Violence risk assessment tools are increasingly used within criminal justice and forensic psychiatry, however there is little relevant, reliable and unbiased data regarding their predictive accuracy. We argue that such data are needed to (i) prevent excessive reliance on risk assessment scores, (ii) allow matching of different risk assessment tools to different contexts of application, (iii) protect against problematic forms of discrimination and stigmatisation, and (iv) ensure that contentious demographic variables are not prematurely removed (...) from risk assessment tools. (shrink)

The EU Artificial Intelligence Act (AIA) defines four risk categories for AI systems: unacceptable, high, limited, and minimal. However, it lacks a clear methodology for the assessment of these risks in concrete situations. Risks are broadly categorized based on the application areas of AI systems and ambiguous risk factors. This paper suggests a methodology for assessing AI risk magnitudes, focusing on the construction of real-world risk scenarios. To this scope, we propose to integrate the AIA (...) with a framework developed by the Intergovernmental Panel on Climate Change (IPCC) reports and related literature. This approach enables a nuanced analysis of AI risk by exploring the interplay between (a) risk determinants, (b) individual drivers of determinants, and (c) multiple risk types. We further refine the proposed methodology by applying a proportionality test to balance the competing values involved in AI risk assessment. Finally, we present three uses of this approach under the AIA: to implement the Regulation, to assess the significance of risks, and to develop internal risk management systems for AI deployers. (shrink)

The primary responsibility of the US Food and Drug Administration is to protect public health by ensuring the safety of the food supply. To that end, it sometimes conducts risk assessments of novel food products, such as genetically modified food. The FDA describes its regulatory review of GM food as a purely scientific activity, untainted by any normative considerations. This paper provides evidence that the regulatory agency is not justified in making that claim. It is argued that the FDA’s (...) policy stance on GM food is shaped by neoliberal considerations. The agency’s review of a genetically engineered animal, the AquAdvantage salmon, is used as a case study to track the influence of neoliberalism on its regulatory review protocol. After that, an epistemic argument justifying public engagement in the risk assessment of new GM food is outlined. It is because risk evaluations involve normative judgments, in a democracy, layperson representatives of informal epistemic communities that could be affected by a new GM food should have the opportunity to decide the ethical, political or other normative questions that arise during the regulatory review of that entity. (shrink)

In this paper, we distinguish between different sorts of assessments of algorithmic systems, describe our process of assessing such systems for ethical risk, and share some key challenges and lessons for future algorithm assessments and audits. Given the distinctive nature and function of a third-party audit, and the uncertain and shifting regulatory landscape, we suggest that second-party assessments are currently the primary mechanisms for analyzing the social impacts of systems that incorporate artificial intelligence. We then discuss two kinds of (...) as-sessments: an ethical risk assessment and a narrower, technical algo-rithmic bias assessment. We explain how the two assessments depend on each other, highlight the importance of situating the algorithm within its particular socio-technical context, and discuss a number of lessons and challenges for algorithm assessments and, potentially, for algorithm audits. The discussion builds on our team’s experience of advising and conducting ethical risk assessments for clients across dif-ferent industries in the last four years. Our main goal is to reflect on the key factors that are potentially ethically relevant in the use of algo-rithms, and draw lessons for the nascent algorithm assessment and audit industry, in the hope of helping all parties minimize the risk of harm from their use. (shrink)

Algorithmic risk assessment tools, such as COMPAS, are increasingly used in criminal justice systems to predict the risk of defendants to reoffend in the future. This paper argues that these tools may not only predict recidivism, but may themselves causally induce recidivism through self-fulfilling predictions. We argue that such “performative” effects can yield severe harms both to individuals and to society at large, which raise epistemic-ethical responsibilities on the part of developers and users of risk (...) class='Hi'>assessment tools. To meet these responsibilities, we present a novel desideratum on algorithmic tools, called explainability-in-context, which requires clarifying how these tools causally interact with the social, technological, and institutional environments they are embedded in. Risk assessment practices are thus subject to high epistemic standards, which haven't been sufficiently appreciated to date. Explainability-in-context, we contend, is a crucial goal to pursue in addressing the ethical challenges surrounding risk assessment tools. (shrink)

Call for Written evidence - Risk Assessment and Risk Planning.

Credit risk, also known as default risk, is the likelihood of a corporation losing money if a business partner defaults. If the liabilities are not met under the terms of the contract, the firm may default, resulting in the loss of the company. There is no clear way to distinguish between organizations that will default and those that will not prior to default. We can only make probabilistic estimations of the risk of default at best. There are (...) two types of credit risk default models in this regard: structural and reduced form models. Structural models are used to calculate the likelihood of a company defaulting based on its assets and liabilities. If the market worth of a company's assets is less than the debt it owes, it will default. Reduced form models often assume an external cause of default, such as a Poisson jump process, which is driven by a stochastic process. They model default as a random event with no regard for the balance sheet of the company. This paper provides a Review of credit risk default models. (shrink)

Risk is an intrinsic part of our lives. In the future, the development and growth of the Internet of things allows getting a huge amount of data. Considering this evolution, our research focuses on developing a novel concept, namely Holistic Risk Assessment (HRA), that takes into consideration elements outside the direct influence of the individual to provide a highly personalized risk assessment. The HRA implies developing a methodology and a model. This paper is related to (...) the epistemological positioning of this research. We consider this research as an artificial science under the constructivism paradigm. We also introduce the positioning of this research in the complexity science paradigm. We end this paper by presenting a methodology based on an adapted Design Thinking Model. (shrink)

