Use Of Risk Analysis Research Articles

European artificial intelligence “trusted throughout the world”: Risk‐based regulation and the fashioning of a competitive common AI market

AbstractThe European Commission has pioneered the coercive regulation of artificial intelligence (AI), including a proposal of banning some applications altogether on moral grounds. Core to its regulatory strategy is a nominally “risk‐based” approach with interventions that are proportionate to risk levels. Yet, neither standard accounts of risk‐based regulation as rational problem‐solving endeavor nor theories of organizational legitimacy‐seeking, both prominently discussed in Regulation & Governance, fully explain the Commission's attraction to the risk heuristic. This article responds to this impasse with three contributions. First, it enrichens risk‐based regulation scholarship—beyond AI—with a firm foundation in constructivist and critical political economy accounts of emerging tech regulation to capture the performative politics of defining and enacting risk vis‐à‐vis global economic competitiveness. Second, it conceptualizes the role of risk analysis within a Cultural Political Economy framework: as a powerful epistemic tool for the discursive and regulatory differentiation of an uncertain regulatory terrain (semiosis and structuration) which the Commission wields in its pursuit of a future common European AI market. Thirdly, the paper offers an in‐depth empirical reconstruction of the Commission's risk‐based semiosis and structuration in AI regulation through qualitative analysis of a substantive sample of documents and expert interviews. This finds that the Commission's use of risk analysis, outlawing some AI uses as matters of deep value conflicts and tightly controlling (at least discursively) so‐called high‐risk AI systems, enables Brussels to fashion its desired trademark of European “cutting‐edge AI … trusted throughout the world” in the first place.

Downside risk and profitability ratios: The case of the New York Stock Exchange

This article analyses the relationship between the volatility and sensitivity measures determined based on accounting profitability ratios and those calculated based on rates of return of public corporations from DJIA. The study employed downside and symmetric risk measures. The average accounting profitability and mean rate of returns on NYSE have also been compared. An additional goal was to present a classification of selected risk measures divided into accounting and market ones. This study examined the profitability and risk of the largest companies listed on the NYSE. The risk measurement was performed with the measures based on information from financial statements as well as measures whose calculation was based on the market share prices. The total risk was considered in the context of variance (standard deviation) and semi-variance (semi-deviation). The downside systematic risk was measured with the modified Bawa-Lindenberg and Harlow-Rao formulas. These formulas were also implemented to determine the downside accounting betas. The empirical studies helped to identify a positive correlation between the mean quarterly profitability ratios (ROA and ROE) and the mean quarterly return rates in the largest NYSE companies. The correlations between the market and accounting betas calculated using the Bawa-Lindenberg modified formula were positive and statistically significant. A positive and statistically positive correlation between the market and accounting measures was also found for the total downside risk. Moreover, it has been shown that for both total and systematic market risk measures their accounting equivalents can be used. The accounting risk measures may be a valuable supplement to market measures. Of particular importance in this context are the downside accounting beta coefficients. The downside accounting betas are a new group of risk measures, and research into their use in risk analysis and in asset pricing is in its early stages.

Distributed sensemaking in network risk analysis.

This article responds to the call advancing risk science as an independent research field, by introducing a conceptual model for risk analysis based on distributed sensemaking. Significant advances in recent decades have expanded the use of risk analysis to almost every organization globally. Continued improvements have been made to our understanding of risk, placing a wide range of contexts under organizational control. This article argues that four dimensions are central in how organizations make sense of uncertainty in their context and hence do risk analysis: the activities the organization engages in, their sensory systems, the role and competence of individuals, and the ability to coordinate information through organizational structures. The structure enables insight into the decision-making process and the dimensions contributing to how organizations perceive risks and uncertainty in a given context. Three examples from the Arctic context illustrate the network risk analysis model's practical application and how it will expose weaknesses in these organizations' risk analysis and decision-making processes. Finally, the article discusses sensemaking in network risk analysis and how such an approach supports organizations' ability to perceive, collect, process, and decide on changes in context.

