Animal welfare scientific literature has accumulated rapidly in recent years, but bias may exist which influences understanding of progress in the field. We conducted a survey of articles related to animal welfare or well being from an electronic database. From 8,541 articles on this topic, we randomly selected 115 articles for detailed review in four funding categories: government; charity and/or scientific association; industry; and educational organization. Ninety articles were evaluated after unsuitable articles were rejected. The welfare states of animals (...) in new treatments, conventional treatments or control groups with no treatment were classified as high, medium or low according to one or more. More articles were published in which the welfare of animals in new treatments was better than that of animals in the conventional or no treatment groups, demonstrating a positive result bias. Failure to publish studies with negative or inconclusive results may lead to other scientists unnecessarily repeating the research. The authors’ assessments of the welfare state of the groups were similarly rated high, medium or low, and it was found that new treatments were rated lower if the research was funded by industry, and higher when funded by charities, with government funding agencies intermediate. These differences were not evident in the Five Freedoms assessment, demonstrating an authors’ assessment bias that appeared to support the funding agencies’ interests. North American funded publications rated the welfare of animals in New treatments higher and those in a Conventional or No Treatment lower, compared with European-funded publications. It is concluded that preliminary evidence was provided of several forms of publication bias in animal welfare science. (shrink)

ABSTRACT: The term ‘publication bias’ usually refers to the tendency for a greater proportion of statistically significant positive results of experiments to be published and, conversely, a greater proportion of statistically significant negative or null results not to be published. It is widely accepted in the fields of healthcare and psychological research to be a major threat to the validity of systematic reviews and meta-analyses. Some methodological work has previously been undertaken, by the author and others, in the (...) field of educational research to investigate the extent of the problem. This paper describes the problem of publication bias with reference to its history in a number of fields, with special reference to the area of educational research. Informal methods for detecting publication bias in systematic reviews and meta-analyses of controlled trials are outlined and retrospective and prospective methods for dealing with the problem are suggested. (shrink)

Animal welfare scientific literature has accumulated rapidly in recent years, but bias may exist which influences understanding of progress in the field. We conducted a survey of articles related to animal welfare or well being from an electronic database. From 8,541 articles on this topic, we randomly selected 115 articles for detailed review in four funding categories: government; charity and/or scientific association; industry; and educational organization. Ninety articles were evaluated after unsuitable articles were rejected. The welfare states of animals (...) in new treatments, conventional treatments or control groups with no treatment were classified as high, medium or low according to one or more. More articles were published in which the welfare of animals in new treatments was better than that of animals in the conventional or no treatment groups, demonstrating a positive result bias. Failure to publish studies with negative or inconclusive results may lead to other scientists unnecessarily repeating the research. The authors’ assessments of the welfare state of the groups were similarly rated high, medium or low, and it was found that new treatments were rated lower if the research was funded by industry, and higher when funded by charities, with government funding agencies intermediate. These differences were not evident in the Five Freedoms assessment, demonstrating an authors’ assessment bias that appeared to support the funding agencies’ interests. North American funded publications rated the welfare of animals in New treatments higher and those in a Conventional or No Treatment lower, compared with European-funded publications. It is concluded that preliminary evidence was provided of several forms of publication bias in animal welfare science. (shrink)

Publication bias is defined as “the tendency on the parts of investigators, reviewers, and editors to submit or accept manuscripts for publication based on the direction or the strength of the study findings.”Publication bias distorts the accumulated data in the literature, causes the over estimation of potential benefits of intervention and mantles the risks and adverse effects, and creates a barrier to assessing the clinical utility of drugs as well as evaluating the long-term safety of (...) medical interventions. The World Medical Association, the International Committee of Medical Journals, and the Committee on Publication Ethics have conferred responsibilities and ethical obligations to editors concerning the avoidance of publication bias. Despite the explicit statements in these international documents, the editors’ role in and ability to avoid publication bias is still being discussed. Unquestionably, all parties involved in clinical research have the ultimate responsibility to sustain the research integrity and validity of accumulated general knowledge. Cooperation and commitment is required at every step of a clinical trial. However, this holistic approach does not exclude effective measures to be taken at the editors’ level. The editors of major medical journals concluded that one precaution that editors can take is to mandate registration of all clinical trials in a public repository as a precondition to submitting manuscripts to journals. Raising awareness regarding the value of publishing negative data for the scientific community and human health, and increasing the number of journals that are dedicated to publishing negative results or that set aside a section in their pages to do so, are positive steps editors can take to avoid publication bias. (shrink)

Journal of Social Philosophy, EarlyView.

