have much in common, that their skills are complementary, and that discovering the (...) data together rather than compartmentalizing research activities was key to their success. (shrink)

The use of Big Data—however the term is defined—involves a wide array of issues and stakeholders, thereby increasing numbers of complex decisions around issues including data acquisition, use, and sharing. Big Data is becoming a significant component of practice in an ever-increasing range of disciplines; however, since it is not a coherent “discipline” itself, specific codes of conduct for Big Data users and researchers do not exist. While many institutions have created, or will create, training opportunities (...) to prepare people to work in and around Big Data, insufficient time, space, and thought have been dedicated to training these people to engage with the ethical, legal, and social issues in this new domain. Since Big Data practitioners come from, and work in, diverse contexts, neither a relevant professional code of conduct nor specific formal ethics training are likely to be readily available. This normative paper describes an approach to conceptualizing ethical reasoning and integrating it into training for Big Data use and research. Our approach is based on a published framework that emphasizes ethical reasoning rather than topical knowledge. We describe the formation of professional community norms from two key disciplines that contribute to the emergent field of Big Data: computer science and statistics. Historical analogies from these professions suggest strategies for introducing trainees and orienting practitioners both to ethical reasoning and to a code of professional conduct itself. We include two semester course syllabi to strengthen our thesis that codes of conduct can be harnessed to support the development of ethical reasoning in, and a sense of professional identity among, Big Data practitioners. (shrink)

The introduction of Artificial Intelligence systems has demonstrated impeccable potential and benefits to enhance the decision-making processes in our society. However, despite the successful performance of AI systems to date, skepticism and concern remain regarding whether AI systems could form a trusting relationship with human users. Developing trusted AI systems requires careful consideration and evaluation of its reproducibility, interpretability, and fairness, which in in turn, poses increased expectations and responsibilities for data scientists. Therefore, the current study focused on understanding (...) Canadian data scientists’ self-confidence in creating trusted AI systems, while relying on their current AI system development practices. (shrink)

This article investigates epistemological aspects of scientists’ reuse of empirical data over decades and centuries. Giving examples, I discuss three respects in which empirical data are historical entities and the implications for the notion of data reuse. First, any data reuse necessitates metadata, which specify the data’s circumstances of origin. Second, interpretation of historical data often requires the tools of humanities disciplines, which produce a further historicization of data. Finally, some qualitative social scientists (...) hold that data are personal to the researcher who coconstructs them in the research process and are therefore skeptical about the prospects of reusing data. (shrink)

This paper presents an analysis of the forms of response that scientists make when confronted with anomalous data. We postulate that there are seven ways in which an individual who currently holds a theory can respond to anomalous data: (1) ignore the data; (2) reject the data; (3) exclude the data from the domain of the current theory; (4) hold the data in abeyance; (5) reinterpret the data; (6) make peripheral changes to the (...) current theory; or (7) change the theory. We analyze psychological experiments and cases from the history of science to support this proposal. Implications for the philosophy of science are discussed. (shrink)

In recent years, many qualitative sociologists, anthropologists, and social theorists have critiqued the use of algorithms and other automated processes involved in data science on both epistemological and political grounds. Yet, it has proven difficult to bring these important insights into the practice of data science itself. We suggest that part of this problem has to do with under-examined or unacknowledged assumptions about the relationship between the two fields—ideas about how data science and its critics can and (...) should relate. Inspired by recent work in Science and Technology Studies on interventions, we attempted to stage an encounter in which practicing data scientists were asked to analyze a corpus of critical social science literature about their work, using tools of textual analysis such as co-word and topic modelling. The idea was to provoke discussion both about the content of these texts and the possible limits of such analyses. In this commentary, we reflect on the planning stages of the experiment and how responses to the exercise, from both data scientists and qualitative social scientists, revealed some of the tensions and interactions between the normative positions of the different fields. We argue for further studies which can help us understand what these interdisciplinary tensions turn on—which do not paper over them but also do not take them as given. (shrink)