Exploring the Combined Risk of Sea Level Rise and Storm Surges Using a Bayesian Network Model: Application to Saemangeum Seawall

Jung, M.-K.; Kim, J.-Y.; Lee, B., and Kwon, H.-H., 2021. Exploring the combined risk of sea level rise and storm surges using a Bayesian network model: Application to Saemangeum seawall. In: Lee, J.L.; Suh, K.-S.; Lee, B.; Shin, S., and Lee, J. (eds.), Crisis and Integrated Management for Coastal and Marine Safety. Journal of Coastal Research, Special Issue No. 114, pp. 186–190. Coconut Creek (Florida), ISSN 0749-0208. In terms of natural hazards, typhoon-induced storm surge along with heavy rainfall has been recognized as the most frequently reported hazard among water-related disasters in the coastal areas in South Korea. Moreover, it has been widely acknowledged that the frequency and intensity of typhoons (or abnormal low-pressure systems) are likely to increase over time due to the potential impact of climate change. A risk analysis covering different loading conditions has been a tool for flood risk management in these contexts. We propose a Bayesian network-based generic risk analysis tool for flood defense systems such as a levee, dike, and seawall. On the other hand, this study explores various failure modes of flood defense structures for the use of risk analysis. In this study, the proposed modeling framework is applied to Saemangeum seawall in South Korea. Based on the literature review, various failure modes for Saemangeum seawall was identified, and the selected failure mode is then translated into nodes in the Bayesian network framework. The failure probability was estimated quantitatively by computing the limit state equation, which is composed of a set of random variables. Moreover, we investigated a Bayesian network model to assess the impact of compounding risk associated with revetment erosion of the seawall from sea-level rise informed by climate change scenarios. A further discussion on the role of the uncertainty for overall risk is provided.

Use of a prospective risk analysis method to improve the safety of healthcare workers in oncology hospitals during the COVID-19 spread among cancer patients

Abstract Background: Because of their vulnerability, cancer patients were exposed to viral infections and especially by the spread of the COVID-19, the coronavirus pandemic. According to published scientific data, the coronavirus was spread by sneezing, coughing, or the contact of the eyes, nose, and mouth through contaminated spaces. For these reasons, the oncology center's staff needs more protection and more precaution. A multidisciplinary hospital team was assembled to define critical failures linked to cancer patient process in hospital and proposed different actions to secure them and improve this process. Methods: According to a risk management tool (Failures Modes Effect and Criticality Analysis), the cancer patient process was mapped in many steps. During the videoconference brainstorming, many ideas were expressed and were classified into an Ishikawa diagram. The criticality indices were calculated from the severity, the occurrence, and the detection probability. Results: The cancer patient process has been described; 20 failures have been identified with 8029 points of the sum of critical indices. The new organization of the cancer patient process, the program of training of administrative staff and security agents of hospitals, wearing the protective resources were reduced to the criticality index of 6615 points, a mean of factor reduction of 1291, and the standard deviation was 5007.75. Conclusion: Modification of cancer patient process in oncology center resulted in an important risk reduction as shown by risk analysis. Our study illustrates the usefulness of risk analysis methods in the healthcare system. A systematic use of risk analysis was needed to improve the safety of high-risk activities in healthcare processes.

Choosing awareness over fear: Risk analysis and free trade support global food security

Livestock production and global trade are key components to achieving food security, but are bedfellows with the risk for emergence and spread of infectious diseases. The World Trade Organization's Agreement on the Application of Sanitary and Phytosanitary Measures outlines provisions for member countries to protect animal, plant, and public health while promoting free trade. The capacity for risk analysis equips countries to increase access to export markets, improve local animal health and food safety regarding known hazards, and build the institutional capacity to respond to unexpected events. The COVID-19 pandemic has highlighted the need to detect, report, and implement effective response measures to emerging challenges on a local and global scale, and it is crucial that these measures are implemented in a way that supports food production and trade. The use of risk analysis coupled with sound understanding of underlying system dynamics will contribute to resilient and enduring food systems.

