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AbstractIn 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.

Even though research has been showing that telework, under regular circumstances, could benefit the integration of work and family life, mandatory telework during the COVID-19 lockdown brought additional challenges, with potential to create conflicts between work and family spheres. Using regression analysis, this study examined the contribution of demographic and job-related variables to the prediction of work–family conflict among a sample of 213 workers who were involved in mandatory telework during the first lockdown due to the COVID-19 pandemic. The results revealed that role overload, after-hours work-related technology use, and low job autonomy accounted for the prediction of work–family conflict. Support from the supervisors and coworkers did not have an impact in easing the perception of work–family conflict but presented a moderation effect between after-hours work-related technology use and work–family conflict. Implications of the study for management practices related to telework, limitations of the study, and directions for future research are discussed.

AbstractBased on a data set of 91 papers and 22 industry studies, we analyse the impact of artificial intelligence on the insurance sector using Porter's (1985) value chain and Berliner's (1982) insurability criteria. Additionally, we present future research directions, from both the academic and practitioner points of view. The results illustrate that both cost efficiencies and new revenue streams can be realised, as the insurance business model will shift from loss compensation to loss prediction and prevention. Moreover, we identify two possible developments with respect to the insurability of risks. The first is that the application of artificial intelligence by insurance companies might allow for a more accurate prediction of loss probabilities, thus reducing one of the industry's most inherent problems, namely asymmetric information. The second development is that artificial intelligence might change the risk landscape significantly by transforming some risks from low-severity/high-frequency to high-severity/low-frequency. This requires insurance companies to rethink traditional insurance coverage and design adequate insurance products.

The data mining its main process is to collect, extract and store the valuable information and now-a-days it's done by many enterprises actively. In advanced analytics, Predictive analytics is the one of the branch which is mainly used to make predictions about future events which are unknown. Predictive analytics which uses various techniques from machine learning, statistics, data mining, modeling, and artificial intelligence for analyzing the current data and to make predictions about future. The two main objectives of predictive analytics are Regression and Classification. It is composed of various analytical and statistical techniques used for developing models which predicts the future occurrence, probabilities or events. Predictive analytics deals with both continuous changes and discontinuous changes. It provides a predictive score for each individual (healthcare patient, product SKU, customer, component, machine, or other organizational unit, etc.) to determine, or influence the organizational processes which pertain across huge numbers of individuals, like in fraud detection, manufacturing, credit risk assessment, marketing, and government operations including law enforcement.

As courts strive to simultaneously remain self-consistent and adapt to new legal challenges, a complex network of of citations between decided cases is established. Using network science methods to analyze the underlying patterns of citations between cases can help us understand the large-scale mechanisms which shape the judicial system. Here, we use the case-to-case citation structure of the Court of Justice of the European Union to examine this question. Using a link-prediction model, we show that over time the complex network of citations evolves in a way which improves our ability to predict new citations. Investigating the factors which enable prediction over time, we find that the content of the case documents plays a decreasing role, whereas both the predictive power and significance of the citation network structure itself show a consistent increase over time. Finally, our analysis enables us to validate existing citations and recommend potential citations for future cases within the court.

Forecasts and alternative scenarios of COVID-19 mortality have been critical inputs for pandemic response efforts, and decision-makers need information about predictive performance. We screen n = 386 public COVID-19 forecasting models, identifying n = 7 that are global in scope and provide public, date-versioned forecasts. We examine their predictive performance for mortality by weeks of extrapolation, world region, and estimation month. We additionally assess prediction of the timing of peak daily mortality. Globally, models released in October show a median absolute percent error (MAPE) of 7 to 13% at six weeks, reflecting surprisingly good performance despite the complexities of modelling human behavioural responses and government interventions. Median absolute error for peak timing increased from 8 days at one week of forecasting to 29 days at eight weeks and is similar for first and subsequent peaks. The framework and public codebase (https://github.com/pyliu47/covidcompare) can be used to compare predictions and evaluate predictive performance going forward.

As courts strive to simultaneously remain self-consistent and adapt to new legal challenges, a complex network of of citations between decided cases is established. Using network science methods to analyze the underlying patterns of citations between cases can help us understand the large-scale mechanisms which shape the judicial system. Here, we use the case-to-case citation structure of the Court of Justice of the European Union to examine this question. Using a link-prediction model, we show that over time the complex network of citations evolves in a way which improves our ability to predict new citations. Investigating the factors which enable prediction over time, we find that the content of the case documents plays a decreasing role, whereas both the predictive power and significance of the citation network structure itself show a consistent increase over time. Finally, our analysis enables us to validate existing citations and recommend potential citations for future cases within the court.

Poverty is one of the fundamental problems that is the center of attention of the government in a country. One of the important aspects to support the Poverty Reduction Strategy is the availability of accurate and targeted poverty data. Naïve Bayes is one method that can be used to classify data. The results of the classification carried out will later help aid managers to make decisions regarding the classification of determining the recipients of basic food assistance. There are two classes of predictions for the recipients of the basic food assistance, namely eligible and not eligible. The data used for prediction is sample data from XYZ village. In this research, the nave Bayes algorithm is implemented and analyzed using a web-based application. From the results of the evaluation using the confusion matrix, the resulting accuracy for 135 training data with 40 testing data and seven attributes used resulted in an accuracy of 86%, recall of 85%, and precision of 88%.