Surveys of almost 1,500 members of three professional societies that do risk analysis found that 3 in 10 respondents had observed a biased research design, 2 in 10 had observed plagiarism, and 1 in 10 observed data fabrication or falsification. Respondents with many years in risk analysis, business consultants, and industrial hygienists reported the greatest prevalence of misconduct. These respondents perceived poor science, economic implications of the research, and lack of training in ethics as causes of misconduct. They supported (...) the teaching of ethics but the vast majority did not support government auditing of their work. (shrink)

ABSTRACTThere has considerable interest in bringing low/middle-income countries scientists into discussions on Open Data – both as contributors and users. The establishment of in situ data sharing practices within LMIC research institutions is vital for the development of an Open Data landscape in the Global South. Nonetheless, many LMICs have significant challenges – resource provision, research support and extra-laboratory infrastructures. These low-resourced environments shape data sharing activities, but are rarely examined within Open Data discourse. In (...) particular, little attention is given to how these research environments shape scientists’ perceptions of data sharing incentives. This paper expands on these issues of incentivizing data sharing, using data from a quantitative survey disseminated to life scientists in 13 countries in sub-Saharan Africa. This interrogated not only perceptions of data sharing amongst LMIC scientists, but also how these are connected to the research environm... (shrink)

BackgroundHealth data-driven activities have become central in diverse fields (research, AI development, wearables, etc.), and new ethical challenges have arisen with regards to privacy, integrity, and appropriateness of use. To ensure the protection of individuals’ fundamental rights and freedoms in a changing environment, including their right to the protection of personal data, we aim to identify the ethical approaches adopted by scientists during intensive data exploitation when collecting, using, or sharing peoples’ health data.MethodsTwelve scientists who were (...) collecting, using, or sharing health data in different contexts in Sweden, were interviewed. We used systematic expert interviews to access these scientists’ specialist knowledge, and analysed the interviews with thematic analysis. Phrases, sentences, or paragraphs through which ethical values and norms were expressed, were identified and coded. Codes that reflected similar concepts were grouped, subcategories were formulated, and categories were connected to traditional ethical approaches.ResultsThrough several examples, the respondents expressed four different ethical approaches, which formed the main conceptual categories: consideration of consequences, respect for rights, procedural compliance, and being professional.ConclusionsTo a large extent, the scientists’ ethical approaches were consistent with ethical and legal principles. Data sharing was considered important and worth pursuing, even though it is difficult. An awareness of the complex issues involved in data sharing was reflected from different perspectives, and the respondents commonly perceived a general lack of practical procedures that would by default ensure ethical and legally compliant data collection and sharing. We suggest that it is an opportune time to move on from policy discussions to practical technological ethics-by-design solutions that integrate these principles into practice. (shrink)

We have seen through many examples that data science and artificial intelligence can reinforce structural inequalities like sexism and racism. Data is power, and that power is distributed unequally. This book offers a vision for a feminist data science that can challenge power and work towards justice. This book takes a stand against a world that benefits some (including the authors, two white women) at the expense of others. It seeks to provide concrete steps for data (...) scientists seeking to learn how feminism can help them work towards justice, and for feminists seeking to learn how their own work can carry over to the growing field of data science. It is addressed to professionals in all fields where data-driven decisions are being made, as well as to communities that want to better understand the data that surrounds them. It is written for everyone who seeks to better understand the charts and statistics that they encounter in their day-to-day lives, and for everyone who seeks to better communicate the significance of such charts and statistics to others. This is an example-driven book written with a broad audience of scholars, students, and practitioners in mind. It offers a way of thinking about data, both their uses and their limits, that is informed by direct experience, by a commitment to action, and by the ideas associated with intersectional feminist thought. (shrink)

Trust is a critical component of research: trust in the work of co-workers and colleagues within the scientific community; trust in the work of research scientists by the non-research community. A wide range of factors, including internally and externally generated pressures and practical and personal limitations, affect the research process. The extent to which these factors are understood and appreciated influence the development of trust in scientific research findings.