Biobanks act as the custodians for the access to and responsible use of human biological samples and related data that have been generously donated by individuals to serve the public interest and scientific advances in the health research realm. Risk assessment has become a daily practice for biobanks and has been discussed from different perspectives. This paper aims to provide a literature review on risk assessment in order to put together a comprehensive typology of diverse risks (...) biobanks could potentially face. Methodologically set as a typology, the conceptual approach used in this paper is based on the interdisciplinary analysis of scientific literature, the relevant ethical and legal instruments and practices in biobanking to identify how risks are assessed, considered and mitigated. Through an interdisciplinary mapping exercise, we have produced a typology of potential risks in biobanking, taking into consideration the perspectives of different stakeholders, such as institutional actors and publics, including participants and representative organizations. With this approach, we have identified the following risk types: economic, infrastructural, institutional, research community risks and participant’s risks. The paper concludes by highlighting the necessity of an adaptive risk governance as an integral part of good governance in biobanking. In this regard, it contributes to sustainability in biobanking by assisting in the design of relevant risk management practices, where they are not already in place or require an update. The typology is intended to be useful from the early stages of establishing such a complex and multileveled biomedical infrastructure as well as to provide a catalogue of risks for improving the risk management practices already in place. (shrink)

The EU proposal for the Artificial Intelligence Act (AIA) defines four risk categories: unacceptable, high, limited, and minimal. However, as these categories statically depend on broad fields of application of AI, the risk magnitude may be wrongly estimated, and the AIA may not be enforced effectively. This problem is particularly challenging when it comes to regulating general-purpose AI (GPAI), which has versatile and often unpredictable applications. Recent amendments to the compromise text, though introducing context-specific assessments, remain insufficient. To (...) address this, we propose applying the risk categories to specific AI scenarios, rather than solely to fields of application, using a risk assessment model that integrates the AIA with the risk approach arising from the Intergovernmental Panel on Climate Change (IPCC) and related literature. This model enables the estimation of the magnitude of AI risk by considering the interaction between (a) risk determinants, (b) individual drivers of determinants, and (c) multiple risk types. We use large language models (LLMs) as an example. (shrink)

In this article, I show why it is necessary to abolish the use of predictive algorithms in the US criminal justice system at sentencing. After presenting the functioning of these algorithms in their context of emergence, I offer three arguments to demonstrate why their abolition is imperative. First, I show that sentencing based on predictive algorithms induces a process of rewriting the temporality of the judged individual, flattening their life into a present inescapably doomed by its past. Second, I demonstrate (...) that recursive processes, comprising predictive algorithms and the decisions based on their predictions, systematically suppress outliers and progressively transform reality to match predictions. In my third and final argument, I show that decisions made on the basis of predictive algorithms actively perform a biopolitical understanding of justice as management and modulation of risks. In such a framework, justice becomes a means to maintain a perverse social homeostasis that systematically exposes disenfranchised Black and Brown populations to risk. (shrink)

Metaphors for describing the introduction, impacts, and management of non-native species are numerous and often quite outspoken. Policy-makers have adopted increasingly disputed metaphorical terms from scientific discourse. We performed a critical analysis of the use of strong metaphors in reporting scientific findings to policy-makers. Our analysis shows that perceptions of harm, invasiveness or nativeness are dynamic and inevitably display multiple narratives in science, policy or management. Improving our awareness of multiple expert and stakeholder narratives that exist in the context of (...) non-native species management, as well as metaphorical alternatives, is critical. (shrink)

An axiom of medical research ethics is that a protocol is moral only if it has a “favorable risk-benefit ratio”. This axiom is usually interpreted in the following way: a medical research protocol is moral only if it has a positive expected value -- that is, if it is likely to do more good (to both subjects and society) than harm. I argue that, thus interpreted, the axiom has two problems. First, it is unusable, because it requires us to (...) know more about the potential outcomes of research than we ever could. Second, it is false, because it conflicts with the so-called “soft paternalist” principles of liberal democracy. In place of this flawed rule I propose a new way of making risk-benefit assessments, one that does comport with the principles of liberalism. I argue that a protocol is moral only if it would be entered into by competent subjects who are informed about the protocol. The new rule this eschews all pseudo-utilitarian calculation about the protocol’s likely harms and benefits. (shrink)

A capability approach has been proposed to risk analysis, where risk is conceptualized as the probability that capabilities are reduced. Capabilities refer to the genuine opportunities of individuals to achieve valuable doings and beings, such as being adequately nourished. Such doings and beings are called functionings. A current debate in risk analysis and other fields where a capability approach has been developed concerns whether capabilities or actual achieved functionings should be used. This paper argues that in (...) class='Hi'>risk analysis the consequences of hazardous scenarios should be conceptualized in terms of capabilities, not achieved functionings. Furthermore, the paper proposes a method for assessing capabilities, which considers the levels of achieved functionings of other individuals with similar boundary conditions. The capability of an individual can then be captured statistically based on the variability of the achieved functionings over the considered population. (shrink)