Cost of Capital and Probability of Default in Value-Based Risk Management

Purpose – This paper aims to present the combination of enterprise risk management (ERM) and value-based management as especially suitable methods for companies with a shareholder value imperative. Among its major benefits, these methods make the contribution of risk management for business decisions more effective. Design/methodology/approach – Any possible inconsistencies between ERM, generating value because of imperfect capital markets and the CAPM to calculate cost of capital, which assumes perfect markets, must be avoided. Therefore, it is imperative that valuation methods used are based on risk analysis, and thus do not require perfect capital markets. Findings – Value-based risk management requires the impact of changes in risk on enterprise value to be calculated and the aggregation of opportunities and risks related to planning to calculate total risk (using Monte Carlo simulation) and valuation techniques that reflect the effects changes in risk, on probability of default, cost of capital and enterprise value (and do not assume perfect capital markets). It is recommended that all relevant risks should be quantified and described using adequate probability distributions derived from the best information. Practical implications – This approach can help to improve the use of risk analysis in decision-making by improving existing risk-management systems. Originality/value – This extension of ERM is outlined to provide risk-adequate evaluation methods for business decisions, using Monte Carlo simulation and recently developed methods for risk–fair valuation with incomplete replication in combination with the probability of default. It is shown that quantification of all risk using available information should be accepted for the linking of risk analysis and business decisions.

Cost of capital and probability of default in value-based risk management

PurposeThis paper aims to present the combination of enterprise risk management (ERM) and value-based management as especially suitable methods for companies with a shareholder value imperative. Among its major benefits, these methods make the contribution of risk management for business decisions more effective.Design/methodology/approachAny possible inconsistencies between ERM, generating value because of imperfect capital markets and the CAPM to calculate cost of capital, which assumes perfect markets, must be avoided. Therefore, it is imperative that valuation methods used are based on risk analysis, and thus do not require perfect capital markets.FindingsValue-based risk management requires the impact of changes in risk on enterprise value to be calculated and the aggregation of opportunities and risks related to planning to calculate total risk (using Monte Carlo simulation) and valuation techniques that reflect the effects changes in risk, on probability of default, cost of capital and enterprise value (and do not assume perfect capital markets). It is recommended that all relevant risks should be quantified and described using adequate probability distributions derived from the best information.Practical implicationsThis approach can help to improve the use of risk analysis in decision-making by improving existing risk-management systems.Originality/valueThis extension of ERM is outlined to provide risk-adequate evaluation methods for business decisions, using Monte Carlo simulation and recently developed methods for risk–fair valuation with incomplete replication in combination with the probability of default. It is shown that quantification of all risk using available information should be accepted for the linking of risk analysis and business decisions.

Improving the safety of chemotherapy process by a risks management tool

Abstract Objectives: Chemotherapy compounding is a main step of chemotherapy cancer process. This step is formed by many parts. A multidisciplinary team is assembled to define critical points and failures linked to this process and proposed different actions to secure them and improve chemotherapy cancer process. Methodology: By a prospective analysis risks tool: the failure modes, effects, and criticality analysis (FMECA), anticancer drug process compounding was sequenced in many parts. During the brainstorming, different ideas expressed and were classified into an Ishikawa cause–effect diagram. The criticality indexes (CI) are calculated from occurrence, severity, and the detection probability. Results: The sum of CIs of 18 identified failure modes was CI = 3607 for the decentralized system and CI = 726 after the new organization of compounding process. The chemotherapy production step represents 37.17% (CI = 1341) of all failures in the old process. The greatest risk reductions between the old and the new process concerned the risk of ‘Double check missing before delivery to the ward’ by a factor reduction of 28.0). Among the CIs remaining superior to 100, there was one failure: ‘Typing error during prescription’ (CI = 144). Conclusion: Modification of the chemotherapy-compounding process by centralization, training program, and implementation of procedures resulted in an important risk reduction as shown by risk analysis. Our study illustrates the usefulness of risk analysis methods in the healthcare system. A systematic use of risk analysis is needed to improve the safety of high-risk activities in healthcare processes.