What happens when the scientific tradition of openness clashes with potential societal risks? The work of American toxic-exposure epidemiologists can attract media coverage and lead the public to change health practices, initiate lawsuits, or take other steps a study’s authors might consider unwarranted. This paper, reporting data from 61 semi-structured interviews with U.S. toxic-exposure epidemiologists, examines whether such possibilities shaped epidemiologists’ selection of journals for potentially sensitive papers. Respondents manifested strong support for the norm of scientific openness, but a (...) significant minority had or would/might, given the right circumstances, publish sensitive data in less visible journals, so as to prevent unwanted media or public attention. Often, even those advocating such limited “burial” upheld openness, claiming that less visible publication allowed them to avoid totally withholding the data from publication. However, 15% of the sample had or would, for the most sensitive types of data, withhold publication altogether. Rather than respondents explaining their actions in terms of an expected split between “pure science” and “social advocacy” models, even those publishing in the more visible journals often described their actions in terms of their “responsibility”. Several practical limitations (particularly involving broader access to scientific literature via the Internet) of the strategy of burial are discussed, and some recommendations are offered for scientists, the media, and the public. (shrink)

In this article, we consider the possible application of the European General Data Protection Regulation to “citizen scientist”-led health research with mobile devices. We argue that the GDPR likely does cover this activity, depending on the specific context and the territorial scope. Remaining open questions that result from our analysis lead us to call for lex specialis that would provide greater clarity and certainty regarding the processing of health data by for research purposes, including these non-traditional researchers.

This book aims to act as an ethics textbook for what it terms ‘bioengineering students’: scientists working with medical technologies either in research or clinical practice. It is aimed at an Amer...

The advent of extremely large data sets, known as “big data,” has been heralded as the instantiation of a new science, requiring a new kind of practitioner: the “data scientist.” This article explores the concept of big data, drawing attention to a number of new issues—not least ethical concerns, and questions surrounding interpretation—which big data sets present. It is observed that the skills required for data scientists are in some respects closer to those (...) traditionally associated with the arts and humanities than to those associated with the natural sciences; and it is urged that big data presents new opportunities for dialogue, especially concerning hermeneutical issues, for theologians and data scientists. (shrink)

In the life sciences, there is an ongoing discussion about a perceived ‘reproducibility crisis’. However, it remains unclear to which extent the perceived lack of reproducibility is the consequence of issues that can be tackled and to which extent it may be the consequence of unrealistic expectations of the technical level of reproducibility. Large-scale, multi-institutional experimental replication studies are very cost- and time-intensive. This Perspective suggests an alternative, complementary approach: meta-research using sociological and philosophical methodologies to examine researcher trust in (...) data. An improved understanding of the criteria used by researchers to judge data reliability will provide crucial, initial evidence on the actual scale of the reproducibility crisis and on measures to tackle it. (shrink)

We conducted a systematic review and meta-analysis of anonymous surveys asking scientists whether they ever committed various forms of plagiarism. From May to December 2011 we searched 35 bibliographic databases, five grey literature databases and hand searched nine journals for potentially relevant studies. We included surveys that asked scientists if, in a given recall period, they had committed or knew of a colleague who committed plagiarism, and from each survey extracted the proportion of those who reported at least one case. (...) Studies that focused on academic plagiarism were excluded. Literature searches returned 12,460 titles from which 17 relevant survey studies were identified. Meta-analysis of studies reporting committed and witnessed plagiarism yielded a pooled estimate of, respectively, 1.7 % and 30 %. Basic methodological factors, including sample size, year of survey, delivery method and whether survey questions were explicit rather than indirect made a significant difference on survey results. Even after controlling for these methodological factors, between-study differences in admission rates were significantly above those expected by sampling error alone and remained largely unexplained. Despite several limitations of the data and of this meta-analysis, we draw three robust conclusions: The rate at which scientists report knowing a colleague who committed plagiarism is higher than for data fabrication and falsification; The rate at which scientists report knowing a colleague who committed plagiarism is correlated to that of fabrication and falsification; The rate at which scientists admit having committed either form of misconduct in surveys has declined over time. (shrink)