Risk assessment is one of the prerequisites for understanding its causes and possible consequences. We base our risk assessment on the principles described in the European standard EN 31000 - Risk Management Process. This standard comprehensively describes the continuous activities that are necessary in managing risks and minimizing their possible adverse effects on the operation of the system under investigation. In this activity, it is necessary to first identify the existing risks, then analyze and evaluate (...) the identified risks. In the analysis of existing risks, it is possible to use both qualitative and quantitative analytical methods, or combine them. We use qualitative methods in cases where we do not have a sufficient amount of input information, these are more subjective. Quantitative methods are more accurate, but also more demanding on input information and time. The choice of a suitable analytical method is a basic prerequisite for knowledge of risks and their evaluation. The values of individual risks obtained in this way are the basis for determining the measures that are ecessary to minimize them, i.e., to adjust them to an acceptable level. The draft measures are always based on the value of the individual components used to calculate the risk number, as well as on the value of the asset , which needs to be protected. Appropriately chosen analytical methods are one of the basic prerequisites for the consistent application of the principles of risk management, as a continuous process aimed at increasing the overall security of the system under study. In the article, the author describes the procedures used in risk assessment, as well as specific analytical methods that can be used in working with risks. The aim of identifying risk factors is to create a list of events that could cause undesirable disruption to ongoing processes. At this stage, we define all the risks that will be subsequently analyzed and evaluated. When identifying, we can use methods such as, e.g. SWOT, PHA (Preliminary Hazard Analysis) or CA (Checklist Analysis). Methods suitable for determining the causes and creating scenarios for the course of a risk event are ETA (Event Tree Analysis) or FTA (Fault Tree Analysis). The basic analysis of the system can be performed using the FMEA method (Failure Mode and Effect Analysis), which provides a numerical risk assessment. By comparison with the numerical value of the risk that we are willing to accept, we obtain 2 groups of risks. Acceptable, which will be given regular attention and unacceptable, which we will focus on in risk management and we will try to minimize its negative affect on the functioning of the system under study. (shrink)

Suppose an agent is choosing between rescuing more people with a lower probability of success, and rescuing fewer with a higher probability of success. How should they choose? Our moral judgments about such cases are not well-studied, unlike the closely analogous non-moral preferences over monetary gambles. In this paper, I present an empirical study which aims to elicit the moral analogues of our risk preferences, and to assess whether one kind of evidence—concerning how they depend on outcome probabilities—can debunk (...) them. I find significant heterogeneity in our moral risk preferences—in particular, moral risk-seeking and risk-neutrality are surprisingly popular. I also find that subjects’ judgments aren't probability-dependent, thus providing an empirical defense against debunking arguments from probability dependence. (shrink)

Abstract: This research focuses on utilizing Artificial Neural Networks (ANNs) to predict Audit Risk accurately, a critical aspect of ensuring financial system integrity and preventing fraud. Our dataset, gathered from Kaggle, comprises 18 diverse features, including financial and historical parameters, offering a comprehensive view of audit-related factors. These features encompass 'Sector_score,' 'PARA_A,' 'SCORE_A,' 'PARA_B,' 'SCORE_B,' 'TOTAL,' 'numbers,' 'marks,' 'Money_Value,' 'District,' 'Loss,' 'Loss_SCORE,' 'History,' 'History_score,' 'score,' and 'Risk,' with a total of 774 samples. Our proposed neural network architecture, consisting (...) of three layers (1 input, 1 hidden, and 1 output), forms the core of this study. While it may seem simple, the power of ANNs lies not in complexity but in their ability to uncover intricate data patterns. The model underwent rigorous training and validation, resulting in remarkable outcomes—an accuracy of 100% and an average error rate of 0.000015. In addition to performance metrics, our research investigates feature importance, revealing the key contributors to Audit Risk prediction. Notably, 'Sector_score,' 'PARA_A,' 'SCORE_A,' 'PARA_B,' 'SCORE_B,' 'TOTAL,' 'numbers,' 'marks,' 'Money_Value,' 'District,' 'Loss,' 'Loss_SCORE,' 'History,' and 'History_score' emerged as the most influential factors, shedding light on the elements crucial for precise risk assessment. This study advances Audit Risk prediction models and highlights the potential of ANNs in strengthening financial system integrity and fraud prevention. The insights gained from our findings offer practical guidance for stakeholders in the audit and financial sectors. (shrink)

International regulation of autonomous weapon systems (AWS) is increasingly conceived as an exercise in risk management. This requires a shared approach for assessing the risks of AWS. This paper presents a structured approach to risk assessment and regulation for AWS, adapting a qualitative framework inspired by the Intergovernmental Panel on Climate Change (IPCC). It examines the interactions among key risk factors—determinants, drivers, and types—to evaluate the risk magnitude of AWS and establish risk tolerance thresholds (...) through a risk matrix informed by background knowledge of event likelihood and severity. Further, it proposes a methodology to assess community risk appetite, emphasizing that such assessments and resulting tolerance levels should be determined through deliberation in a multistakeholder forum. The paper highlights the complexities of applying risk-based regulations to AWS internationally, particularly the challenge of defining a global community for risk assessment and regulatory legitimization. (shrink)

A small but growing number of studies have aimed to understand, assess and reduce existential risks, or risks that threaten the continued existence of mankind. However, most attention has been focused on known and tangible risks. This paper proposes a heuristic for reducing the risk of black swan extinction events. These events are, as the name suggests, stochastic and unforeseen when they happen. Decision theory based on a fixed model of possible outcomes cannot properly deal with this kind of (...) event. Neither can probabilistic risk analysis. This paper will argue that the approach that is referred to as engineering safety could be applied to reducing the risk from black swan extinction events. It will also propose a conceptual sketch of how such a strategy may be implemented: isolated, self-sufficient, and continuously manned underground refuges. Some characteristics of such refuges are also described, in particular the psychosocial aspects. Furthermore, it is argued that this implementation of the engineering safety strategy safety barriers would be effective and plausible and could reduce the risk of an extinction event in a wide range of possible scenarios. Considering the staggering opportunity cost of an existential catastrophe, such strategies ought to be explored more vigorously. (shrink)

