The main aim of this article is to reflect on the impact of biases related to artificial intelligence (AI) systems developed to tackle issues arising from the COVID-19 pandemic, with special focus on those developed for triage and risk prediction. A secondary aim is to review assessment tools that have been developed to prevent biases in AI systems. In addition, we provide a conceptual clarification for some terms related to biases in this particular context. We focus mainly (...) on nonracial biases that may be less considered when addressing biases in AI systems in the existing literature. In the manuscript, we found that the existence of bias in AI systems used for COVID-19 can result in algorithmic justice and that the legal frameworks and strategies developed to prevent the apparition of bias have failed to adequately consider social determinants of health. Finally, we make some recommendations on how to include more diverse professional profiles in order to develop AI systems that increase the epistemic diversity needed to tackle AI biases during the COVID-19 pandemic and beyond. (shrink)

This paper mainly analyzes the theories related to the financial risk of the company and combines the principles of principal component analysis, particle swarm optimization algorithm, and artificial neural network to derive the financial risk index system of the company. To improve the accuracy of financial risk prediction, principal component analysis and particle swarm algorithm are applied to optimize the BP neural network model, the input data of the prediction model is improved, and the optimal (...) initial weights and thresholds are given to the BP neural network by using particle swarm algorithm search, whereby the financial risk prediction model of particle swarm optimization BP neural network is constructed. The empirical results show that the model constructed by BP neural network not only has a high accuracy rate for static financial risk evaluation but also has a better prediction effect. After training and testing, the BP neural network-based enterprise financial risk evaluation model can accurately determine the existing financial situation of enterprise financial management and has a good prediction effect. Our research method is a fusion of the processing of the two methods, which belongs to the first integration of results. (shrink)

Introduction: The coronavirus disease 2019 pandemic has placed intensive care units (ICU) triage at the center of bioethical discussions. National and international triage guidelines emerged from professional and governmental bodies and have led to controversial discussions about which criteria—e.g. medical prognosis, age, life-expectancy or quality of life—are ethically acceptable. The paper presents the main points of agreement and disagreement in triage protocols and reviews the ethical debate surrounding them. Sources of data: Published articles, news articles, book chapters, (...) ICU triage guidelines set out by professional societies and health authorities. Areas of agreement: Points of agreement in the guidelines that are widely supported by ethical arguments are (i) to avoid using a first come, first served policy or quality-adjusted life-years and (ii) to rely on medical prognosis, maximizing lives saved, justice as fairness and non-discrimination. Areas of controversy: Points of disagreement in existing guidelines and the ethics literature more broadly regard the use of exclusion criteria, the role of life expectancy, the prioritization of healthcare workers and the reassessment of triage decisions. Growing points: Improve outcome predictions, possibly aided by Artificial intelligence (AI); develop participatory approaches to drafting, assessing and revising triaging protocols; learn from experiences with implementation of guidelines with a view to continuously improve decision-making. Areas timely for developing research: Examine the universality vs. context-dependence of triaging principles and criteria; empirically test the appropriateness of triaging guidelines, including impact on vulnerable groups and risk of discrimination; study the potential and challenges of AI for outcome and preference prediction and decision-support. (shrink)

Entrepreneurship plays an important role in the development of national economy. The study aims to accelerate the construction of social and economic structure by improving the success rate of new entrepreneurs in the process of innovation and entrepreneurship. First, the related theories of financial risk prediction are introduced, and entrepreneurial psychological status and the psychological states on entrepreneurship are analyzed. Second, the current situation of entrepreneurial psychology of new entrepreneurs is analyzed through a questionnaire survey and model test. (...) The results show that private enterprises account for the largest proportion, with a percentage of 58.14% of 125. In total, 32 Chinese-foreign joint ventures rank second and account for 14.88% of the total, and the scale of each type of enterprises accounts for 25%, respectively. The operating years of enterprises are mainly between 10 and 20 years. Among the enterprises surveyed, the significant level of entrepreneurial psychology and entrepreneurial opportunity of entrepreneurs is p = 0.000–0.01. It indicates that males’ psychological adjustment ability and entrepreneurial ability in the entrepreneurial process are higher than females’, and their entrepreneurial psychological states and entrepreneurial ability will improve with the growth of age, education levels, and positions. It is concluded that entrepreneurial psychological capital and entrepreneurial opportunity are significantly positively correlated with financial risk expectation. The research results prove the impact of financial risks on the entrepreneurial psychology of new entrepreneurs, and provide a reference for new entrepreneurs in predicting financial risks. (shrink)