Scientists’ sense of social responsibility is particularly relevant for emerging technologies. Since a regulatory vacuum can sometimes occur in the early stages of these technologies, individual scientists’ social responsibility might be one of the most significant checks on the risks and negative consequences of this scientific research. In this article, we analyze data from a 2011 mail survey of leading U.S. nanoscientists to explore their perceptions the regarding social and ethical responsibilities for their nanotechnology research. Our analyses show that (...) leading U.S. nanoscientists express a moderate level of social responsibility about their research. Yet, they have a strong sense of ethical obligation to protect laboratory workers from unhealthy exposure to nanomaterials. We also find that there are significant differences in scientists’ sense of social and ethical responsibility depending on their demographic characteristics, job affiliation, attention to media content, risk perceptions and benefit perceptions. We conclude with some implications for future research. (shrink)

There is no doubt science is currently suffering from a credibility crisis. This thought-provoking book argues that, ironically, science's credibility is being undermined by tools created by scientists themselves. Scientific disinformation and damaging conspiracy theories are rife because of the internet that science created, the scientific demand for empirical evidence and statistical significance leads to data torturing and confirmation bias, and data mining is fuelled by the technological advances in Big Data and the development of ever-increasingly powerfulcomputers. (...) Using a wide range of entertaining examples, this fascinating book examines the impacts of society's growing distrust of science, and ultimately provides constructive suggestions for restoring the credibility of the scientific community. (shrink)

This book explores the work of activists in the Americas who are documenting feminicide, arguing that feminist activists at the margins have much to teach mainstream data scientists about data ethics: how to work with data ethically amidst extreme and durable structural inequalities.

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

Scientists have rules pertaining to data fabrication and falsification that are enforced with significant punishments, such as loss of funding, termination of employment, or imprisonment. These rules pertain to data that describe observable and unobservable entities. In this commentary I argue that scientists would not adopt rules that impose harsh penalties on researchers for data fabrication or falsification unless they believed that an aim of scientific research is to develop true theories and hypotheses about entities that exist, (...) including unobservable ones. This argument presents a challenge for constructive empiricists, such as van Fraassen. Constructive empiricists need to be able to explain why rules pertaining to data fabrication and falsification do not threaten their philosophy of science. (shrink)

Social scientists and data analysts are increasingly making use of Big Data in their analyses. These data sets are often “found data” arising from purely observational sources rather than data derived under strict rules of a statistically designed experiment. However, since these large data sets easily meet the sample size requirements of most statistical procedures, they give analysts a false sense of security as they proceed to focus on employing traditional statistical methods. We explain (...) how most analyses performed on Big Data today lead to “precisely inaccurate” results that hide biases in the data but are easily overlooked due to the enhanced significance of the results created by the data size. Before any analyses are performed on large data sets, we recommend employing a simple data segmentation technique to control for some major components of observational data biases. These segments will help to improve the accuracy of the results. (shrink)

This article examines the relevance of survey data of scientists’ attitudes about science and values to case studies in philosophy of science. We describe two methodological challenges confronting such case studies: 1) small samples, and 2) potential for bias in selection, emphasis, and interpretation. Examples are given to illustrate that these challenges can arise for case studies in the science and values literature. We propose that these challenges can be mitigated through an approach in which case studies and survey (...) methods are viewed as complementary, and use data from the Toolbox Dialogue Initiative to illustrate this claim. (shrink)