Abstract: This research employs a Random Forest classification model to predict and assess obesity and cardiovascular disease (CVD) risk based on a comprehensive dataset collected from individuals in Mexico, Peru, and Colombia. The dataset comprises 17 attributes, including information on eating habits, physical condition, gender, age, height, and weight. The study focuses on classifying individuals into different health risk categories using machine learning algorithms. Our Random Forest model achieved remarkable performance with an accuracy, F1-score, recall, and precision all (...) reaching 97.23%. The model's success is attributed to its ability to effectively leverage attributes related to eating habits, physical condition, and demographic factors. Feature importance analysis reveals key factors contributing to accurate predictions. This paper contributes to the field of health analytics by demonstrating the effectiveness of Random Forest in predicting and categorizing health risks. The insights gained from this research have the potential to inform personalized health interventions and contribute to the advancement of precision health strategies. Furthermore, the study highlights the importance of leveraging machine learning techniques for health risk assessment in diverse populations. (shrink)

Over the past year, technology companies have made headlines claiming that their artificially intelligent (AI) products can outperform clinicians at diagnosing breast cancer, brain tumours, and diabetic retinopathy. Claims such as these have influenced policy makers, and AI now forms a key component of the national health strategies in England, the United States, and China. While it is positive to see healthcare systems embracing data analytics and machine learning, concerns remain about the efficacy, ethics, and safety of some commercial, AI (...) health solutions. This paper argues that improved regulation and guidance is urgently required to mitigate risks, ensure transparency and best practice, Without this, patients, clinicians, and other stakeholders cannot be assured of an app’s efficacy, and safety. (shrink)

Abstract: This research paper presents a novel approach to predict audit risks using a neural network model. The dataset used for this study was obtained from Kaggle and comprises 774 samples with 18 features, including Sector_score, PARA_A, SCORE_A, PARA_B, SCORE_B, TOTAL, numbers, marks, Money_Value, District, Loss, Loss_SCORE, History, History_score, score, and Risk. The proposed neural network architecture consists of three layers, including one input layer, one hidden layer, and one output layer. The neural network model was trained and validated, (...) achieving an impressive accuracy of 100% and an average error of 0.000015, indicating its robust predictive capability. Moreover, we conducted feature importance analysis to identify the most influential features for predicting audit risk. The key features found to be critical for classifying fraudulent activities in audit risk prediction are Sector_score, PARA_A, SCORE_A, PARA_B, SCORE_B, TOTAL, numbers, marks, Money_Value, District, Loss, Loss_SCORE, History, and History_score. This research contributes to the field of audit risk prediction by demonstrating the effectiveness of a neural network-based approach and highlighting the importance of specific features in detecting fraudulent activities. The findings have significant implications for auditors and organizations seeking to enhance their audit risk assessment processes, ultimately leading to improved financial transparency and fraud detection. (shrink)

How should contractualists assess the permissibility of risky actions? Both main views on the question, ex ante and ex post, fail to distinguish between different kinds of risk. In this article, I argue that this overlooks a third alternative that I call “objective ex ante contractualism”. Objective ex ante substitutes discounting complaints by epistemic risk in favor of discounting by objective risk. I further argue in favor of this new view. Objective ex ante contractualism provides the best (...) model of justifiability to each. (shrink)

Currently, under the conditions of permanent financial risks that hamper the sustainable economic growth in the financial sector, the development of evaluation and risk management methods both regulated by Basel II and III and others seem to be of special importance. The reputation risk is one of significant risks affecting reliability and credibility of commercial banks. The importance of reputation risk management and the quality of their assessment remain relevant as the probability of decrease in or (...) loss of business reputation influences the financial results and the degree of customers’, partners’ and stakeholders’ confidence. By means of imitating modeling based on Bayesian Networks and the fuzzy data analysis, the article characterizes the mechanism of reputation risk assessment and possible losses evaluation in banks by plotting normal and lognormal distribution functions. Monte-Carlo simulation is used to calculate the probability of losses caused by reputation risks. The degree of standardized histogram similarity is determined on the basis of the fuzzy data analysis applying Hamming distance method. The tree-like hierarchy based on the OWA-operator is used to aggregate the data with Fishburne's coefficients as the convolution scales. The mechanism takes into account the impact of criteria, such as return on equity, goodwill value, the risk assets ratio, the share of the productive assets in net assets, the efficiency ratio of interest bearing liabilities, the risk ratio of credit operations, the funding ratio and reliability index on the business reputation of the bank. The suggested methods and recommendations might be applied to develop the decision-making mechanism targeted at the implementation of reputation risk management system in commercial banks as well as to optimize risk management technologies. (shrink)

Uncertainty, insufficient information or information of poor quality, limited cognitive capacity and time, along with value conflicts and ethical considerations, are all aspects thatmake risk managementand riskcommunication difficult. This paper provides a review of different risk concepts and describes how these influence risk management, communication and planning in relation to forest ecosystem services. Based on the review and results of empirical studies, we suggest that personal assessment of risk is decisive in the management of forest (...) ecosystem services. The results are used together with a reviewof different principles of the distribution of risk to propose an approach to risk communication that is effective aswell as ethically sound. Knowledge of heuristics and mutual information on both beliefs and desires are important in the proposed risk communication approach. Such knowledge provides an opportunity for relevant information exchange, so that gaps in personal knowledge maps can be filled in and effective risk communication can be promoted. (shrink)