The present study examined effects of sociodemographic, risk, and resilience factors on marital, parental, and financial stress early in the COVID-19 pandemic. A cross-sectional online survey was conducted among 480 married Palestinians living in Israel, using self-report questionnaires. Descriptive statistics and bivariate correlations were computed. Then, hierarchical multiple regression analyses were conducted to predict each of the three stress measures. Finally, dominance analyses were conducted to compare the contributions of sociodemographic, risk, and resilience factors. The results showed considerable (...) differences between predictors of marital and parental stress and predictors of financial stress. For marital and parental stress, resilience factors mainly family resilience were the strongest predictors, followed by risk factors and then sociodemographic characteristics. For financial stress, risk factors were the strongest predictors, followed by sociodemographic characteristics and resilience factors. These results suggest that family and individual resilience reduce marital and parental stress under difficult conditions, including the COVID-19 pandemic. Though resilience has been found to ameliorate financial stress to some extent, it appears that other measures are required to manage stress related to financial difficulties. (shrink)

This paper proposes and analyzes the psychometric properties of the PREVI-A scale. It describes the process of item development, the factorial structure of the scale, reliability, evidence of validity and diagnostic performance with regard to recidivism risk in juvenile offenders. The sample was made up of 212 juvenile offenders held at detention centers run by the Madrid Agency for Reeducation and Reintegration of Juvenile Offenders, a regional government body. Statistical analyses were used to corroborate the theoretical factorial structure of (...) the PREVI-A, which consists of six risk/protection dimensions based on the Risk-Needs-Responsivity Model, and to obtain empirical support for the reliability and validity of PREVI-A as a tool to assess the risk of recidivism by juvenile offenders in Spain. (shrink)

As suicide rates increase worldwide, the mental health industry has reached an impasse in attempts to assess patients, predict risk, and prevent suicide. Traditional assessment tools are no more accurate than chance, prompting the need to explore new avenues in artificial intelligence (AI). Early studies into these tools show potential with higher accuracy rates than previous methods alone. Medical researchers, computer scientists, and social media companies are exploring these avenues. While Facebook leads the pack, its efforts stem from scrutiny (...) following suicides and suicide attempts broadcasted on Facebook Live. The company continues to face scrutiny for ethical, privacy, and safety concerns associated with the quick rollout of proprietary AI technology that seems more focused on the optics of the company than the protection of its billions of users. This paper explores some of these issues, including a lack of transparency, questionable data practices, escalation to law enforcement, little to no regulation, potential for bias, increased viewership of troubling content, and worsening thoughts of suicide among vulnerable Facebook users. Despite AI tools showing promise in improving the accuracy of suicide risk predictions over traditional methods, without regulation, external review, and increased protection of users and data, these tools have the potential to cause more harm than good in the hands of powerful companies like Facebook. (shrink)

In the age of big data, machine learning models are globally used to execute default risk prediction. Imbalanced datasets and redundant features are two main problems that can reduce the performance of machine learning models. To address these issues, this study conducts an analysis from the viewpoint of different balance ratios as well as the selection order of feature selection. Accordingly, we first use data rebalancing and feature selection to obtain 32 derived datasets with varying ratios of balance (...) and feature combinations for each dataset. Second, we propose a comprehensive metric model based on multimachine learning algorithms to select the best-derived dataset with the optimal balance ratio and feature combination. Finally, the convolutional neural network is trained on the selected derived dataset to evaluate the performance of our approach in terms of type-II error, accuracy, G-mean, and AUC. There are two contributions in this study. First, the optimal balance ratio is found through the classification accuracy, which changes the deficiency of the existing research that samples are imbalanced or the balance ratio is 1 : 1 and ensures the accuracy of the classification model. Second, a comprehensive metric model based on the machine learning algorithm is proposed, which can simultaneously find the best balance ratio and the optimal feature selection. The experimental results show that our method can noticeably improve the performance of CNN, and CNN outperforms the other four commonly used machine learning models in the task of default risk prediction on four benchmark datasets. (shrink)

Risk taking is a component of the decision-making process in situations that involve uncertainty and in which the probability of each outcome – rewards and/or negative consequences – is already known. The influence of cognitive and emotional processes in decision making may affect how risky situations are addressed. First, inaccurate assessments of situations may constitute a perceptual bias in decision making, which might influence RT. Second, there seems to be consensus that a proneness bias exists, known as risk (...) proneness, which can be defined as the propensity to be attracted to potentially risky activities. In the present study, we take the approach that risk perception and risk proneness affect RT behaviours. The study hypothesises that locus of control, emotion regulation, and executive control act as perceptual biases in RT, and that personality, sensation seeking, and impulsivity traits act as proneness biases in RT. The results suggest that locus of control, emotion regulation and executive control influence certain domains of RT, while personality influences in all domains except the recreational, and sensation seeking and impulsivity are involved in all domains of RT. The results of the study constitute a foundation upon which to build in this research area and can contribute to the increased understanding of human behaviour in risky situations. (shrink)