In the last few years, biologists and computer scientists have claimed that the introduction of data science techniques in molecular biology has changed the characteristics and the aims of typical outputs (i.e. models) of such a discipline. In this paper we will critically examine this claim. First, we identify the received view on models and their aims in molecular biology. Models in molecular biology are mechanistic and explanatory. Next, we identify the scope and aims of data science (machine (...) learning in particular). These lie mainly in the creation of predictive models which performances increase as data set increases. Next, we will identify a tradeoff between predictive and explanatory performances by comparing the features of mechanistic and predictive models. Finally, we show how this a priori analysis of machine learning and mechanistic research applies to actual biological practice. This will be done by analyzing the publications of a consortium—The Cancer Genome Atlas—which stands at the forefront in integrating data science and molecular biology. The result will be that biologists have to deal with the tradeoff between explaining and predicting that we have identified, and hence the explanatory force of the ‘new’ biology is substantially diminished if compared to the ‘old’ biology. However, this aspect also emphasizes the existence of other research goals which make predictive force independent from explanation. (shrink)

In the last few years, biologists and computer scientists have claimed that the introduction of data science techniques in molecular biology has changed the characteristics and the aims of typical outputs (i.e. models) of such a discipline. In this paper we will critically examine this claim. First, we identify the received view on models and their aims in molecular biology. Models in molecular biology are mechanistic and explanatory. Next, we identify the scope and aims of data science (machine (...) learning in particular). These lie mainly in the creation of predictive models which performances increase as data set increases. Next, we will identify a tradeoff between predictive and explanatory performances by comparing the features of mechanistic and predictive models. Finally, we show how this a priori analysis of machine learning and mechanistic research applies to actual biological practice. This will be done by analyzing the publications of a consortium—The Cancer Genome Atlas—which stands at the forefront in integrating data science and molecular biology. The result will be that biologists have to deal with the tradeoff between explaining and predicting that we have identified, and hence the explanatory force of the ‘new’ biology is substantially diminished if compared to the ‘old’ biology. However, this aspect also emphasizes the existence of other research goals which make predictive force independent from explanation. (shrink)

This unique volume is an introduction for computer scientists, including a formal study of theoretical algorithms for Big Data applications, which allows them to work on such algorithms in the future. It also serves as a useful reference guide for the general computer science population, providing a comprehensive overview of the fascinating world of such algorithms. To achieve these goals, the algorithmic results presented have been carefully chosen so that they demonstrate the important techniques and tools used in Big (...) Data algorithms, and yet do not require tedious calculations or a very deep mathematical background. (shrink)

In 1880, Galton carried out an investigation of imagery in a sample of distinguished men and a sample of nonscientists (adolescent male students). He concluded that scientists were either totally lacking in visual imagery or had “feeble” powers of mental imagery. This finding has been widely accepted in the secondary literature in psychology. A replication of Galton’s study with modern scientists and modern university undergraduates found no scientists totally lacking in visual imagery and very few with feeble visual imagery. Examination (...) of Galton’s published data shows that his own published data do not support his claims about deficient visual imagery in scientists. The modern data for scientists and nonscientists and the 1880 data for scientists and nonscientists are in agreement in showing that all groups report substantial imagery on recollective memory tasks such as Galton’s breakfast questionnaire. We conclude that Galton’s conclusions were an example of theory-laden interpretation of data based on the initial responses from several very salient scientists who reported little or no visual imagery on Galton’s imagery questionnaire. (shrink)

It has been argued that ethical frameworks for data science often fail to foster ethical behavior, and they can be difficult to implement due to their vague and ambiguous nature. In order to overcome these limitations of current ethical frameworks, we propose to integrate the analysis of the connections between technical choices and sociocultural factors into the data science process, and show how these connections have consequences for what data subjects can do, accomplish, and be. Using healthcare (...) as an example, attention to sociocultural conversion factors relevant to health can help in navigating technical choices that require broader considerations of the sociotechnical system, such as metric tradeoffs in model validation, resulting in better ethical and technical choices. This approach promotes awareness of the ethical dimension of technical choices by data scientists and others, and that can foster the cultivation of 'ethical skills' as integral to data science. (shrink)