Development of a HACCP-based approach to control risk factors associated with biscuit manufacturing plant, Bangladesh

PurposeThe purpose of this study is to develop a hazard analysis and critical control point (HACCP)-based approach to control risk factors associated with biscuit manufacturing plant.Design/methodology/approachMethodology was developed to achieve and implement the food safety and quality requirements. Procedure for hazard analysis and risk assessment was based on ISO 22000:2005 and codex standard. The HACCP Plan was summarized in comprehensive figures and tables. Biological, chemical and physical hazards that could exist in every step of biscuits production were identified. Moreover, the critical control points were selected and the critical limits, monitoring, corrective measures, records and verifications were established using risk analysis and decision tree.FindingsWith the implementation of HACCP system, it could be possible to reduce the food safety hazard and provide safe food and increase customer satisfaction. The critical control points were identified in sieving, sugar crushing, baking and detecting metal through the use of risk analysis and decision-making tree to control the biological, physical hazards along with monitoring through the control sheets. Critical limits, monitoring methods and frequency, responsibilities and corrective actions of the processes are also presented. This measuring instrument could be used by a food company as assessment tool and a benchmarking tool.Originality/valueApplying the HACCP system in food manufacturing can effectively assure food safety and quality, as well as expand the market and improve the manufacturers’ management level. In doing so, suitable strategies can be selected by a food company to allocate resources, increase HACCP effectiveness and improve its product safety.

Use of Risk Analysis for Water Security Assessment

This paper presents analysis of water security using risk analysis. It contributes to an enhanced understanding of the water security issue through creating indicators and their use in the determination of the water security level. The paper presents first the methodology followed in this research, which includes three steps: identification of water risk drivers, the determination of their severity and likelihood and finally the calculation of the water security score. The paper presents the first step of the application of this methodology to Palestinian territory through the identification of the drivers of the water risk and the levels of both the severity and likelihood to be used in future water security assessment.

RISK ANALYSIS: CHANGING THE QUARANTINE PARADIGM?

As modern zoos evolved from menageries, quarantine practices were developed to protect animal collections from the introduction of diseases that might arrive with new animals from wild populations and, more recently, from other managed populations. As the understanding of, and ability to diagnose, diseases grew tremendously in recent decades, quarantine protocols became more stringent and standardized as the tolerance for disease introduction decreased. These practices resulted in healthier animal collections, and the introduction of transmissible pathogens of concern became rarer. Sustainable population management in zoos has further lessened the reliance on taking animals from the wild, and the knowledge of disease risk in such situations has deepened. Today animals being transferred between modern zoological institutions have well-documented medical histories complemented with robust collection health histories. This improved understanding has reduced concerns about disease transmission in many cases. Consequently, more formal use of risk analysis to drive transfer-related examination, diagnostic testing, and quarantine decision making was adopted by a number of zoos and aquariums that have modified their quarantine practices and use risk analysis to reduce quarantine requirements, in some cases moving animals directly into collections after transportation. This commentary discusses the rationale for and changes to quarantine processes for animal acquisitions in zoos and aquariums, using risk analysis to direct decision making.

An Etiological Approach to Sexual Offender Assessment: CAse Formulation Incorporating Risk Assessment (CAFIRA).

Case formulations (CF) have been the cornerstone of effective practice in clinical psychology since the 1950s and now form one of the core competencies in clinical and forensic assessment. The use of CFs within forensic settings is becoming more relevant when working with offenders who have experienced significant trauma, suffered from personality disorder, and have displayed sexually abusive behavior. Furthermore, most North American and European jurisdictions insist that expert witnesses adopt an idiosyncratic approach to risk assessment and consider the characteristics of the individual as part of a wider formulation of the problem behavior. This article focuses specifically on CF incorporating risk assessment procedures of sexual offenders. While empirical support for the use of risk analysis and formulation in managing offending behavior generally, and sexual offending behavior in particular, is limited, there is mounting evidence to suggest that CF can improve understanding of an individual's problem sexual behaviors. We argue that by integrating risk formulations into the CF provides a conceptually robust link between the etiologically development of the problem sexual behavior and effective assessment and risk management of sexual offenders. As forensic treatment programs increasingly moved toward strength-based approaches, in keeping with the Risk-Need-Responsivity principles Andrews and Bonta (2004), and the Good Lives Model Ward and Stewart (Prof Psychol Res Pract 34:353-60, 2003) of offender rehabilitation, the use of CFs in the assessment, treatment, and management of sexual offenders is indispensable. We present an etiological framework for understanding risk in an individual sexual offender by integrating a case formulation model to include the use of (static, stable, and acute) actuarial and clinical risk assessment measures as well as protective risk factors, referred to as the CAse Formulation Incorporating Risk Assessment (CAFIRA) model.