Algorithmic predictions are promising for insurance companies to develop personalized risk models for determining premiums. In this context, issues of fairness, discrimination, and social injustice might arise: Algorithms for estimating the risk based on personal data may be biased towards specific social groups, leading to systematic disadvantages for those groups. Personalized premiums may thus lead to discrimination and social injustice. It is well known from many application fields that such biases occur frequently and naturally when prediction models (...) are applied to people unless special efforts are made to avoid them. Insurance is no exception. In this paper, we provide a thorough analysis of algorithmic fairness in the case of insurance premiums. We ask what “fairness” might mean in this context and how the fairness of a premium system can be measured. For this, we apply the established fairness frameworks of the fair machine learning literature to the case of insurance premiums and show which of the existing fairness criteria can be applied to assess the fairness of insurance premiums. We argue that two of the often-discussed group fairness criteria, independence (also called statistical parity or demographic parity) and separation (also known as equalized odds), are not normatively appropriate for insurance premiums. Instead, we propose the sufficiency criterion (also known as well-calibration) as a morally defensible alternative that allows us to test for systematic biases in premiums towards certain groups based on the risk they bring to the pool. In addition, we clarify the connection between group fairness and different degrees of personalization. Our findings enable insurers to assess the fairness properties of their risk models, helping them avoid reputation damage resulting from potentially unfair and discriminatory premium systems. (shrink)

Recent scholarship in philosophy of science and technology has shown that scientific and technological decision making are laden with values, including values of a social, political, and/or ethical character. This paper examines the role of value judgments in the design of machine-learning systems generally and in recidivism-prediction algorithms specifically. Drawing on work on inductive and epistemic risk, the paper argues that ML systems are value laden in ways similar to human decision making, because the development and design of (...) ML systems requires human decisions that involve tradeoffs that reflect values. In many cases, these decisions have significant—and, in some cases, disparate—downstream impacts on human lives. After examining an influential court decision regarding the use of proprietary recidivism-prediction algorithms in criminal sentencing, Wisconsin v. Loomis, the paper provides three recommendations for the use of ML in penal systems. (shrink)

Elselijn Kingma argues that Christopher Boorse’s biostatistical theory does not show how the reference classes it uses—namely, age groups of a sex of a species—are objective and naturalistic. Boorse has replied that this objection is of no concern, because there are no examples of clinicians’ choosing to use reference classes other than the ones he suggests. Boorse argues that clinicians use the reference classes they do because these reflect the natural classes of organisms to which their patients belong. Drawing on (...) a thorough exploration of how the disease osteoporosis is defined in adults, I argue that clinicians do indeed make choices about which reference classes to use in diagnosis. Clinicians use young adult reference classes to diagnose osteoporosis in elderly patients. They also use young female reference classes to diagnose osteoporosis in elderly males. Clinicians adjust their reference classes so that the diagnosis of osteoporosis reflects a person’s risk of sustaining a fragility fracture. The ethical intuition that people with the same risk of fracture should receive the same diagnosis overwhelms the naturalistic intuition that reference classes should reflect natural classes of organisms of uniform functional design. Clinicians construct a variety of reference class types, including pathological reference classes and epidemiological population-specific reference classes, to serve this ethical intuition. I show how clinicians use several reference classes at once so that they can more accurately predict risk of fracture. Ultimately, the reference classes chosen and used in medical practice are quite different from those proposed in naturalistic philosophy of medicine. (shrink)

The construction of a financial risk prediction model has become the need of the hour due to long-term and short-term violations in the financial market. To reduce the default risk of peer-to-peer companies and promote the healthy and sustainable development of the P2P industry, this article uses a model based on the LightGBM algorithm to analyze a large number of sample data from Renrendai, which is a representative platform of the P2P industry. This article explores the base (...) LightGBM model along with the integration of linear blending to build an optimal default risk identification model. The proposed approach is applicable for a large number of multi-dimensional data samples. The results show that the prediction accuracy rate of the LightGBM algorithm model on the test set reaches 80.25%, which can accurately identify more than 80% of users, and the model has the best prediction performance in terms of different performance evaluation indicators. The integration of LightGBM and the linear blending approach yield a precision value of 91.36%, a recall of 75.90%, and an accuracy of 84.36%. The established LightGBM algorithm can efficiently identify the default of the loan business on the P2P platform compared to the traditional machine learning models, such as logistic regression and support vector machine. For a large number of multi-dimensional data samples, the LightGBM algorithm can effectively judge the default risk of users on P2P platforms. (shrink)