In this study, I examine the institutional model of data science as a nascent profession undergoing an occupational founding phase. Drawing on interviews with sixty data scientists, senior managers, and professors from Israel as well as observations at the local professional community’s events, I argue that data scientists endorse an open institutional model, upholding largely internet-based institutions focusing on knowledge sharing, networking, and collaboration. This model grants data scientists expertise, autonomy, and authority vis-à-vis clients, employers, and (...) states; provides them with continued credentialing independent of employing organizations; encourages the wide entry of new members; and helps them deal with the accelerated temporality of their field. This open model enables an omnivorous spreading of data science expertise and is used to challenge professionalization as an occupational-institutional model in other professions. Still, this model faces many challenges. (shrink)

The concept of dual-use encapsulates the potential for well-intentioned, beneficial scientific research to also be misused by a third party for malicious ends. The concept of dual-use challenges scientists to look beyond the immediate outcomes of their research and to develop an awareness of possible future (mis)uses of scientific research. Since 2001 much attention has been paid to the possible need to regulate the dual-use potential of the life sciences. Regulation initiatives fall under two broad categories—those that develop the ethical (...) education of scientists and foster an awareness and responsibility of dual-use issues, and those which assess the regulation of information being generated by current research. Both types of initiatives are premised on a cautious, risk-adverse philosophy which advocates careful examination of all future endpoints of research endeavors. This caution advocated within initiatives such as pre-publication review of journal articles contrasts to the obligation to share underpinning data sharing discussions. As the dual-use debate has yet to make a significant impact on data sharing discussions (and vice versa) it is possible that these two areas of knowledge control may present areas of ethical conflict for scientists, and thus need to be more closely examined. This paper examines the tension between the obligation to share exemplified by data sharing principles and the concerns raised by the risk-cautious culture of the dual-use debates. The paper concludes by reflecting on the issues of responsibility as raised by dual-use as relating to data sharing, such as the chain of custody for shared data. (shrink)

Despite a widespread impression that the public is woefully ignorant of science and cares little for the subject, U.S. National Science Foundation (NSF) surveys show the majority are very interested and understand that they are not well informed about science. The data are consistent with the author’s view that the popularity of pseudoscience does not indicate a rejection of science. If this is so, opportunities for scientists to communicate with the public promise a more rewarding result than is commonly (...) believed among scientists. In fact, the increasing visibility of science in the mass media correlates with a slow, steady improvement in public understanding of science in recent years. (shrink)

In June 2005, Italy held a referendum on repealing the law on medically assisted fertilization , which limits access to artificial reproduction to infertile couples, and prohibits the donation of gametes, the cryopreservation of embryos, preimplantation genetic diagnosis , and research on human embryos. The referendum was invalidated, and the law remained unchanged. The Italian political e bioethical debate on assisted reproduction was manipulated by the Catholic Church, which distorted scientific data and issues at stake with the help of (...) Catholic politicians and bioethicists. What happened in Italy shows that some perverse socio-cultural e political mechanisms are spreading the absurd and anti-historical view that scientific and technological advancements are threatening democracy and personal freedom. Scientists should not only contrast the political attempts at limiting freedom of scientific research, but also tell politicians, humanists and citizens that the invention of Western science with its view of scientific community as an “open society”, contributed and still contributes, through scientific education, to the construction and maintaining of the moral and political values underlying Western democracies. (shrink)

The speed of development in Big Data and associated phenomena, such as social media, has surpassed the capacity of the average consumer to understand his or her actions and their knock-on effects. We are moving towards changes in how ethics has to be perceived: away from individual decisions with specific and knowable outcomes, towards actions by many unaware that they may have taken actions with unintended consequences for anyone. Responses will require a rethinking of ethical choices, the lack thereof (...) and how this will guide scientists, governments, and corporate agencies in handling Big Data. This essay elaborates on the ways Big Data impacts on ethical conceptions. (shrink)