BackgroundPublication and outcome reporting bias is often caused by researchers selectively choosing which scientific results and outcomes to publish. This behaviour is ethically significant as it distorts the literature used for future scientific or clinical decision-making. This study investigates the practicalities of using ethics applications submitted to a UK National Health Service research ethics committee to monitor both types of reporting bias.MethodsAs part of an internal audit we accessed research ethics database records for studies submitting an end of (...) study declaration to the Hampshire A research ethics committee between 1st January 2010 and 31st December 2011. A literature search was used to establish the publication status of studies. Primary and secondary outcomes stated in application forms were compared with outcomes reported in publications.ResultsOut of 116 studies the literature search identified 57 publications for 37 studies giving a publication rate of 32 %. Original Research Ethics Committee applications could be obtained for 28 of the published studies. Outcome inconsistencies were found in 16 of the published studies.ConclusionsThis study showed that the problem of publication and outcome reporting bias is still significant in the UK. The method described here demonstrates that UK NHS research ethics committees are in a good position to detect such bias due to their unique access to original research protocols. Data gathered in this way could be used by the Health Research Authority to encourage higher levels of transparency in UK research. (shrink)

Components of artificial intelligence (AI) for analysing social big data, such as natural language processing (NLP) algorithms, have improved the timeliness and robustness of health data. NLP techniques have been implemented to analyse large volumes of text from social media platforms to gain insights on disease symptoms, understand barriers to care and predict disease outbreaks. However, AI-based decisions may contain biases that could misrepresent populations, skew results or lead to errors. Bias, within the scope of this paper, is described (...) as the difference between the predictive values and true values within the modelling of an algorithm. Bias within algorithms may lead to inaccurate healthcare outcomes and exacerbate health disparities when results derived from these biased algorithms are applied to health interventions. Researchers who implement these algorithms must consider when and how bias may arise. This paper explores algorithmic biases as a result of data collection, labelling and modelling of NLP algorithms. Researchers have a role in ensuring that efforts towards combating bias are enforced, especially when drawing health conclusions derived from social media posts that are linguistically diverse. Through the implementation of open collaboration, auditing processes and the development of guidelines, researchers may be able to reduce bias and improve NLP algorithms that improve health surveillance. (shrink)

By focusing on patents for recent algorithms that incorporate publicly available data to yield automated racial and ethnic classification schemes, I provide a glimpse into how engineers and programmers understand and define racial and ethnic categories. Patents provide insights into how engineers and programmers encode assumptions about identity and behavior, due to disclosure provisions required by US patent law; similar requirements are present in patent laws throughout the world. Such disclosures provide insights that are otherwise unavailable for most proprietary assets. (...) I further discuss how such classification tools, especially when combined with advertisement-based platforms such as social media, can amplify systemic racism present in the underlying data sets. (shrink)

This study investigated how individuals perceive public opinion, examining several typical biases in estimating other people's opinions. Analyses of survey responses from university students provided evidence of ?looking glass perception?, a tendency to see others as holding opinions similar to one's own on public issues. Looking glass perception was found more significant in estimating the opinions of one's reference groups than in assessing the opinion of the anonymous general public. This finding suggests that looking glass perception may be attributed to (...) individuals? desire to be accepted by ?significant? other people. This study also found that a respondent's estimate of the opinion (% of conservative opinions) of a particular group on one issue was correlated with his/her estimate of the opinion of the same group on another issue. This finding suggests that individuals may have a preconception about the ideological position ("conservative? or ?liberal") of a particular social group and employ the preconception, to some extent, to estimate the opinions of the group on various public issues. (shrink)

We examine the interface of entrepreneurship and society by considering a novel source of gender bias (public opinion) and a novel expression of it (affective evaluations). We posit that women-led teams displaying success will trigger a “penalty for success” bias, and this will be inhibited if the team receives a “stamp of approval” from a gender congruent individual (i.e., an investor who is a man). Analysis from our first study, based on archival data, indicated that other mechanisms might (...) be at play that we could not tease apart in our archival data, so we conducted a follow-up experimental study to further examine our initial findings. Results jointly indicate that women are viewed unfavorably unless either (a) they are validated by success or endorsement from a man, or (b) they fail but are “warm and accommodating” about it. We also find that while men benefit from being validated by success or endorsement of an opposite-gender investor, they do not face a similarly steep penalty absent that validation. (shrink)

Across the world, artificial intelligence (AI) technologies are being more widely employed in public sector decision-making and processes as a supposedly neutral and an efficient method for optimizing delivery of services. However, the deployment of these technologies has also prompted investigation into the potentially unanticipated consequences of their introduction, to both positive and negative ends. This paper chooses to focus specifically on the relationship between gender bias and AI, exploring claims of the neutrality of such technologies and how its (...) understanding of bias could influence policy and outcomes. Building on a rich seam of literature from both technological and sociological fields, this article constructs an original framework through which to analyse both the perpetuation and mitigation of gender biases, choosing to categorize AI technologies based on whether their input is text or images. Through the close analysis and pairing of four case studies, the paper thus unites two often disparate approaches to the investigation of bias in technology, revealing the large and varied potential for AI to echo and even amplify existing human bias, while acknowledging the important role AI itself can play in reducing or reversing these effects. The conclusion calls for further collaboration between scholars from the worlds of technology, gender studies and public policy in fully exploring algorithmic accountability as well as in accurately and transparently exploring the potential consequences of the introduction of AI technologies. (shrink)