Epigenetic markers could potentially be used for risk assessment in risk-stratified population-based cancer screening programmes. Whereas current screening programmes generally aim to detect existing cancer, epigenetic markers could be used to provide risk estimates for not-yet-existing cancers. Epigenetic risk-predictive tests may thus allow for new opportunities for risk assessment for developing cancer in the future. Since epigenetic changes are presumed to be modifiable, preventive measures, such as lifestyle modification, could be used to reduce the (...) class='Hi'>risk of cancer. Moreover, epigenetic markers might be used to monitor the response to risk-reducing interventions. In this article, we address ethical concerns related to personal responsibility raised by epigenetic risk-predictive tests in cancer population screening. Will individuals increasingly be held responsible for their health, that is, will they be held accountable for bad health outcomes? Will they be blamed or subject to moral sanctions? We will illustrate these ethical concerns by means of a Europe-wide research programme that develops an epigenetic risk-predictive test for female cancers. Subsequently, we investigate when we can hold someone responsible for her actions. We argue that the standard conception of personal responsibility does not provide an appropriate framework to address these concerns. A different, prospective account of responsibility meets part of our concerns, that is, concerns about inequality of opportunities, but does not meet all our concerns about personal responsibility. We argue that even if someone is responsible on grounds of a negative and/or prospective account of responsibility, there may be moral and practical reasons to abstain from moral sanctions. (shrink)

an observation to formulate a theory, it is no surprise that the resulting theory accurately captures that observation. However, when the theory makes a novel prediction—when it predicts an observation that was not used in its formulation—this seems to provide more substantial confirmation of the theory. This paper presents a new approach to the vexed problem of understanding the epistemic difference between prediction and accommodation. In fact, there are several problems that need to be disentangled; in all of (...) them, the key is the concept of overfitting. We float the hypothesis that accommodation is a defective methodology only when the methods used to accommodate the data fail to guard against the risk of overfitting. We connect our analysis with the proposals that other philosophers have made. We also discuss its bearing on the conflict between instrumentalism and scientific realism. Introduction Predictivisms—a taxonomy Observations Formulating the problem What might Annie be doing wrong? Solutions Observations explained Mayo on severe tests The miracle argument and scientific realism Concluding comments. (shrink)

PurposeWhite matter damage (WMD) was defined as the appearance of rough and uneven echo enhancement in the white matter around the ventricle. The aim of this study was to develop and validate a risk prediction model for neonatal WMD.Materials and MethodsWe collected data for 1,733 infants hospitalized at the Department of Neonatology at The First Affiliated Hospital of Zhengzhou University from 2017 to 2020. Infants were randomly assigned to training (n= 1,216) or validation (n= 517) cohorts at a (...) ratio of 7:3. Multivariate logistic regression and least absolute shrinkage and selection operator (LASSO) regression analyses were used to establish a risk prediction model and web-based risk calculator based on the training cohort data. The predictive accuracy of the model was verified in the validation cohort.ResultsWe identified four variables as independent risk factors for brain WMD in neonates by multivariate logistic regression and LASSO analysis, including gestational age, fetal distress, prelabor rupture of membranes, and use of corticosteroids. These were used to establish a risk prediction nomogram and web-based calculator (https://caowenjun.shinyapps.io/dynnomapp/). The C-index of the training and validation sets was 0.898 (95% confidence interval: 0.8745–0.9215) and 0.887 (95% confidence interval: 0.8478–0.9262), respectively. Decision tree analysis showed that the model was highly effective in the threshold range of 1–61%. The sensitivity and specificity of the model were 82.5 and 81.7%, respectively, and the cutoff value was 0.099.ConclusionThis is the first study describing the use of a nomogram and web-based calculator to predict the risk of WMD in neonates. The web-based calculator increases the applicability of the predictive model and is a convenient tool for doctors at primary hospitals and outpatient clinics, family doctors, and even parents to identify high-risk births early on and implementing appropriate interventions while avoiding excessive treatment of low-risk patients. (shrink)

The mortality and morbidity resulting from noncommunicable diseases including cancer in sub- Saharan Africa are predicted to overtake that of infectious diseases by the year 2030. Esophageal cancer is on the increase in Tanzania. This study estimates risk factors for esophageal cancer, ethical issues and the level of awareness of cancer related services among adults in rural Kilimanjaro. A cross sectional descriptive study was conducted of adults aged 18 years and above in three wards, namely, Kahe, mabogini and Arusha (...) Chini, Moshi Rural District, Kilimanjaro region. Face-to-face interviews were conducted with a total of 419 individuals. The mean age was 36.47 =13.49) years. Of those who participated, 211 were male and 60% reported to have completed primary education. A majority of participants were knowledgeable about esophageal cancer risk factors. Overall, 15% and 23% of the participants reported to have been smoking and drinking alcohol, respectively. Male respondents were almost three times more likely to be smokers as compared to female respondents. Moreover, people who were self-employed were about five times more likely to be smokers as compared to those who were unemployed.. Male respondents were almost two times more likely to be drinkers as compared to female respondents. Overall, the level of awareness of cancer related health services was about 70%. The study findings confirm that despite a good level of knowledge about risk factors for esophageal cancer, the practice of exposure to risk factors is alarmingly high. Awareness of where to seek cancer related health services was high with hospitals been mentioned by the majority though there was low understanding on modalities of treatment among adults in Rural Kilimanjaro. Intervention measures should aim at further increasing knowledge of cancer risk factors and reduce the practice of such risks. Ethical matters including truth telling, ageism and autonomy need to be addressed to improve the perception of cancer related health issues and health seeking behaviour. (shrink)