In “Risk Based Passenger Screening in Aviation Security: Implications and Variants of a New Paradigm”, Sebastian Weydner-Volkmann describes the current paradigm shift from ‘traditional’ forms of screening to ‘risk based passenger screening’ (RBS) in aviation security. This paradigm shift is put in the context of the wider historical development of risk management approaches. Through a discussion of Michel Foucault, Herfried Münkler and Ulrich Beck, Weydner-Volkmann analyses the shortcomings of such approaches in public security policies, which become especially (...) evident in the aviation security context. As he shows, the turn towards methods of RBS can be seen as an attempt to address a trade-off ‘trilemma’ between the effective provision of security, the implied costs for industry and passengers, and the ethical, legal and societal implications of the screening procedures. In order to analyse foreseeable outcomes of embracing RBS, he differentiates three prototypical variants of the new paradigm on the basis of their main referent and rationale. For each variant, he then subsequently assesses the implications for the ‘trilemma’, after having unveiled the criteria of analysis that will necessarily have to be followed within a serious appraisal of RBS methods. (shrink)

Risk propagation encompasses a plethora of techniques for analyzing how risk “spreads” in a given system. Albeit commonly used in technical literature, the very notion of risk propagation turns out to be a conceptually imprecise and overloaded one. This might also explain the multitude of modeling solutions that have been proposed in the lit- erature. Having a clear understanding of what exactly risk is, how it be quantified, and in what sense it can be propagated is (...) fundamental for devising high-quality risk assessment and decision-making solutions. In this paper, we exploit a previous well-established work about the nature of risk and related notions with the goal of providing a proper interpre- tation of the different notions of risk propagation, as well as revealing and harmonizing the alternative semantics for the links used in common risk propagation graphs. Finally, we discuss how these results can be leveraged in practice to model risk propagation scenarios. (shrink)

Many healthcare practices expose people to risks of harmful outcomes. However, the major theories of moral philosophy struggle to assess whether, when and why it is ethically justifiable to expose individuals to risks, as opposed to actually harming them. Sven Ove Hansson has proposed an approach to the ethical assessment of risk imposition that encourages attention to factors including questions of justice in the distribution of advantage and risk, people’s acceptance or otherwise of risks, and the scope (...) individuals have to influence the practices that generate risk. This paper investigates the ethical justifiability of preventive healthcare practices that expose people to risks including overdiagnosis. We applied Hansson’s framework to three such practices: an ‘ideal’ breast screening service, a commercial personal genome testing service, and a guideline that lowers the diagnostic threshold for hypertension. The framework was challenging to apply, not least because healthcare has unclear boundaries and involves highly complex practices. Nonetheless, the framework encouraged attention to issues that would be widely recognised as morally pertinent. Our assessment supports the view that at least some preventive healthcare practices that impose risks including that of overdiagnosis are not ethically justifiable. Further work is however needed to develop and/or test refined assessment criteria and guidance for applying them. (shrink)

If states are permitted to create and maintain a military force, by what means are they permitted to do so? This paper argues that a theory of just recruitment should incorporate a concern for moral risk. Since the military is a morally risky profession for its members, recruitment policies should be evaluated in terms of how they distribute moral risk within a community. We show how common military recruitment practices exacerbate and concentrate moral risk exposure, using the (...) UK as a case study. We argue that the British state wrongs its citizens by subjecting them to excessively morally risky recruitment practices. Since, we argue, this risk exposure cannot be justified by appealing to the benefits of a military career for recruits, our argument calls for reform of existing practices. Our method of evaluation is generalizable and thus can be used to assess other states’ practices. (shrink)

Recently criticisms against autonomous weapons were presented in a video in which an AI-powered drone kills a person. However, some said that this video is a distraction from the real risk of AI—the risk of unlimitedly self-improving AI systems. In this article, we analyze arguments from both sides and turn them into conditions. The following conditions are identified as leading to autonomous weapons becoming a global catastrophic risk: 1) Artificial General Intelligence (AGI) development is delayed relative to (...) progress in narrow AI and manufacturing. 2) The potential for very cheap manufacture of drones, with prices below 1 USD each. 3) Anti-drone defense capabilities lagging offensive development. 4) Special global military posture encouraging development of drone swarms as a strategic offensive weapon, able to kill civilians. We conclude that while it is unlikely that drone swarms alone will become existential risk, lethal autonomous weapons could contribute to civilizational collapse in case of new world war. (shrink)

In a recent commentary, Kim and colleagues argued that minimal-risk research should be deregulated so that such studies do not require review by an institutional review board. They claim that regulation of minimal-risk studies provides no adequate counterbalancing good and instead leads to a costly human subjects oversight system. We argue that the counterbalancing good of regulating minimal-risk studies is that oversight exists to ensure that respect for persons and justice requirements are satisfied when they otherwise might (...) not be. (shrink)

In Western societies many immigrants live in difficult social and working conditions. Together with other factors, this state of affairs represents a risk for the well being of their children. This article will consider the principle risk factors for child psychopathology and/or distress, with a distinction between temporary and permanent factors and with a peculiar attention to the interplay between risk and protective factors. Risk factors can be ordered in cultural, social, familiar/parental and individual factors. Some (...) of these are general risk factors, applying to child and adolescent psychopathology and distress independently from the status of immigrants’ offspring. Other factors are specific of migration, some of them being related to: a) different ways of immigrated families to situate themselves within the host society ; b) cultural/familiar attitudes in child’s nurture and education; c) the family role of women as well as factors specific of the pregnancy period in immigrants; d) the ability of the school system to enhance and support children’s abilities to integrate within the new society; e) the political/bureaucratic facilitation/impediment to the regularization of VISA, with the consequent effect on the sense of identity/rejection within/from the host society. In conclusion, the programs for monitoring immigrants’ living and health conditions should also include: the assessment of parental skills, the dynamic indicators of risk and protection indexes, the assessment of living conditions and social school environment, with a careful attention to those early signs of discomfort that might precede possible later onset of psychopathology and/or social distress. (shrink)