Benchmark dose analysis framework for developing wildlife toxicity reference values.

The effects characterization phase of ecological risk assessments (ERAs) often includes the selection or development of toxicity reference values (TRVs) for chemicals under investigation. In wildlife risk assessments, TRVs are thresholds represented by a dose or concentration associated with a specified adverse response. Traditionally, a TRV may be derived from an estimate of the no-observed-adverse effect level or lowest-observed-adverse-effect level, identified from a controlled toxicity study. Because of the limitations of this approach, risk assessors are increasingly developing TRVs using alternative methods. Benchmark dose (BMD) analysis is widely recognized as one approach for developing TRVs. A BMD is derived using the full dose-response relationship from all experimental doses and may represent a user-specified response level (e.g., 5, 10, 20, or 50%). Although many regulatory programs consider the use of BMD-derived wildlife TRVs, there is limited guidance available for implementing the BMD approach, particularly for ERA. The present study provides a framework for ecological risk assessors to identify appropriate data, examine dose-response relationships, estimate BMDs, and document the results for use in risk analysis. This framework demonstrates the process of developing a TRV using BMD analysis and identifies applications for which this approach may enhance ERAs (e.g., site assessment, chemical or pesticide registration programs). Environ Toxicol Chem 2018;37:1496-1508. © 2018 SETAC.

Classification of operational risks in construction companies on the basis of big data

Nowadays, Big Data is commonly used in many business sectors. Its use is also relevant for the construction industry. One of the most promising areas of Big Data technologies application is their use for risk analysis and assessment. Big Data represents an efficient way to manage modern risks by analyzing the unlimited amount of structured and unstructured information. The study examines principles of operational risks classification in construction companies on the basis of Big Data technologies. The final goal of such classification is the creation of a solution pattern for subsequent use of Big Data. As an example, a solution pattern for such business problem as "Construction: Detection of Insurance Fraud" is created. Application of the Big Data analytics for fraud detection has a series of advantages as compared to traditional approaches. Insurance companies can build systems that include all relevant data sources. An analysis of operational risks by means of self-organizing Kohonen maps on the basis of the Deductor analytical platform is performed.

Evolution of seismic risk management for insurance over the past 30 years

During the past 30 years, there has been spectacular growth in the use of risk analysis and risk management tools developed by engineers in the financial and insurance sectors. The insurance, the reinsurance, and the investment banking sectors have enthusiastically adopted loss estimation tools developed by engineers in developing their business strategies and for managing their financial risks. As a result, insurance/reinsurance strategy has evolved as a major risk mitigation tool in managing catastrophe risk at the individual, corporate, and government level. This is particularly true in developed countries such as US, Western Europe, and Japan. Unfortunately, it has not received the needed attention in developing countries, where such a strategy for risk management is most needed. Fortunately, in the last five years, there has been excellent focus in developing “InsurTech” tools to address the much needed “Insurance for the Masses”, especially for the Asian Markets. In the earlier years of catastrophe model development, risk analysts were mainly concerned with risk reduction options through engineering strategies, and relatively little attention was given to financial and economic strategies. Such state-of-affairs still exists in many developing countries. The new developments in the science and technologies of loss estimation due to natural catastrophes have made it possible for financial sectors to model their business strategies such as peril and geographic diversification, premium calculations, reserve strategies, reinsurance contracts, and other underwriting tools. These developments have not only changed the way in which financial sectors assess and manage their risks, but have also changed the domain of opportunities for engineers and scientists. This paper will address the issues related to developing insurance/reinsurance strategies to mitigate catastrophe risks and describe the role catastrophe risk insurance and reinsurance has played in managing financial risk due to natural catastrophes. Historical losses and the share of those losses covered by insurance will be presented. How such risk sharing can help the nation share the burden of losses between tax paying public, the “at risk” property owners, the insurers and the reinsurers will be discussed. The paper will summarize the tools that are used by the insurance and reinsurance companies for estimating their future losses due to catastrophic natural events. The paper will also show how the results of loss estimation technologies developed by engineers are communicated to the business flow of insurance/reinsurance companies. Finally, to make it possible to grow “Insurance for the Masses–IFM”, the role played by parametric insurance products and InsurTech tools will be discussed.