Under the traditional system of peer-reviewed publication, the degree of prestige conferred to authors by successful publication is tied to the degree of the intellectual rigor of its peer review process: ambitious scientists do well professionally by doing well epistemically. As a result, we should expect journal editors, in their dual role as epistemic evaluators and prestige-allocators, to have the power to motivate improved author behavior through the tightening of publication requirements. Contrary to this expectation, I will (...) argue that the publication bias literature in academic medicine demonstrates that editor interventions have had limited effectiveness in improving the health of the publication and trial registration record, suggesting that much stronger interventions are needed. (shrink)

The use of advanced AI and data-driven automation in the public sector poses several organisational, practical, and ethical challenges. One that is easy to underestimate is automation bias, which, in turn, has underappreciated legal consequences. Automation bias is an attitude in which the operator of an autonomous system will defer to its outputs to the point where the operator overlooks or ignores evidence that the system is failing. The legal problem arises when statutory office-holders (or their employees) either (...) fetter their discretion to in-house algorithms or improperly delegate their discretion to third-party software developers—something automation bias may facilitate. A synthesis of previous research suggests an easy way to mitigate the risks of automation bias and its potential legal ramifications is for those responsible for procurement decisions to adhere to a simple checklist that ensures that the pitfalls of automation are avoided as much as possible. (shrink)

To arrive at their final evaluation of a manuscript or grant proposal, reviewers must convert a submission’s strengths and weaknesses for heterogeneous peer review criteria into a single metric of quality or merit. I identify this process of commensuration as the locus for a new kind of peer review bias. Commensuration bias illuminates how the systematic prioritization of some peer review criteria over others permits and facilitates problematic patterns of publication and funding in science. Commensuration bias (...) also foregrounds a range of structural strategies for realigning peer review practices and institutions with the aims of science. (shrink)

Next SectionBackground: Publication bias and discrimination are increasingly recognised in medicine. A survey was conducted to determine if medical journals were more likely to publish research reports from members of their own than a rival journal’s editorial board. Methods: A retrospective review was conducted of all research reports published in 2006 in the four competing medical journals within five medical specialties. Only three journals were willing to divulge the authorship of reports that had been rejected. Results: Overall, 4460 (...) research reports were published in 2006 by the 20 journals from five subspecialties (mean 223 (SD = 164) reports per journal; median 176; interquartile range 108–238). On average, 17.2 (7.7%) reports were from a journal’s own editorial board (SD = 10.7; median 15; interquartile range 10–23; n = 20), and 6.3 (2.8%) reports were from a member of the editorial board of one of the three rival journals within the specialty (SD = 7.3; median 3.5; interquartile range 1–8; n = 60). There was a statistically significant excess of publications from the journal’s own editorial board in 14 of the 20 journals (p<0.05). Journals were almost three times more likely to publish reports from their own editorial board than from one of the three rivals within their subspecialty (p<0.0001; median difference 11; Mann–Whitney U test; power for 5% significance >99.99%). Conclusions: There was a significant excess of publications from medical journals’ own editorial boards, although it is not possible to determine whether this is due to bias in the peer review process or selective submission by editors. (shrink)

One of the important causes of bias in the medical literature is failure to publish data because it is “negative”. Usually, this is due to failure to write a manuscript and submit it for publication. Since publication is an essential part of research and patients have been recruited into a study in the belief that they are participating in medical research, there is an ethical commitment to publish the observations made on volunteer subjects. This can be enforced (...) by ethical committees if they refuse to approve studies of investigators and sponsors who have failed to publish earlier studies. (shrink)

To analyze which ethically relevant biases have been identified by academic literature in artificial intelligence algorithms developed either for patient risk prediction and triage, or for contact tracing to deal with the COVID-19 pandemic. Additionally, to specifically investigate whether the role of social determinants of health have been considered in these AI developments or not. We conducted a scoping review of the literature, which covered publications from March 2020 to April 2021. ​Studies mentioning biases on AI algorithms developed for contact (...) tracing and medical triage or risk prediction regarding COVID-19 were included. From 1054 identified articles, 20 studies were finally included. We propose a typology of biases identified in the literature based on bias, limitations and other ethical issues in both areas of analysis. Results on health disparities and SDOH were classified into five categories: racial disparities, biased data, socio-economic disparities, unequal accessibility and workforce, and information communication. SDOH needs to be considered in the clinical context, where they still seem underestimated. Epidemiological conditions depend on geographic location, so the use of local data in studies to develop international solutions may increase some biases. Gender bias was not specifically addressed in the articles included. The main biases are related to data collection and management. Ethical problems related to privacy, consent, and lack of regulation have been identified in contact tracing while some bias-related health inequalities have been highlighted. There is a need for further research focusing on SDOH and these specific AI apps. (shrink)

In response to reports on embryonic stem cell research in the AMWA Journal, the official publication of the American Medical Writers Association, the author submitted a brief article discussing ethical objections to such research. The Journal editor rejected the article, saying the Journal was not an appropriate place to discuss the ethics of stem cell research. The author challenged this reasoning because the Journal is committed to informing members about relevant ethical issues, ethics were mentioned in one of the (...) original reports, and related ethical concerns were discussed in a previous issue. Further efforts to secure balanced reporting failed. Concerned professionals should be aware of the AMWA bias. National Catholic Bioethics Quarterly 8.4 (Winter 2008): 639–654. (shrink)