Introspective reports are used as sources of information about other minds, in both everyday life and science. Many scientists and philosophers consider this practice unjustified, while others have made the untestable assumption that introspection is a truthful method of private observation. I argue that neither skepticism nor faith concerning introspective reports are warranted. As an alternative, I consider our everyday, commonsensical reliance on each other’s introspective reports. When we hear people talk about their minds, we neither refuse to learn from (...) nor blindly accept what they say. Sometimes we accept what we are told, other times we reject it, and still other times we take the report, revise it in light of what we believe, then accept the modified version. Whatever we do, we have (implicit) reasons for it. In developing a sound methodology for the scientific use of introspective reports, we can take our commonsense treatment of introspective reports and make it more explicit and rigorous. We can discover what to infer from introspective reports in a way similar to how we do it every day, but with extra knowledge, methodological care, and precision. Sorting out the use of introspective reports as sources of data is going to be a painstaking, piecemeal task, but it promises to enhance our science of the mind and brain. (shrink)

Due to advancements in technology such as data science and artificial intelligence, healthcare research has gained momentum and is generating new findings and predictions on abnormalities leading to the diagnosis of diseases or disorders in human beings. On one hand, the extensive application of data science to healthcare research is progressing faster, while on the other hand, the ethical concerns and adjoining risks and legal hurdles those data scientists may face in the future slow down the progression (...) of healthcare research. Simply put, the application of data science to ethically guided healthcare research appears to be a dream come true. Hence, in this paper, we discuss the current practices, challenges, and limitations of the data collection process during medical image analysis (MIA) conducted as part of healthcare research and propose an ethical data collection framework to guide data scientists to address the possible ethical concerns before commencing data analytics over a medical dataset. (shrink)

Intellectual property can be an important asset for academic institutions. Good data management practices are important for capture, development and protection of intellectual property assets. Selected issues focused on the relationship between data management and intellectual property are reviewed and a thesis that academic institutions and scientists should honor their obligations to responsibly manage data.

“Big Data” can revolutionize historical social science if it arises from substantively important contexts and is oriented towards answering substantively important questions. Such data may be especially important for answering previously largely intractable questions about the timing and sequencing of events, and of event boundaries. That said, “Big Data” makes no difference for social scientists and historians whose accounts rest on narrative sentences. Since such accounts are the norm, the effects of Big Data on the practice (...) of historical social science may be more limited than one might wish. (shrink)

The American Association for the Advancement of Science (AAAS) Dialogue on Science, Ethics, and Religion (DoSER) program has embarked on an exciting project, “Scientists and Religious Communities: Investigating Perceptions to Build Understanding.” The project will provide the first quantitative data on the underlying assumptions and concerns that shape national attitudes on science. A nationally representative survey conducted in collaboration with sociologists at Rice University has reached 10,000 people, including evangelical Christians, mainline Protestants, Catholics, and Jews. The survey probed how (...) a broad range of religious people, particularly evangelical Christians, understands and thinks about science, and what they perceive about scientists. Scientists, broadly defined, were likewise surveyed to gauge their perceptions of how religious people regard science. The goal for AAAS is to increase understanding between the scientific and evangelical Christian communities and redefine this critical relationship. DoSER will bring together leaders from scientific and evangelical communities to discuss the implications of survey results and to use them for building better understanding and communication strategies. Building relationships between scientists and religious communities has the potential to create a new paradigm of understanding. Finding out what each group actually thinks, through a survey, is only the beginning. (shrink)

First-person data have been both condemned and hailed because of their alleged privacy. Critics argue that science must be based on public evidence: since first-person data are private, they should be banned from science. Apologists reply that first-person data are necessary for understanding the mind: since first-person data are private, scientists must be allowed to use private evidence. I argue that both views rest on a false premise. In psychology and neuroscience, the subjects issuing first-person reports (...) and other sources of first-person data play the epistemic role of a (self-) measuring instrument. Data from measuring instruments are public and can be validated by public methods. Therefore, first-person data are as public as other scientific data: their use in science is legitimate, in accordance with standard scientific methodology. (shrink)