Recommender systems are recently developed computer-assisted tools that support social and informational needs of various communities and help users exploit huge amounts of data for making optimal decisions. In this study, we present a new recommender system for assessment and risk prediction in child welfare institutions in Israel. The system exploits a large diachronic repository of manually completed questionnaires on functioning of welfare institutions and proposes two different rule-based computational models. The system accepts users’ requests via a simple (...) graphical interface, calculates the institutions’ profiles according to user preferences, and presents assessment scores, trends and comparative analyses of the corresponding data using assorted visual aids. Based on the analysis, the system offers three different strategies for objective assessment of the institutions’ functioning and risks. Qualitative and quantitative evaluation of the system’s effectiveness and accuracy demonstrates that it substantially improves the assessment process of a welfare institution. Moreover, it provides an effective tool for objective large-scale analysis of the institution’s overall state and trends, which were previously based primarily on the institution supervisors’ subjective judgment and intuition. In addition, the proposed recommender system has great practical and social impact as it may help identify and avert potential problems, malfunctions, flaws, risks and even tragic incidents in child welfare institutions, as well as increase their overall functioning levels. As a result, as a long-term social implication, the system may also help reduce inequality and social gaps in the Israeli society. (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)

Purpose Existing algorithms for predicting suicide risk rely solely on data from electronic health records, but such models could be improved through the incorporation of publicly available socioeconomic data – such as financial, legal, life event and sociodemographic data. The purpose of this study is to understand the complex ethical and privacy implications of incorporating sociodemographic data within the health context. This paper presents results from a survey exploring what the general public’s knowledge and concerns are about such publicly (...) available data and the appropriateness of using it in suicide risk prediction algorithms. Design/methodology/approach A survey was developed to measure public opinion about privacy concerns with using socioeconomic data across different contexts. This paper presented respondents with multiple vignettes that described scenarios situated in medical, private business and social media contexts, and asked participants to rate their level of concern over the context and what factor contributed most to their level of concern. Specific to suicide prediction, this paper presented respondents with various data attributes that could potentially be used in the context of a suicide risk algorithm and asked participants to rate how concerned they would be if each attribute was used for this purpose. Findings The authors found considerable concern across the various contexts represented in their vignettes, with greatest concern in vignettes that focused on the use of personal information within the medical context. Specific to the question of incorporating socioeconomic data within suicide risk prediction models, the results of this study show a clear concern from all participants in data attributes related to income, crime and court records, and assets. Data about one’s household were also particularly concerns for the respondents, suggesting that even if one might be comfortable with their own being used for risk modeling, data about other household members is more problematic. Originality/value Previous studies on the privacy concerns that arise when integrating data pertaining to various contexts of people’s lives into algorithmic and related computational models have approached these questions from individual contexts. This study differs in that it captured the variation in privacy concerns across multiple contexts. Also, this study specifically assessed the ethical concerns related to a suicide prediction model and determining people’s awareness of the publicness of select data attributes, as well as which of these data attributes generated the most concern in such a context. To the best of the authors’ knowledge, this is the first study to pursue this question. (shrink)

The use of risk assessment tools has come to play an increasingly important role in sentencing decisions in many jurisdictions. A key issue in the theoretical discussion of risk assessment concerns the predictive accuracy of such tools. For instance, it has been underlined that most risk assessment instruments have poor to moderate accuracy in most applications. However, the relation between, on the one hand, judgements of the predictive accuracy of a risk assessment tool and, on the (...) other, conclusions concerning the justified use of such an instrument in sentencing practice, is often very unclear. The purpose of this paper is to examine this relation. More precisely, it is argued that the relation between predictive accuracy and the question as to whether a new risk assessment tool should be introduced in sentencing practice is highly complicated. For instance, there may be cases in which a new risk assessment tool is more accurate than those currently in use, but should nevertheless not be introduced; and conversely, where a new tool is less accurate, but where its introduction instead of current tools would be morally preferable. (shrink)

Background and ObjectivesSchool bullying threatens the health of children and adolescents, such as mental health disorders, social deviant behaviors, suicidal behaviors, and coping difficulties. The present study aims to address prevalence rates of both traditional and cyber school bullying perpetration, and the associations between self-control, parental involvement, experiencing conflicts with parents, experiencing interparental conflict, and risk behaviors, and school bullying perpetration among Chinese children and adolescents.MethodThis study used data from a national representative school bullying survey among children and adolescents (...) from all grades in seven cities in China. Negative binomial regression was used to estimate the effects of these predictive factors on traditional and cyber school bullying perpetration, respectively. Seven control variables were included, such as gender, boarding school, family socioeconomic status, and parents’ education levels.ResultsThe sample comprised 52% female, 18% at boarding school, 70% of the participants’ academic performance was average or above. Approximately 17.3% of the participants reported participating in traditional school bullying against their peers, and 7.8% perpetrated cyberbullying behaviors. Also, after controlling sociodemographic characteristics and high self-control, parental involvement reduced the likelihood of traditional and cyberbullying perpetrating. Experiencing interparental conflict and risk behavior was significantly associated with increased perpetration of traditional and cyber school bullying. We found that having a conflict with parents was significantly associated with cyberbullying perpetration.ImplicationsFindings have implications for practice. Anti-bullying intervention programs targeting this population should consider these factors. For example, school administrators may develop school programs involving parents in the efforts and interventions workshops improving children and adolescents’ levels of self-control. Limitations are also discussed. (shrink)

Volume 20, Issue 7, July 2020, Page 207-209.