In Britain, policy‐makers tend to view racism as a social attitude rather than an institutional/structural phenomenon. Not until the publication of the MacPherson Report (1999) was the idea of ‘institutional racism’ officially recognised. According to Jules Holroyd, implicit bias as a concept can help us understand and combat the kind of unwitting prejudice the Macpherson report describes. This article explores whether implicit bias is indeed a viable framework for understanding institutional/structural racism. To do so, I bring together (...) Charles Mills’ notion of ‘global white ignorance’ and Lewis Gordon's interpretation of ‘bad faith’. Through Mills’ and Gordon's analyses, which together illuminate both the structural and psychic dimensions of racism I offer an account of the psychodynamics of racism far more consistent with our observations of how racism actually operates in Britain. Specifically, we see that institutional/structural racism is neither unconscious nor is it unmotivated as implicit bias would suggest. As such, I reject implicit bias as a useful or necessary explanatory framework for helping us understand institutional racism as a structural phenomenon. (shrink)

It is by now no secret that some scientific articles are ghost authored – that is, written by someone other than the person whose name appears at the top of the article. Ghost authorship, however, is only one sort of ghosting. In this article, we present evidence that pharmaceutical companies engage in the ghost management of the scientific literature, by controlling or shaping several crucial steps in the research, writing, and publication of scientific articles. Ghost management allows the pharmaceutical (...) industry to shape the literature in ways that serve its interests. This article aims to reinforce and expand publication ethics as an important area of concern for bioethics. Since ghost-managed research is primarily undertaken in the interests of marketing, large quantities of medical research violate not just publication norms but also research ethics. Much of this research involves human subjects, and yet is performed not primarily to increase knowledge for broad human benefit, but to disseminate results in the service of profits. Those who sponsor, manage, conduct, and publish such research therefore behave unethically, since they put patients at risk without justification. This leads us to a strong conclusion: if medical journals want to ensure that the research they publish is ethically sound, they should not publish articles that are commercially sponsored. (shrink)

In following the form of a standard “bias” paper, the authors highlight a potentially serious bias of uncertain magnitude. A negative focus in research has certain adaptive features in terms of professional and public support, as well as theory generation. The bias deserves attention and correction, but in ways that do not exaggerate its liabilities or overlook its virtues.

Liberals often view religion chiefly as "a problem" for democratic discourse in modern pluralistic societies and propose an allegedly neutral solution in the form of philosophical distinctions between "the right" and "the good" or populist invocations of a "right to choose." Drawing on cultural theory and ethnographic research among activists in the Oregon debates over the legalization of physician-assisted suicide, I demonstrate that liberal "neutrality" harbors its own cultural bias, flattens the complexity of public debates, and undermines liberalism's own (...) commitments to equality. I conclude that the praiseworthy liberal goal of impartiality in policy decisions would best be met not by the inaccessible norm of neutrality but by a norm of inclusivity, which intentionally solicits multiple cultural perspectives. (shrink)

Summary The journal Systematic Zoology is studied in order to see what effect factionalism in the taxonomic community had on the refereeing process. Publication patterns in the journal were largely independent of changes in editorship. Although the taxonomic community was subdivided into feuding factions during the period under study, little bias along these lines was discernible in the refereeing process when studied statistically. Several explanations are suggested for the wide-spread impression at the time that bias was pervasive.

More than 40 primary studies, and three recent systematic reviews and meta-analyses, have shown a clear association between pharmaceutical industry funding of clinical trials and pro-industry results. Industry sponsorship biases published scientific research in favour of the sponsors, a result of the strong interest commercial sponsors have in obtaining favourable results.Three proposed remedies to this problem are widely agreed upon among those concerned with the level of sponsorship bias: financial disclosure, reporting standards and trial registries. This paper argues that (...) all of these remedies either fail to address the mechanisms by which pharmaceutical companies’ sponsorship leads to biased results—design bias, multiple trials with predictable outcomes, fraud, rhetorical effects and publication bias—or else only inadequately address those mechanisms. As a result, the policies normally proposed for dealing with sponsorship bias are unable to eliminate it. Only completely separating public clinical research from pharmaceutical industry funding can eliminate sponsorship bias. (shrink)

Neuroscience research, like all science, is vulnerable to the influence of extraneous values in the practice of research, whether in research design or the selection, analysis and interpretation of data. This is particularly problematic for research into the biological mechanisms that underlie behavior, and especially the neurobiological underpinnings of moral development and ethical reasoning, decision-making and behavior, and the other elements of what is often called the neuroscience of ethics. The problem arises because neuroscientists, like most everyone, bring to their (...) work assumptions, preconceptions and values and other sources of potentially inappropriate bias of which they may be unaware. It is important that the training of neuroscientists, and research practice itself, include open and in-depth discussion and examination of the assumptions that underlie research. Further, policy makers, journalists, and the general public, that is, the consumers of neuroscience research findings (and by extension, neurotechnologies) should be made aware of the limitations as well as the strengths of the science, the evolving nature of scientific understanding, and the often invisible values inherent in science. (shrink)