Risk Management Strategies and Investment Behaviors are considered important factors in the investing activities of the retail investors. This study seeks to determine the relationship between Risk Management Strategies and Investment Behavior of Generation Z retail investors. The study is a correlational research and purposive sampling was used to select the respondents for this study. Cochran’s formula was utilized to determine the total sample size or total number of respondents. Spearman’s Rank-Order Correlation was employed to assess the significant (...) relationship of Risk Management Strategies and Investment Behaviors. Based on the results, it showed that risk management strategies have a significant relationship with one or more of the investment behaviors. Risk avoidance has a significant relationship with confirmation bias. Risk reduction has a significant relationship with all of the investment behaviors such as overconfidence, loss-aversion, recency bias, and confirmation bias. Risk transfer shows a significant relationship with only loss-aversion and confirmation bias. And lastly, risk acceptance also has a significant relationship with all of the investment behaviors. This study recommends exploring other types of behavioral biases under cognitive bias and emotional bias to determine their correlation with risk management strategies. Also, broadening the sample size, demographic scope, and geographic scope of the study can improve the generalizability of the study and provide a more comprehensive understanding of the relationship between investment behavior and risk management strategies. The researchers also recommend to utilize interviews and focus group discussions to gain an in-depth understanding of the underlying values and attitudes concerning investment behavior. (shrink)

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 study employs machine learning to predict diabetes using a Kaggle dataset with 13 features. Our three-layer model achieves an accuracy of 98.73% and an average error of 0.01%. Feature analysis identifies Age, Gender, Polyuria, Polydipsia, Visual blurring, sudden weight loss, partial paresis, delayed healing, irritability, Muscle stiffness, Alopecia, Genital thrush, Weakness, and Obesity as influential predictors. These findings have clinical significance for early diabetes risk assessment. While our research addresses gaps in the field, further work is needed (...) to enhance model generalizability. (shrink)

The Adverse Outcome Pathway (AOP) concept is a knowledge assembly and communication tool to facilitate the transparent translation of mechanistic information into outcomes meaningful to the regulatory assessment of chemicals. The AOP framework and associated knowledgebases (KBs) have received significant attention and use in the regulatory toxicology community. However, it is increasingly apparent that the potential stakeholder community for the AOP concept and AOP KBs is broader than scientists and regulators directly involved in chemical safety assessment. In this (...) paper we identify and describe those stakeholders who currently—or in the future—could benefit from the application of the AOP framework and knowledge to specific problems. We also summarize the challenges faced in implementing pathway-based approaches such as the AOP framework in biological sciences, and provide a series of recommendations to meet critical needs to ensure further progression of the framework as a useful, sustainable and dependable tool supporting assessments of both human health and the environment. Although the AOP concept has the potential to significantly impact the organization and interpretation of biological information in a variety of disciplines/applications, this promise can only be fully realized through the active engagement of, and input from multiple stakeholders, requiring multi-pronged substantive long-term planning and strategies. (shrink)

Some patients, following brain injury, do not outwardly respond to spoken commands, yet show patterns of brain activity that indicate responsiveness. This is “cognitive-motor dissociation” (CMD). Recent research has used machine learning to diagnose CMD from electroencephalogram (EEG) recordings. These techniques have high false discovery rates, raising a serious problem of inductive risk. It is no solution to communicate the false discovery rates directly to the patient’s family, because this information may confuse, alarm and mislead. Instead, we need a (...) procedure for generating case-specific probabilistic assessments that can be communicated clearly. This article constructs a possible procedure with three key elements: (1) a shift from categorical “responding or not” assessments to degrees of evidence; (2) the use of patient-centred priors to convert degrees of evidence to probabilistic assessments; and (3) the use of standardized probability yardsticks to convey those assessments as clearly as possible. (shrink)

Common mental health disorders are rising globally, creating a strain on public healthcare systems. This has led to a renewed interest in the role that digital technologies may have for improving mental health outcomes. One result of this interest is the development and use of artificial intelligence for assessing, diagnosing, and treating mental health issues, which we refer to as ‘digital psychiatry’. This article focuses on the increasing use of digital psychiatry outside of clinical settings, in the following sectors: education, (...) employment, financial services, social media, and the digital well-being industry. We analyse the ethical risks of deploying digital psychiatry in these sectors, emphasising key problems and opportunities for public health, and offer recommendations for protecting and promoting public health and well-being in information societies. (shrink)

The public’s actions will likely have a significant effect on the course of the novel coronavirus (COVID-19) pandemic. Human behavior is conditioned and shaped by information and people’s perceptions. This study investigated the impact of risk perception on trust in government and self-efficacy. It examined whether the use of social media helped people adopt preventive actions during the pandemic. To test this hypothesis, the researchers gathered data from 512 individuals (students and academics) based in Malaysia during the COVID-19 pandemic. (...) Our results suggested that risk perception had a significant effect on trust in government and self-efficacy. Moreover, these correlations were stronger when social media was used as a source for gathering information on COVID-19. In some cases, it even helped users avoid exposure to the virus. This study assessed the relationship between risk perception and the awareness gained from using social media during the pandemic and highlighted how social media usage influences trust in government and self-efficacy. -/- . (shrink)