Objectives: The objective of this study is to investigate the relationship between a physician’s subjective mortality prediction and the level of confidence with which that mortality prediction is made.Design and participants: The study is a prospective cohort of patients less than 18 years of age admitted to a tertiary Paediatric Intensive Care Unit at a University Children’s Hospital with a minimum length of ICU stay of 10 h. Paediatric ICU attending physicians and fellows provided mortality risk predictions (...) and the level of confidence associated with these predictions on consecutive patients at the time of multidisciplinary rounds within 24 hours of admission to the paediatric ICU. Median confidence levels were compared across different ranges of mortality risk predictions.Results: Data were collected on 642 of 713 eligible patients . Mortality predictions greater than 5% and less than 95% were made with significantly less confidence than those predictions 95%. Experience was associated with greater confidence in prognostication.Conclusions: We conclude that a physician’s subjective mortality prediction may be dependent on the level of confidence in the prognosis; that is, a physician less confident in his or her prognosis is more likely to state an intermediate survival prediction. Measuring the level of confidence associated with mortality risk predictions may therefore be important because different levels of confidence may translate into differences in a physician’s therapeutic plans and their assessment of the patient’s future. (shrink)

Under conditions with a low level of available genetic information, mutualistic private insurance markets will often create broadly just outcomes, even if by accident rather than by design. Normatively acceptable outcomes of this kind would come under threat if insurers were to have increased access to genetic information with substantial predictive content.1 As the availability of relevant individual genetic information grows, mutualistic forms of market-based insurance face a dilemma between either sacrificing individuals’ interests in genetic privacy, or creating conditions for (...) market failure due to adverse selection. My view is therefore that we have good reason to shift towards more solidaristic forms of ‘risk-sharing institutions’ with regard to functions such as life insurance and critical illness insurance, as a way of resolving normative tensions between values of equality, privacy, non-discrimination and autonomy. Such a position, I have argued, does not depend on endorsing any particular conception of social justice, but can be defended on the basis of a number of different plausible accounts of what justice demands.1 Jonathan Pugh argues against my endorsement of a shift towards solidaristic, non-market risk-sharing institutions, and instead argues broadly in favour of the status quo. He holds that it counts against my proposals that they ‘have wide-ranging and highly revisionary implications’, and argues that my approach is mistaken in relying on ‘a single contestable theory’. Pugh offers a purportedly less contestable account of the relevant normative considerations underlying insurance regimes, composed of three …. (shrink)

The debate over the genetic testing of minors has developed into a major bioethical topic. Although several controversial questions remain unanswered, a degree of consensus has been reached regarding the policies on genetic testing of minors. Recently, several commentators have suggested that these policies are overly restrictive, too narrow in focus, and even in conflict with the limited empirical evidence that exists on this issue. We respond to these arguments in this paper, by first offering a clarification of three key (...) concepts—autonomy of the minor, future autonomy, and parental authority—which must be disentangled. We then respond to the arguments by noting the uncertainty of the value of predictive genetic information, and by assessing the psychosocial risks still involved in genetic testing of minors, which are also largely unknown. We conclude that the current consensus position is justified at this stage, in light of the predictions of harm resulting from genetic testing of minors that have not been adequately proved to be unwarranted. (shrink)

Based on the existing financial system risk models, a set of time-lag financial system risk models is established considering the influence brought by time-lag factors on the financial risk system, and the dynamical behavior of this system is analyzed by using chaos theory. Through Matlab simulation, the bifurcation diagram and phase diagram of time-lag risk intensity and control intensity are plotted. The analysis shows that this kind of time-lag financial system risk model has complex dynamic (...) behavior, different motion states will appear when different parameter values are selected, and the time-lag risk intensity parameter also has a very strong influence on the system motion. To ensure the operation of the financial system in a stable state, measures with certain delay effects must be taken to control the risk and to choose the appropriate time-lag control intensity, and too much or too little time-lag control intensity is not conducive to the benign operation of the system. (shrink)