BackgroundDescriptive studies examining publication rates and citation counts demonstrate a geographic skew toward high-income countries (HIC), and research from low- or middle-income countries (LMICs) is generally underrepresented. This has been suggested to be due in part to reviewers’ and editors’ preference toward HIC sources; however, in the absence of controlled studies, it is impossible to assert whether there is bias or whether variations in the quality or relevance of the articles being reviewed explains the geographic divide. This study (...) synthesizes the evidence from randomized and controlled studies that explore geographic bias in the peer review process.MethodsA systematic review was conducted to identify research studies that explicitly explore the role of geographic bias in the assessment of the quality of research articles. Only randomized and controlled studies were included in the review. Five databases were searched to locate relevant articles. A narrative synthesis of included articles was performed to identify common findings.ResultsThe systematic literature search yielded 3501 titles from which 12 full texts were reviewed, and a further eight were identified through searching reference lists of the full texts. Of these articles, only three were randomized and controlled studies that examined variants of geographic bias. One study found that abstracts attributed to HIC sources elicited a higher review score regarding relevance of the research and likelihood to recommend the research to a colleague, than did abstracts attributed to LIC sources. Another study found that the predicted odds of acceptance for a submission to a computer science conference were statistically significantly higher for submissions from a “Top University.” Two of the studies showed the presence of geographic bias between articles from “high” or “low” prestige institutions.ConclusionsTwo of the three included studies identified that geographic bias in some form was impacting on peer review; however, further robust, experimental evidence is needed to adequately inform practice surrounding this topic. Reviewers and researchers should nonetheless be aware of whether author and institutional characteristics are interfering in their judgement of research. (shrink)

: In the last two decades, scientists, government officials, and science policy experts have expressed concerns about the increasing role of financial interests in research. Many believe that these interests are undermining research by causing bias and error, suppression of results, and even outright fraud. This paper seeks to shed some light on this view by (1) explicating the concept research bias, (2) describing some ways that financial interests can cause research biases, and (3) discussing some strategies for (...) mitigating or managing the influence of financial interests. These strategies include (a) using government funds to counter-balance privately funded research, (b) increasing public input into government funding decisions, (c) disclosing and managing conflicts of interest in research, (d) auditing data, (e) expanding access to data. Since it is neither possible nor desirable to eliminate financial interests from research, the wisest policy is to manage and counter-balance these interests for the good of science and society. (shrink)

The concept of equipoise is considered by many to be part of the ethical justification for using human subjects in clinical research. In general, equipoise indicates some uncertainty about the relative merits of the experimental intervention compared to existing treatments. Relieving this uncertainty gives scientific value to an experiment, thereby making the risks to human subjects in the trial acceptable, other considerations notwithstanding. But characterizing equipoise remains controversial since Freedman’s groundbreaking publication on the subject. We offer a new account (...) of equipoise that draws on and extends an option Freedman discarded. After establishing the importance of some account of equipoise as part of ethically justified clinical trials, we revisit Freedman’s distinction between clinical and theoretical equipoise. We raise concerns about Freedman’s preferred clinical equipoise and then rehabilitate theoretical equipoise. In particular, we use a variety of arguments from epistemology to show how Freedman was too hasty in rejecting theoretical equipoise. In addition, we argue that theoretical equipoise is a subspecies of epistemic equipoise, which we characterize as a form of uncertainty that is the result of the possibility of error. This type of uncertainty can best be alleviated by research, which produces beliefs supported by strong statistical evidence, which is a key aim of clinical trials. Further, this type of uncertainty can explain why even clinicians with more firsthand experience with an intervention than their peers, and who may not be in theoretical equipoise, could still justifiably support a trial designed to ameliorate the possibility of error due to cognitive bias. (shrink)

Statistics in public life .................................................................................................... .....5 Things and numbers............................................................................................. ...................8 Representative samples............................................................................................. ..........8 Averages: meaning and relevance .....................................................................................9 Correlations........................................................................................ ................................10 Applied statistics .................................................................................................... ................13 Relative risks .................................................................................................... ..................14 Relative risk versus absolute risk.....................................................................................16 Problems of classification and confounding factors....................................................17 Epidemiological research............................................................................................ ..........19 Publication bias.................................................................................................... ..............20 Statistical significance versus scientific relevance................................................................24 Relative risk again............................................................................................... ...............24 P-values............................................................................................ ...................................25 Confidence intervals .................................................................................................... .....26 Correlation is not causation .............................................................................................26 An infamous episode .................................................................................................... ....27 Terror, utopianism and power .............................................................................................29 Faith and science .................................................................................................... ...........29 Fear and power: the precautionary principle.................................................................30 Utopian salvation........................................................................................... ....................32....