This book addresses controversies concerning the epistemological foundations of data science: Is it a genuine science? Or is data science merely some inferior practice that can at best contribute to the scientific enterprise, but cannot stand on its own? The author proposes a coherent conceptual framework with which these questions can be rigorously addressed. Readers will discover a defense of inductivism and consideration of the arguments against it: an epistemology of data science more or less by definition (...) has to be inductivist, given that data science starts with the data. As an alternative to enumerative approaches, the author endorses Federica Russo’s recent call for a variational rationale in inductive methodology. Chapters then address some of the key concepts of an inductivist methodology including causation, probability and analogy, before outlining an inductivist framework. The inductivist framework is shown to be adequate and useful for an analysis of the epistemological foundations of data science. The author points out that many aspects of the variational rationale are present in algorithms commonly used in data science. Introductions to algorithms and brief case studies of successful data science such as machine translation are included. Data science is located with reference to several crucial distinctions regarding different kinds of scientific practices, including between exploratory and theory-driven experimentation, and between phenomenological and theoretical science. Computer scientists, philosophers and data scientists of various disciplines will find this philosophical perspective and conceptual framework of great interest, especially as a starting point for further in-depth analysis of algorithms used in data science. (shrink)

Ethics requires good science. Many scientists, government leaders, and industry representatives support tripling of global-nuclear-energy capacity on the grounds that nuclear fission is “carbon free” and “releases no greenhouse gases.” However, such claims are scientifically questionable (and thus likely to lead to ethically questionable energy choices) for at least 3 reasons. (i) They rely on trimming the data on nuclear greenhouse-gas emissions (GHGE), perhaps in part because flawed Kyoto Protocol conventions require no full nuclear-fuel-cycle assessment of carbon content. (ii) (...) They underestimate nuclear-fuel-cycle releases by erroneously assuming that mostly high-grade uranium ore, with much lower emissions, is used. (iii) They inconsistently compare nuclear-related GHGE only to those from fossil fuels, rather than to those from the best GHG-avoiding energy technologies. Once scientists take account of (i)–(iii), it is possible to show that although the nuclear fuel cycle releases (per kWh) much fewer GHG than coal and oil, nevertheless it releases far more GHG than wind and solar-photovoltaic. Although there may be other, ethical, reasons to support nuclear tripling, reducing or avoiding GHG does not appear to be one of them. (shrink)

Ethicists differ considerably in their reasons for using empirical data. This paper presents a brief overview of four traditional approaches to the use of empirical data: “the prescriptive applied ethicists,” “the theorists,” “the critical applied ethicists,” and “the particularists.” The main aim of this paper is to introduce a fifth approach of more recent date (i.e. “integrated empirical ethics”) and to offer some methodological directives for research in integrated empirical ethics. All five approaches are presented in a table (...) for heuristic purposes. The table consists of eight columns: “view on distinction descriptive-prescriptive sciences,” “location of moral authority,” “central goal(s),” “types of normativity,” “use of empirical data,” “method,” “interaction empirical data and moral theory,” and “cooperation with descriptive sciences.” Ethicists can use the table in order to identify their own approach. Reflection on these issues prior to starting research in empirical ethics should lead to harmonization of the different scientific disciplines and effective planning of the final research design. Integrated empirical ethics (IEE) refers to studies in which ethicists and descriptive scientists cooperate together continuously and intensively. Both disciplines try to integrate moral theory and empirical data in order to reach a normative conclusion with respect to a specific social practice. IEE is not wholly prescriptive or wholly descriptive since IEE assumes an interdepence between facts and values and between the empirical and the normative. The paper ends with three suggestions for consideration on some of the future challenges of integrated empirical ethics. (shrink)