The coronavirus disease 2019 (COVID-19) pandemic has urged many governments in the world to enforce a strict lockdown where all nonessential businesses are closed and citizens are ordered to stay at home. One of the consequences of this policy is a significant change in electricity consumption patterns. Since load forecasting models rely on calendar or meteorological information and are trained on historical data, they fail to capture the significant break caused by the lockdown and have exhibited poor performances since the beginning of the pandemic. In this paper we introduce two methods to adapt generalized additive models, alleviating the aforementioned issue. Using Kalman filters and fine-tuning allows to adapt quickly to new electricity consumption patterns without requiring exogenous information. The proposed methods are applied to forecast the electricity demand during the French lockdown period, where they demonstrate their ability to significantly reduce prediction errors compared to traditional models. Finally, expert aggregation is used to leverage the specificities of each predictions and enhance results even further.

Prefabricated solutions incorporating thermal insulation are increasingly adopted as an energy conservation measure for building renovation. The InnoWEE European project developed three technologies from Construction and Demolition Waste (CDW) materials through a manufacturing process that supports the circular economy strategy of the European Union. Two of them consisted of geopolymer panels incorporated into an External Thermal Insulation Composite System (ETICS) and a ventilated façade. This study evaluates their thermal performance by means of monitoring data from three pilot case studies in Greece, Italy, and Romania, and calibrated building simulation models enabling the reliable prediction of energy savings in different climates and use scenarios. Results showed a reduction in energy demand for all demo buildings, with annual energy savings up to 25% after placing the novel insulation solutions. However, savings are highly dependent on weather conditions since the panels affect cooling and heating loads differently. Finally, a parametric assessment is performed to assess the impact of insulation thickness through an energy performance prediction and a cash flow analysis.

As courts strive to simultaneously remain self-consistent and adapt to new legal challenges, a complex network of of citations between decided cases is established. Using network science methods to analyze the underlying patterns of citations between cases can help us understand the large-scale mechanisms which shape the judicial system. Here, we use the case-to-case citation structure of the Court of Justice of the European Union to examine this question. Using a link-prediction model, we show that over time the complex network of citations evolves in a way which improves our ability to predict new citations. Investigating the factors which enable prediction over time, we find that the content of the case documents plays a decreasing role, whereas both the predictive power and significance of the citation network structure itself show a consistent increase over time. Finally, our analysis enables us to validate existing citations and recommend potential citations for future cases within the court.

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.

AbstractEvolutionary theory posits that grandmothers can increase their inclusive fitness by investing time and resources in their grandchildren. According on the X-linked grandmother hypothesis, the asymmetric inheritance of X-chromosomes should be responsible for the biased effect of the investment by maternal and paternal grandmothers towards granddaughters and grandsons. The British Millennium Cohort Study (n=4445 children) was used to investigate the association between grandmaternal childcare and children's injuries between the ages of 9 months and 3 years. Support was found for the X-linked grandmother hypothesis predicting that the investment of paternal grandmothers benefits more granddaughters than grandsons, the investment of paternal grandmothers benefits granddaughters more than the investment of maternal grandmothers, and the investment of maternal grandmothers is similarly associated with the injuries of granddaughters and grandsons. However, no support was found for the prediction that maternal grandmothers benefit more grandsons than paternal grandmothers. Thus, some, although not univocal, evidence for the prediction that X-chromosomal relatedness shapes the grandmaternal effect on child outcomes was found.

Transaction costs strongly influence diversification dynamics, as predicted by resource theory. A mainstream view links the profitability of diversification with the existence of transaction costs that prevent a firm from trading in fungible, scale-free resources. This study applies a neo-Penrosian perspective to transaction costs, with the notion that diversification may be driven by the redeployment of non-scale-free resources. An empirical analysis, using tax changes in the drink sector as a measure of exogenous demand variation, offers results consistent with the prediction that redeployment is particularly relevant when retailing is concentrated and single-product competition within a focal product niche (e.g., beer) is fragmented. This study also measures redeployment across a portfolio of a multiniche firm when changes in its sales-growth rates for a particular product niche might imply contrasting changes in other product niches. The resulting evidence is consistent with predictions that demand uncertainty, and transaction costs create viable redeployment opportunities for multiniche companies.

Abstract
This paper studies how an introduction of paid parental leave (PPL) affects maternal labor market outcomes in the short run. Using a reform in Australia, the PPL scheme, that gave the primary caregiver of a child born or adopted on or after January 1 2011, $672.70 a week for a maximum of 18 weeks, this paper develops theoretical predictions of the effect of PPL on maternal labor market outcomes, and tests these predictions using confidential data from the Australian Pregnancy and Employment Transitions Survey. The theoretical results imply that after the introduction of PPL, hours of work in the pre-birth period should decrease for mothers who will qualify for PPL, and increase for mothers who are attempting to qualify for PPL. Post birth, the theoretical results imply that more mothers are out of work and on leave than would have been in the absence of PPL. The empirical results suggest that the PPL scheme had no significant effect on labor market outcomes pre birth or post birth.