Several uncorroborated, false, or misinterpreted conceptions have for years been widely distributed in academic publications, thus becoming scientific myths. How can such misconceptions persist and proliferate within the inimical environment of academic criticism? Examining 613 articles we demonstrate that the reception of three myth-exposing publications is skewed by an ‘affirmative citation bias’: The vast majority of articles citing the critical article will affirm the idea criticized. 468 affirmed the myth, 105 were neutral, while 40 took a negative stance. Once (...) misconceptions proliferate wide and long enough, criticizing them not only becomes increasingly difficult, efforts may even contribute to the continued spreading of the myths. (shrink)

Possible measures to mitigate climate change require global collective actions whose impacts will be felt by many, if not all. Implementing such actions requires successful communication of the reasons for them, and hence the underlying climate science, to a degree that far exceeds typical scientific issues which do not require large-scale societal response. Empirical studies have identified factors, such as the perceived level of consensus in scientific opinion and the perceived reliability of scientists, that can limit people's trust in science (...) communicators and their subsequent acceptance of climate change claims. Little consideration has been given, however, to recent formal results within philosophy concerning the relationship between truth, the reliability of evidence sources, the coherence of multiple pieces of evidence/testimonies, and the impact of independence between sources of evidence. This study draws on these results to evaluate exactly what has been established in the empirical literature about the factors that bias the public's reception of scientific communications about climate change. (shrink)

I discuss under what conditions the objection that an expert’s argument is biased by her self-interest can be a meaningful and sound argumentative move. I suggest replacing the idea of bias qua self-interest by that of a conflict of interests, exploit the distinction between an expert context and a public context, and hold that the objection can be meaningful. Yet, the evaluation is overall negative, because the motivational role of self-interest for human behavior remains unclear. Moreover, if recent social-psychological (...) results from the “heuristics and biases” program are accepted, it is plausible to assume that humans also satisfice (rather than optimize/maximize) when identifying and then acting in their self-interest. My thesis is: insofar as the objection is sound with a particular audience, it is not needed; and insofar as the objection is needed, it is unsound. (shrink)

Prior research has documented the continued existence of an expectation gap, defined as the divergence between the public’s and the profession’s conceptions of auditor’s duties, despite the auditing profession’s attempt to adopt standards and practices to close this gap. In this paper, we consider one potential explanation for the persistence of the expectation gap: the role of media bias in shaping public opinion and views. We analyze press articles covering 40 U.S. corporate fraud cases discovered between 1992 and 2011. (...) We compare the auditor’s duties, described by the auditing standards, with the description of the fraud cases as found in the press articles. We draw upon prior research to identify three sources of the expectation gap: deficient performance, deficient standards, and unreasonable expectations. The results of our analysis provide evidence that the performance gap can be reduced by strengthening auditor’s willingness and ability to apply existing auditing standards concerning fraud detection; the standards gap can be narrowed by improving existing auditing standards; and unreasonable expectations, however, involve elements beyond the profession’s sphere of control. As a result, the expectation gap is unlikely to disappear given the media’s tendency to bias, with an overemphasis of unreasonable expectations in their coverage of frauds and press articles tending to reinforce the view that the auditor should take more responsibility for detecting fraud, irrespective of whether this is feasible at a reasonable cost. In addition to the primary role of the press in perpetuating the expectation gap, a second reason for continuation of the expectation gap is that the rational auditor will have difficulty in assessing subjective components of fraudulent behavior. (shrink)

Our understanding of implicit bias and how to measure it has yet to be settled. Various debates between cognitive scientists are unresolved. Moreover, the public’s understanding of implicit bias tests continues to lag behind cognitive scientists’. These discrepancies pose potential problems. After all, a great deal of implicit bias research has been publicly funded. Further, implicit bias tests continue to feature in discourse about public- and private-sector policies surrounding discrimination, inequality, and even the purpose of science. (...) We aim to do our part by reconstructing some of the recent arguments in ordinary language and then revealing some of the operative norms or values that are often hidden beneath the surface of these arguments. This may help the public learn more about the science of implicit bias. It may also help both laypeople and scientists reflect on the values, interests, and stakeholders involved in establishing, justifying, and communicating scientific research. (shrink)