This study investigates the volatility risk premium on the emerging financial market. We also consider the expected return and ESG sentiment. Based on the SSE 50 ETF 5-minute high-frequency spots and daily options data from 2016 to 2021, we adopt nonparametric model-free approaches to calculate realized and implied volatilities. And the volatility risk premium is constructed by subtracting these volatility series. We examine the relations between the volatility risk premium and future excess returns as well as ESG (...) sentiment through multifactor specifications. We find that the volatility risk premium also exists in the Chinese market and is significantly negative. In addition, the statistically positive correlation between the volatility risk premium and aggregate returns is an outlier compared to the empirically negative pattern in developed markets. At last, ESG sentiment is positively associated with the volatility risk premium, especially the impact of environmental and social. This evidence supports the agency theory, which indicates that investors perceive ESG investments as waste resources in a short term and become potentially risky. (shrink)

We often show a greater inclination to assist and avoid harming people identified as those at high risk of great harm than to assist and avoid harming people who will suffer similar harm but are not identified (as yet). Call this the identified person bias. Some ethicists think such bias is justified; others disagree and claim that the bias is discriminatory against statistical people. While the issue is present in public policy and politics, perhaps the most notable examples can (...) be found in medical ethics such as in ICU triage decisions made during the COVID‐19 pandemic. The Rule of Rescue, as the application of the identified person bias is sometimes called, states that spending large amounts of resources rescuing identifiable individuals who are in imminent danger is justified. In this paper, I show that our distorted attitudes toward time play a role in identified person bias. I claim that ICU triage decisions are better explained by a preference to treat people sooner rather than later, which is at least partially due to near bias (positive events are to be preferred to be near rather than distant), than by a preference to treat identified lives over statistical lives. Thus, another bias, near bias, is involved in the reasoning behind the identified person bias and the Rule of Rescue. (shrink)

With the ascent of modern epidemiology in the Twentieth Century came a new standard model of prediction in public health and clinical medicine. In this article, we describe the structure of the model. The standard model uses epidemiological measures-most commonly, risk measures-to predict outcomes (prognosis) and effect sizes (treatment) in a patient population that can then be transformed into probabilities for individual patients. In the first step, a risk measure in a study population is generalized or extrapolated (...) to a target population. In the second step, the risk measure is particularized or transformed to yield probabilistic information relevant to a patient from the target population. Hence, we call the approach the Risk Generalization-Particularization (Risk GP) Model. There are serious problems at both stages, especially with the extent to which the required assumptions will hold and the extent to which we have evidence for the assumptions. Given that there are other models of prediction that use different assumptions, we should not inflexibly commit ourselves to one standard model. Instead, model pluralism should be standard in medical prediction. (shrink)

Prevention of age-related disorders is increasingly in focus of health policies, and it is hoped that early intervention on processes of deterioration can promote healthier and longer lives. New opportunities to slow down the aging process are emerging with new fields such as personalized nutrition. Data-intensive research has the potential to improve the precision of existing risk factors, e.g., to replace coarse-grained markers such as blood cholesterol with more detailed multivariate biomarkers. In this paper, we follow an attempt to (...) develop a new aging biomarker. The vision among the project consortium, comprising both research and industrial partners, is that the new biomarker will be predictive of a range of age-related conditions, which may be preventable through personalized nutrition. We combine philosophical analysis and ethnographic fieldwork to explore the possibilities and challenges of managing aging through bodily signs that are not straightforwardly linked to symptomatic disease. We document how the improvement of measurement brings about new conceptual challenges of demarcating healthy and unhealthy states. Moreover, we highlight that the reframing of aging as risk has social and ethical implications, as it is generative of normative notions of what constitutes successful aging and good citizenship. (shrink)

Unethical pro-organisational behaviour is an ongoing concern, prompting the need for more nuanced understanding of the workplace environment most likely to inhibit it. This study considers the role of risk climate, sometimes referred to as risk culture, as well as ethical climate, for reducing UPB. The study investigates whether four risk climate factors can, by focusing on the long-term consequences of UPB to the organisation, and providing guidance on behavioural norms, reduce UPB misconduct. Surveying employees in three (...) financial institutions we found that only one of the risk climate factors, avoidance, is consistently associated with UPB. On the other hand, a self-interested, ethical climate significantly increases UPB in all samples. In multi-climate analysis, we found that risk climate factors interacted with ethical climate to predict UPB. That is, when employees perceive that the workplace ethos is not self-interested, three factors of risk climate—avoidance, proactive and manager—significantly predict UPB. UPB is least likely when self-interest is low, avoidance risk climate is low, proactive risk climate is high and manager risk climate is high. Implications for leaders are discussed. (shrink)

Shared activity is often simply willed into existence by individuals. This poses a problem. Philosophical reflection suggests that shared activity involves a distinctive, interlocking structure of intentions. But it is not obvious how one can form the intention necessary for shared activity without settling what fellow participants will do and thereby compromising their agency and autonomy. One response to this problem suggests that an individual can have the requisite intention if she makes the appropriate predictions about fellow participants. I argue (...) that implementing this predictive strategy risks derailing practical reasoning and preventing one from forming the intention. My alternative proposal for reconciling shared activity with autonomy appeals to the idea of acting directly on another's intention. In particular, I appeal to the entitlement one sometimes has to another's practical judgment, and the corresponding authority the other sometimes has to settle what one is to do. (shrink)