Recent progress in artificial intelligence (AI) has drawn attention to the technology’s transformative potential, including what some see as its prospects for causing large-scale harm. We review two influential arguments purporting to show how AI could pose catastrophic risks. The first argument — the Problem of Power-Seeking — claims that, under certain assumptions, advanced AI systems are likely to engage in dangerous power-seeking behavior in pursuit of their goals. We review reasons for thinking that AI systems might seek power, that (...) they might obtain it, that this could lead to catastrophe, and that we might build and deploy such systems anyway. The second argument claims that the development of human-level AI will unlock rapid further progress, culminating in AI systems far more capable than any human — this is the Singularity Hypothesis. Power-seeking behavior on the part of such systems might be particularly dangerous. We discuss a variety of objections to both arguments and conclude by assessing the state of the debate. (shrink)

Sortition, i.e. random appointment for public duty, has been employed by societies throughout the years as a firewall designated to prevent illegitimate interference between parties in a legal case and agents of the legal system. In judicial systems of modern western countries, random procedures are mainly employed to select the jury, the court and/or the judge in charge of judging a legal case. Therefore, these random procedures play an important role in the course of a case, and should comply with (...) some principles, such as transparency and complete auditability. Nevertheless, these principles are neglected by random procedures in some judicial systems, which are performed in secrecy and are not auditable by the involved parties. The assignment of cases in the Brazilian Supreme Court is an example of such a procedure, for it is performed using procedures unknown to the parties involved in the judicial cases. This article presents a review of how sortition has been historically employed by societies and discusses how Mathematical Statistics may be applied to random procedures of the judicial system, as it has been applied for almost a century on clinical trials, for example. A statistical model for assessing randomness in case assignment is proposed and applied to the Brazilian Supreme Court. As final remarks, guidelines for the development of good randomization procedures are outlined. (shrink)

methods that have shown promise for improving extreme risk analysis, particularly for assessing the risks of invasive pests and pathogens associated with international trade. We describe the legally inspired regulatory regime for banks, where these methods have been brought to bear on extreme ‘operational risks’. We argue that an ‘advocacy model’ similar to that used in the Basel II compliance regime for bank operational risks and to a lesser extent in biosecurity import risk analyses is ideal for permitting (...) the diversity of relevant evidence about invasive species to be presented and soundly evaluated. We recommend that the process be enhanced in ways that enable invasion ecology to make more explicit use of the methods found successful.. (shrink)

Since the International Agency on Cancer Research’s monograph found glyphosate to be a likely carcinogen, the regulatory focus on the chemical has centred on this determinative criterion for regulatory action. Yet, other pertinent factors, such as the effects of glyphosate on fresh and ground water and ensuing effects on biodiversity, have received less attention as legitimate rationales for regulating the chemical. This underrepresentation prevents a wider policy discussion on the environmental and human health effects of the chemical and fails to (...) disrupt assumptions of path-dependently continuing on agriculture’s chemical treadmill. To avoid ad hoc post hoc chemical regulation, we assess four areas of chemical regulatory oversight in Europe with regard to glyphosate affecting water: (1) the undue emphasis on in laboratorio versus in situ testing; (2) assessing single chemicals (isolated glyphosate) versus admixtures (glyphosate plus surfactants and adjuvants) that are used in practice; (3) the tendency to downplay harms to non-human life; and (4) the lack of policy coherence in the existing regulatory framework. Focusing on European Union regulation of pesticide and water policy affecting aquatic environments, we conclude that issues of measurement and priority have become highly politicised in both science and policy, requiring preventative, precautionary frameworks utilising plural forms of measurement. (shrink)

This article assesses how autonomy and machine learning impact the existential risk of nuclear war. It situates the problem of cyber security, which proceeds by stealth, within the larger context of nuclear deterrence, which is effective when it functions with transparency and credibility. Cyber vulnerabilities poses new weaknesses to the strategic stability provided by nuclear deterrence. This article offers best practices for the use of computer and information technologies integrated into nuclear weapons systems. Focusing on nuclear command and control, (...) avoiding autonomy and machine learning is recommended as one means to reduce the existential risk of unintended nuclear conflict. (shrink)

Looks into evaluation of information provision probability from different sources, based on use of linguistic variables. Formation of functions appurtenant for its unclear variables provides for adoption of decisions by the decision maker, in conditions of nonprobabilistic equivocation. The development of market relations in Ukraine increases the independence and responsibility of enterprises in justifying and making management decisions that ensure their effective, competitive activities. As a result of the analysis, it is determined that the condition of economic facilities can be (...) described and determined by the decision-maker, in the presence of the necessary information. The confidence of the decision-maker in the information received is different and the decisions made have a correspondingly different level of information risk. It is important to substantiate the procedure for assessing the numerical extent of information risk in decision-making based on the information obtained in conditions of uncertainty. The use of a linguistic variable in the processing of expert data presented in the form of a matrix of binary relations of values of the membership function, which allowed to move to further processing of knowledge to support decision-making in the management of industrial, commercial, financial and other activities. As a mathematical model for estimating the numerical measure of information risk when making decisions based on the information obtained in conditions of non-stochastic uncertainty, a model has been developed to model natural language uncertainties, which differs from existing ones by formalizing knowledge taking into account uncertainty of input information. Making such a clear decision in a fuzzy environment has appropriate values of effectiveness and risk. The paper proposes all the functions and accessories of indicators of both quantitative nature and qualitative nature to bring their values in the field of definition to one scale. Then the indicator of the effectiveness of decision-making will be a measure of the clarity of the cross-section of fuzzy subsets, which correspond to the introduced indicators of information risk. The condition of economic facilities can be described and determined by the decision-maker, if the necessary information is available. Decision-making on thenumerical measure of information risk must be determined by a set of basic indicators, which can be both quantitative and qualitative in nature. Predictive values of indicators should be determined in conditions of nonstochastic uncertainty. In this case, the indicators of a quantitative nature can be determined by fuzzy triangular numbers, which implement a high level of confidence in the subjective judgments of experts. Indicators of qualitative nature should be presented in linguistic variables. The values of the indicators of qualitative nature that are predicted must be considered for all fuzzy variable terms-sets of linguistic variables introduced into consideration. For any fuzzy variable, the introduction to the consideration of a clear set of values as carriers of the α-level of its membership function allows to reduce to a single interpretation of the predicted values of indicators of quantitative and qualitative nature in terms of non-stochastic uncertainty. (shrink)