What I suggest we can see in this brief overview of the literature is an extensive interpenetration on both sides of these debates between scientific, political, and social values. Important shifts in political and social values were of course occurring over the same period, some of them in parallel with, and perhaps even contributing to, these transitions I have been speaking of in evolutionary discourse. The developments that I think of as at least suggestive of possible parallels include the progressive (...) encroachment of public values into the private domain of post-World War II American life, the cold war, the rise of consumerism, and the flowering of what Christopher Lasch calls a “narcissistic individualism.”35 In popular language, the 1960s gave birth to the “me” generation. Perhaps the most tantalizing analogue is suggested by Barbara Ehrenreich's argument for the emergence of a new meaning of masculinity — an ideal of masculinity measured not by commitment, responsibility, or success as family provider, but precisely by the strength of a man's autonomy in the private sphere, his resistance to the demands of a hampering female.36 It is tempting to speculate about possible connections between changes in scientific discourse and developments in the social and political spheres, but such connections, however suggestive, would clearly have to be demonstrated.For now, however, I want to focus on another kind of change —a transformation not so much in the social or political sphere as in the scientific sphere. I make this turn, or return, in support of a more complex account of scientific change that incorporates reverberations within the scientific communty along with social and political changes.In the 1960s, all of biology was undergoing a major transformation in direct response to the dramatic successes of molecular biology. These successes seemed to completely vindicate the values on which the molecular revolution was premised — namely, simplicity and mechanism. Following the victory of Watson and Crick, and of others after them, the fever of that endeavor swept through biology leaving in its wake a new standard of science, and of scientific discourse — one predicated on clarity, simplicity, and analyzability; on the definition of legitimate questions as those capable of clear and unambiguous answers. Every biological discipline felt it — even evolutionary biology, which in some respects was at the furthest pole. Perhaps precisely because it seemed conceptually so remote, evolutionary biology may have felt it most of all. Lewontin inadvertently provides us with some direct support for this view. Indeed, he begins his introduction to Population Biology and Evolution with the following remarks: The twenty years since World War II have seen a vindication in biology of our faith in the Cartesian method as a way of doing science. Some of the most fundamental and interesting problems of biology have been solved or are very nearly solved by an analytic technique that is now loosely called “molecular biology.” But it is not specifically the “molecular” aspect of biology of the last twenty years that has led to its success. It is, rather, the analytic aspect, the belief that by breaking systems down into their component parts, by simplifying them or using simpler organisms, one can learn about more complex systems. As it happens, the problems that were attacked and are being attacked by this method lead to answers in terms of molecules and cell organelles.... There is a host of problems in biology, however, that has been much neglected in these twenty exciting years, because the answers to them cannot be meaningfully framed in molecular and cellular terms.37Lewontin is referring, of course, to problems in evolution. The remainder of his remarks is devoted to an argument for the applicability of the method, if not the content, of molecular biology to these problems. He writes, “It is not the case that molecular biology is Cartesian and analytic while population biology is holistic. Population biology is properly analytic and operates, within the framework of its own problems, by the process of simplification, analysis, and resynthesis.”38 With these remarks, he leads into the criticism of the “holists” who have “held up progress.”This new ethic of simplicity, clarity, and mechanism — embodying the very virtues lauded by Williams — was explicitly carried into evolutionary biology in the name of scientific progress. As it happened, the values implied also fit conveniently well with other values — each set of values providing crucial support for the other.However substantive the scientific gains may have been in some respects, the net effect of this ethic has also been a systematic “perceptual bias” — a bias with profound practical consequences for the entire program of methodological individualism in evolutionary biology, if not elsewhere as well. It may well be that the whole is equivalent to the reconstituted aggregate of its parts, if, in the process of aggregation or summation, all possible interactions among the parts are included. But if certain kinds of interactions are systematically excluded, our confidence in that program necessarily founders. My claim here is that such systematic exclusion does occur, and that it occurs on a number of different levels. To briefly review the interlocking kinds of “bias” that I see occurring in practice, I suggest the following schematic listing:On the most general level: The ethic of simplicity — the privileging of certain values, even certain methodologies, as having an a priori superior claim to scientific credibility.Only slightly less general, and crucially related, is the equation of “scientific” with “tractible”: Given the techniques of analysis available, the equation of science with what we can do inevitably leads to a systematic technical bias favoring simplicity. That is, because we don't know how to model complex dynamics, nonlinear interactions are systematically biased against because of the limitations of our technical know-how. The consequences of this equation of the scientific with the tractible are greatly compounded by the additional equation between what we can do and what is — that is, by our temptation to confuse tractibility with reality.Finally, and also closely related, a further kind of elision occurs even within the confines of tractibility. This kind of elision — taking the form almost of inferring tractibility from one's prior assumptions of what is real — is exemplified by the history of a mathematical ecology of mutualism. Even when mutualism can be introduced into the same technical machinery, it is still not pursued. The basic assumption is that competition is what is real, not because it is easier to model, but because it is what we expect. When the actual difficulties of modeling competition are then in turn suppressed, as in the Robert May story, what we have, given the temptation to equate the tractible with the real, is the possibility of a truly self-fulfilling prophecy. (shrink)

High percentages of submitted papers are rejected at editorial levels without offering a second chance to authors by sending their papers for further peer-reviews. In most cases, the rejections are typical quick answers without helpful argumentations related to the content of the rejected material. More surprisingly, some journals vaunt their high rejection rates as a “mark of prestige”!However, journals that reject high percentages of submitted papers have built their prominent positions based on a flawed measure, the impact factor, and from (...) a long and favorable historical context. Their shareholders may think that they are allowed to have a large margin of rejection rates without affecting their sponsorship or funding sources thanks to an extended anchorage since tens, or in some cases hundreds, of years compared to unknown or new journals that struggle to pave a way in the scientific publication world. Historical anchorage of some journals also makes it unfair to compare old and new .. (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)