Bayesian Methods for Quantifying and Reducing Uncertainty and Error in Forest Models

Forest models are tools for analysis and prediction of productivity and other services. Model outputs can only be useful if possible errors in inputs and model structure are recognized. However, errors cannot be quantified directly, making uncertainty inevitable. In this paper, we aim to clarify terminological confusion around the concepts of error and uncertainty and review current methods for addressing uncertainty in forest modelling. Modellers increasingly recognize that all uncertainties—in data, model inputs and model structure—can be represented using probability distributions. This has stimulated the use of Bayesian methods for quantifying and reducing uncertainty and error in models of forests and other vegetation. The Achilles’ heel of Bayesian methods has always been their computational demand, but solutions are being found. We conclude that future work will likely include (1) more use of Bayesian methods, (2) more use of hierarchical modelling, (3) replacement of model spin-up by Bayesian calibration, (4) more use of ensemble modelling and Bayesian model averaging, (5) new ways to account for model structural error in calibration, (6) better software for Bayesian calibration of complex models, (7) faster Markov chain Monte Carlo algorithms, (8) more use of model emulators, (9) novel uncertainty visualization techniques, (10) more use of graphical modelling and (11) more use of risk analysis.

Network model of risk analysis in the technical structures

The aim of the article is to present the possibilities offered by network models. This article discusses the network model of risk analysis in technical structures. We are discussed some network centrality measures and their use in risk analysis. We have proposed a mixed, expert-formal model for analyzing the risk of asset risk in technical structures based on own methodology of network building. We shown that mathematically model of structure of the network and its corresponding physical are linked by bijection. This creates a unequivocal model for quantitative risk analysis.

WE‐DE‐BRC‐04: Fault‐Tree Analysis

This SAMs Therapy Educational Course will introduce participants to the tools recommended by TG 100 for use in the development of a risk‐based Quality Management Program. The Course will start with an Overview of the background and rationale behind the TG 100 initiative, from which its charge was developed. After setting the scene, four 15 minute presentations will introduce the principal components of the soon to be published Report of TG 100. These are Process Mapping, Failure Modes and Effects Analysis, Fault Tree Analysis and the development of a QA/QM risk‐based Program. There will be time for two short exercises based on Failure Modes and Effects Analysis and a Discussion before the SAMs questions which will conclude the session. Learning Objectives: To appreciate the underlying philosophy of the TG 100 initiative. To gain a brief overview of the principal risk‐based tools recommended by TG 100. In an informal workshop format, to explore, at an introductory level, the use of risk analysis as proposed by TG 100 as a prelude to the development of a Quality Management Program.

WE‐DE‐BRC‐05: Risk‐Based QA/QM Programs

This SAMs Therapy Educational Course will introduce participants to the tools recommended by TG 100 for use in the development of a risk‐based Quality Management Program. The Course will start with an Overview of the background and rationale behind the TG 100 initiative, from which its charge was developed. After setting the scene, four 15 minute presentations will introduce the principal components of the soon to be published Report of TG 100. These are Process Mapping, Failure Modes and Effects Analysis, Fault Tree Analysis and the development of a QA/QM risk‐based Program. There will be time for two short exercises based on Failure Modes and Effects Analysis and a Discussion before the SAMs questions which will conclude the session. Learning Objectives: To appreciate the underlying philosophy of the TG 100 initiative. To gain a brief overview of the principal risk‐based tools recommended by TG 100. In an informal workshop format, to explore, at an introductory level, the use of risk analysis as proposed by TG 100 as a prelude to the development of a Quality Management Program.