As software piracy continues to be a threat to the growth of national and global economies, understanding why people continue to use pirated software and learning how to discourage the use of pirated software are urgent and important issues. In addition to applying the theory of planned behavior (TPB) perspective to capture behavioral intention to use pirated software, this paper considers perceived risk as a salient belief influencing attitude and intention toward using pirated software. Four perceived risk components (...) related to the use of pirated software (performance, social, prosecution and psychological risks) have been identified, measured and tested. Data were collected through an online survey of 305 participants. The results indicate that perceived prosecution risk has an impact on intention to use pirated software, and perceived psychological risk is a strong predictor of attitude toward using pirated software. In addition, attitude and perceived behavior control contribute significantly to the intended use of pirated software. However, the proposed direct relationship between subjective norm and intention to use pirated software is not supported. Implications for research and practice are discussed. (shrink)

In The Empire of Chance, historians of science Gigerenzer et al. argue that statistical thinking has been “second to no other area of scientific endeavor” in its influence on “modern life and thought”. This article describes how quantitative descriptions of risk associated with industrialization and technological change became part of the mentality of ordinary Americans. It explains why Americans began counting accidents, tells what kinds of accidents they counted and how they counted them, and shows how statistical representations of (...) risk were used to justify prescriptions for public policy and individual behavior. On this frontier of the empire of chance, safety experts and self-styled “practical statisticians” were the principal colonizers. Distant from the centers of academic statistical science, they compromised rigorous scientific standards of methodology and accurate prediction in order to make convincing arguments outside their circles of expertise. To convey their point of view to audiences who were not literate in the field of statistics, they created a public language that conveyed statistical ideas through metaphors, graphic representations, and other rhetorical devices. They also engaged non-experts in collecting and analyzing data and, by the 1920s, even used quantitative self-measurement as a device to convince members of the public to alter their own risk-taking behaviors. (shrink)

The so-called predictive coding or predictive processing theory of mind has attracted significant attention in the brain and behavioral sciences over the past couple of decades. We aim to discuss an important ethical implication of the theory’s success. As predictive coding has become influential in the study of mental disorder and illness, particularly on autism spectrum disorder and schizophrenia, we point out a significant risk of further harming an already stigmatized population. Specifically, because predictive coding is undergirded by Bayesian (...) inference, and Bayesian inference is often thought to imply ‘rationality’, the cognitive framework engenders a risk of strengthening existing negative attitudes towards individuals having mental disorders and illnesses by associating such individuals with also having ‘irrational brains.’ ER -. (shrink)

In this paper, we analyse higher-order risky choices by the representative cumulative prospect theory (CPT) decision maker from three alternative reference points. These are the status quo, average payout and maxmin. The choice tasks we consider in our analysis include binary risks, and are the ones employed in the experimental literature on higher order risk preferences. We demonstrate that the choices made by the representative subject depend on the reference point. If the reference point is the status quo and (...) the lottery choices exhibit symmetric risk, we demonstrate that there is no third order reflection effect of lottery choices but there is a fourth order reflection effect. When the average payout is the reference point, we demonstrate that any third or fourth order lottery choice is possible dependent upon the lottery payoffs. However, under the assumption of maxmin reference point, the risky choices are prudent and temperate. In addition to these results, our analysis reveals that the representative CPT subject can choose combinations of second with third and fourth order risky options that differ from those in other major models of decision under risk. We contrast our theoretical predictions with the empirical results reported in the literature on higher order risk preferences and are able to reconcile some conflicting experimental evidence. (shrink)

In recent years, in the UK and elsewhere, scientists and science policymakers have grappled with the question of how to reap the benefits of nanotechnologies while minimising the risks. Having recognised the importance of public support for future innovations, they have placed increasing emphasis on ‘engaging’ ‘the public’ during the early phase of technology development. Meaningful engagement suggests some common ground between experts and lay publics in relation to the definition of nanotechnologies and of their benefits and risks. However, views (...) on nanotechnologies are likely to vary according to where actors stand in the technology production/consumption/assessment cycle. Drawing on data from a recent UK-based study, this article examines how scientists (‘upstream’ and ‘downstream’) and policymakers portray the benefits and risks of nanotechnologies, particularly as they relate to two major areas of predicted application, namely medicine/public health and environmental sustainability. The findings reveal that, in the main, scientists and science policymakers held a positive conception of nanotechnologies and see imminent applications, although they acknowledged particular risks, including adverse public reaction. While definitions of ‘benefit’ and ‘risk’ varied, most saw the benefits as outweighing the risks and believed that the risks could be adequately regulated once they were assessed. The difficulties of assessing risk, however, were acknowledged. The study raises a number of questions that will need to be addressed if regulations are to be developed that not only protect people’s heath and wellbeing and the environment but also engender public trust in nanotechnologies. (shrink)