The author has extensive law enforcement experience and the paper is intended to provoke thought on the use of technology as it pertains to information sharing between the police and the private sector. -/- As the world edges closer and closer to the convergence of man and machine, the human capacity to retrieve information is increasing by leaps and bounds. We are on the verge of knowing everything and anything there is to know, and literally in the blink of an (...) eye! -/- This means that police will have the capacity to learn everything about everyone with the only restriction being privacy legislation. But it also means that those involved in immoral, unlawful or illegal activity will have that same capacity and with no such restriction. ‘Bad Brother’ may be far more dangerous than ‘Big Brother!' -/- The global community requires a secure and credible system to retrieve and assess all of the information ‘generally available to the public.' A system that will strive to keep ‘Big Brother’ in check and ‘Bad Brother’ out, all the while providing a means of alerting citizens to genuine risks or to dangerous people. Such as system would help diffuse the systemic inaccurate and harmful profiling that is often based on rumours and innuendo. -/- There is an identified public-private partnership opportunity. A chance to work with privacy advocate groups and background checking private companies to define, design and deliver on something that will be of immense benefit to citizens around the globe. -/- We have an opportunity to create something that will work to ensure that only the best information gets used and in a moralistic, lawful and legal way! Technology continues to move forward at incomprehensible speeds – failure to act could have serious consequences. (shrink)

This article reports the findings of AI4People, an Atomium—EISMD initiative designed to lay the foundations for a “Good AI Society”. We introduce the core opportunities and risks of AI for society; present a synthesis of five ethical principles that should undergird its development and adoption; and offer 20 concrete recommendations—to assess, to develop, to incentivise, and to support good AI—which in some cases may be undertaken directly by national or supranational policy makers, while in others may be led by other (...) stakeholders. If adopted, these recommendations would serve as a firm foundation for the establishment of a Good AI Society. (shrink)

In AI safety research, the median timing of AGI creation is often taken as a reference point, which various polls predict will happen in second half of the 21 century, but for maximum safety, we should determine the earliest possible time of dangerous AI arrival and define a minimum acceptable level of AI risk. Such dangerous AI could be either narrow AI facilitating research into potentially dangerous technology like biotech, or AGI, capable of acting completely independently in the real (...) world or an AI capable of starting unlimited self-improvement. In this article, I present arguments that place the earliest timing of dangerous AI in the coming 10–20 years, using several partly independent sources of information: 1. Polls, which show around a 10 percent of the probability of an early creation of artificial general intelligence in the next 10-15 years. 2. The fact that artificial neural network (ANN) performance and other characteristics, like number of “neurons”, are doubling every year, and extrapolating this tendency suggests that roughly human-level performance will be reached in less than a decade. 3. The acceleration of the hardware performance available for AI research, which outperforms Moore’s law thanks to advances in specialized AI hardware, better integration of such hardware in larger computers, cloud computing and larger budgets. 4. Hyperbolic growth extrapolations of big history models. (shrink)

Investigation of neural and cognitive processes underlying individual variation in moral preferences is underway, with notable similarities emerging between moral- and risk-based decision-making. Here we specifically assessed moral distributive justice preferences and non-moral financial gambling preferences in the same individuals, and report an association between these seemingly disparate forms of decision-making. Moreover, we find this association between distributive justice and risky decision-making exists primarily when the latter is assessed with the Iowa Gambling Task. These findings are consistent with neuroimaging (...) studies of brain function during moral and risky decision-making. This research also constitutes the first replication of a novel experimental measure of distributive justice decision-making, for which individual variation in performance was found. Further examination of decision-making processes across different contexts may lead to an improved understanding of the factors affecting moral behaviour. (shrink)

Over the years, companies have adopted hiring algorithms because they promise wider job candidate pools, lower recruitment costs and less human bias. Despite these promises, they also bring perils. Using them can inflict unintentional harms on individual human rights. These include the five human rights to work, equality and nondiscrimination, privacy, free expression and free association. Despite the human rights harms of hiring algorithms, the AI ethics literature has predominantly focused on abstract ethical principles. This is problematic for two reasons. (...) First, AI principles have been criticized for being vague and not actionable. Second, the use of vague ethical principles to discuss algorithmic risks does not provide any accountability. This lack of accountability creates an algorithmic accountability gap. Closing this gap is crucial because, without accountability, the use of hiring algorithms can lead to discrimination and unequal access to employment opportunities. This paper makes two contributions to the AI ethics literature. First, it frames the ethical risks of hiring algorithms using international human rights law as a universal standard for determining algorithmic accountability. Second, it evaluates four types of algorithmic impact assessments in terms of how effectively they address the five human rights of job applicants implicated in hiring algorithms. It determines which of the assessments can help companies audit their hiring algorithms and close the algorithmic accountability gap. (shrink)