One of the predominant debates in AI Ethics is the worry and necessity to create fair, transparent and accountable algorithms that do not perpetuate current social inequities. I offer a critical analysis of Reuben Binns’s argument in which he suggests using public reason to address the potential bias of the outcomes of machine learning algorithms. In contrast to him, I argue that ultimately what is needed is not public reason per se, but an audit of the implicit moral assumptions (...) of societies within which algorithms are built and applied. Public justification is appealing since it offers us the possibility to align the decision-making outcomes of the algorithm with the core moral values of stakeholders within a constitutional democratic society. My concern is that the common moral principles that form the foundation of public reason are not necessarily neutral, as they still express specific moral ideals and normative standards even if there is moral agreement by society as a whole, or among different stakeholders within society. Appealing to such normative standards may thus still lead to algorithmic outcomes being biased as common moral values may very well still be discriminatory even though they are formed from a consensus, and even if public reason is applied as a kind of filter for potential algorithmic outcomes. Hence, I argue that these implicit moral norms within society that we take as a given in public reasoning, need to be audited from generation to generation in order to effectively mitigate potential algorithmic bias. (shrink)

Publication date: 30 August 2017 Source: Author: Ghulam Abid, Tahira Hassan Butt Employees are the building blocks and valuable assets in an organization. Organizational researchers and practitioners have shown a burgeoning attention to satisfy and retain key performer as the cost of leaving a job is very high for the employing organizations. Discovering turnover intention in its formation stages is very crucial, not only to resist its’ piled up effect but also to control the actual turnover in the future. (...) Most of the times, management is not aware of the employee’s quit intention because employees don’t show their intention explicitly until they actually leave the organization. However, majority of the times employees share their feeling with their colleagues or other close work mates. Based on positive relationship at work, we argued that the individuals who work together normally share their feelings with their close colleagues regarding their decision of leaving or staying with the current employer. Therefore, the objective of the current study is to investigate the relationship between turnover intention and expressed turnover intention. Furthermore, we offer a new perspective to data collection from other sources, which will assist us in shortening the questionnaire, also minimizing the common method bias and knowing the quit intention from other source. Finally, theoretical and practical implications along with direction for future studies are also discussed. (shrink)

Amid public health concerns over climate change, “precision public health” is emerging in next generation approaches to practice. These novel methods promise to augment public health operations by using ever larger and more robust health datasets combined with new tools for collecting and analyzing data. Precision strategies to protecting the public health could more effectively or efficiently address the systemic threats of climate change, but may also propagate or exacerbate health disparities for the populations most vulnerable in a changing climate. (...) How PPH interventions collect and aggregate data, decide what to measure, and analyze data pose potential issues around privacy, neglecting social determinants of health, and introducing algorithmic bias into climate responses. Adopting a health justice framework, guided by broader social and climate justice tenets, can reveal principles and policy actions which may guide more responsible implementation of PPH in climate responses. (shrink)

To evaluate how study characteristics and methodological aspects compare based on presence or absence of industry funding, Hughes et al. conducted a systematic survey of randomized controlled trials (RCTs) published in three major medical journals. The authors found industry-funded RCTs were more likely to be blinded, post results on a clinical trials registration database (ClinicalTrials.gov), and accrue high citation counts.1 Conversely, industry-funded trials had smaller sample sizes and more frequently used placebo as the comparator, used a surrogate as their primary (...) outcome, and had positive results. (shrink)

Recently, many scientists have become concerned about an excessive number of failures to reproduce statistically significant effects. The situation has become dire enough that the situation has been named the ‘reproducibility crisis’. After reviewing the relevant literature to confirm the observation that scientists do indeed view replication as currently problematic, I explain in philosophical terms why the replication of empirical phenomena, such as statistically significant effects, is important for scientific progress. Following that explanation, I examine various diagnoses of the reproducibility (...) crisis, and argue that for the majority of scientists the crisis is due, at least in part, to a form of publication bias. This conclusion sets the stage for an assessment of the view that evidential relations in science are inherently value-laden, a view championed by Heather Douglas and Kevin Elliott. I argue, in response to Douglas and Elliott, and as motivated by the meta-scientific resistance scientists harbour to a publication bias, that if we advocate the value-ladenness of science the result would be a deepening of the reproducibility crisis. (shrink)

Thomas Christiano argues that democracies acquire a right to rule by being the unique embodiment of publicly accessible rules. Justice requires the equal advancement of the interests of all. However, due to the need for citizens to shape a common world despite disagreement and limitations of human cognition, publicity is a necessary constraint on the pursuit of justice. Given that democracy is necessary to secure public equality, democratic authority is thus justified, as democracy is the only political arrangement that satisfies (...) both the publicity constraint and equality requirement. Christiano’s argument depends on a claim that individuals should be given the right to advance what they take to be their interests. This right is defended through two interlocking claims. First, according individuals this right advances three fundamental interests: i) correcting cognitive biases; ii) being at home in the world; and iii) equal moral standing. Second, the three fundamental interests imply that any collective decision-making procedure must be publicly accessible. Thus, any argument against the right to judge for oneself either violates the publicity requirement or the equality constraint. In this paper, I argue that Christiano’s argument faces a problem—under a two-stage plural voting system, an unequal distribution of voting rights can satisfy both the publicity requirement and the equality constraint. Thus, Christiano’s claim that public equality requires an equal distribution of the right to judge for oneself is mistaken. (shrink)