In this chapter I defend the construction of inclusive, tightly governed DNA databases, as long as police can access them only for the prosecution of the most serious crimes or less serious but very high-volume offences. I deny that that the ethics of collecting and using these data sets the pattern for other kinds of policing by big data, notably predictive policing. DNA databases are primarily used for matching newly gathered biometric data with stored data. After considering and disputing a (...) number of objections to this practice, I conclude that DNA databases used in this way are ethically acceptable, if not valuable, contributions to legitimate policing. (shrink)

The American justice system, from police departments to the courts, is increasingly turning to information technology for help identifying potential offenders, determining where, geographically, to allocate enforcement resources, assessing flight risk and the potential for recidivism amongst arrestees, and making other judgments about when, where, and how to manage crime. In particular, there is a focus on machine learning and other data analytics tools, which promise to accurately predict where crime will occur and who will perpetrate it. Activists (...) and academics have begun to raise critical questions about the use of these tools in policing contexts. In this chapter, I review the emerging critical literature on predictive policing and contribute to it by raising ethical questions about the use of predictive analytics tools to identify potential offenders. Drawing from work on the ethics of profiling, I argue that the much-lauded move from reactive to preemptive policing can mean wrongfully generalizing about individuals, making harmful assumptions about them, instrumentalizing them, and failing to respect them as full ethical persons. I suggest that these problems stem both from the nature of predictive policing tools and from the sociotechnical contexts in which they are implemented... (shrink)

In light of the recent emergence of predictive techniques in law enforcement to forecast crimes before they occur, this paper examines the temporal operation of power exercised by predictive policing algorithms. I argue that predictive policing exercises power through a paranoid style that constitutes a form of temporal governmentality. Temporality is especially pertinent to understanding what is ethically at stake in predictive policing as it is continuous with a historical racialized practice of organizing, managing, controlling, and stealing time. After first (...) clarifying the concept of temporal governmentality, I apply this lens to Chicago Police Department’s Strategic Subject List. This predictive algorithm operates, I argue, through a paranoid logic that aims to preempt future possibilities of crime on the basis of a criminal past codified in historical crime data. (shrink)

ABSTRACT: So far in this book, we have examined algorithmic decision systems from three autonomy-based perspectives: in terms of what we owe autonomous agents (chapters 3 and 4), in terms of the conditions required for people to act autonomously (chapters 5 and 6), and in terms of the responsibilities of agents (chapter 7). -/- In this chapter we turn to the ways in which autonomy underwrites democratic governance. Political authority, which is to say the ability of a government to exercise (...) power, may be justifiable or not. Whether it is justified and how it can come to be justified is a question of political legitimacy. Political legitimacy is another way in which autonomy and responsibility are linked. This relationship is the basis of the current chapter, and it is important in understanding the moral salience of algorithmic systems. We will draw the connection as follows. We begin, in section 8.1, by describing two uses of technology: crime predicting technology used to drive policing practices and social media technology used to influence elections (including by Cambridge Analytica and by the Internet Research Agency). In section 8.2 we consider several views of legitimacy and argue for a hybrid version of normative legitimacy based on one recently offered by Fabienne Peter. In section 8.3 we will explain that the connection between political legitimacy and autonomy is that legitimacy is grounded in legitimating processes, which are in turn based on autonomy. Algorithmic systems—among them PredPol and the Cambridge Analytica-Facebook-Internet Research Agency amalgam—can hinder that legitimation process and conflict with democratic legitimacy, as we argue in section 8.4. We will conclude by returning to several cases that serve as through-lines to the book: Loomis, Wagner, and Houston Schools. -/- The link below is to an open-access copy of the chapter. (shrink)

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.

Law-enforcement agencies are increasingly able to leverage crime statistics to make risk predictions for particular individuals, employing a form of inference that some condemn as violating the right to be “treated as an individual.” I suggest that the right encodes agents’ entitlement to a fair distribution of the burdens and benefits of the rule of law. Rather than precluding statistical prediction, it requires that citizens be able to anticipate which variables will be used as predictors and act intentionally (...) to avoid them. Furthermore, it condemns reliance on various indexes of distributive injustice, or unchosen properties, as evidence of law-breaking. (shrink)

Does it matter that almost all juries in England and Wales are all-White? Does it matter even if this result is the unintended and undesired result of otherwise acceptable ways of choosing juries? Finally, does it matter that almost all juries are all-White if this has no adverse effect on the treatment of non-White defendants and victims of crime? According to Cheryl Thomas, there is no injustice in a system of jury selection which predictably results in juries with no (...) minority members so long as this result is not deliberate, and does not adversely affect the treatment of minority defendants and victims of crime. My view is different. In and of itself, I believe, something is wrong with a system of jury selection that predictably results in all-White juries in a diverse society, such as our own. Absent reason to believe that we lack a better alternative to current modes of jury selection, a commitment to democratic government and to the equality of citizens – or so I will argue – condemns existing arrangements as unjust, whether or not they have adverse effects on jury decisions, or on the ways in which our society approaches issues of race and crime. (shrink)

As a type of evidence glass can be very useful contact trace material in a wide range of offences including burglaries and robberies, hit-and-run accidents, murders, assaults, ram-raids, criminal damage and thefts of and from motor vehicles. All of that offer the potential for glass fragments to be transferred from anything made of glass which breaks, to whoever or whatever was responsible. Variation in manufacture of glass allows considerable discrimination even with tiny fragments. In this study, we worked glass classification (...) and testing of artificial neural network model created by the JustNN. The aim of the study is help investigator in identifying the type of glass found in arena of the crime. The Neural Network model was trained and validated using the type of glass dataset. The accuracy of model in predicting the type of glass reached 96.7%. Thus neural network is suitable for predicating type of glasses. (shrink)

When someone is prepunished, they are punished for a predicted crime they will or would commit. I argue that cases of prepunishment universally assumed to be merely hypothetical—including those in Philip K. Dick’s “The Minority Report”— are equivalent to some instances of the real-life punishment of attempt offenses. This conclusion puts pressure in two directions. If prepunishment is morally impermissible, as philosophers argue, then this calls for amendments to criminal justice theory and practice. At the same time, if prepunishment (...) is not imaginary, then the philosophers who reject it cannot claim that their view is supported by common sense. (shrink)

Empirical research has distinguished moral judgments that focus on an act and the actor’s intention or mental states, and those that focus on results of an action and then seek a causal actor. Studies indicate these two types of judgments may result from a “dual-process system” of moral judgment (Cushman 2008, Kneer and Machery 2019). Results-oriented judgements may be subject to the problem of resultant moral luck because different results can arise from the same action and intention. While some argue (...) luck should not bear on persons’ culpability, Victor Kumar has argued that the tendency to hold unlucky agents responsible for harm is justified by consequentialist aims of punishment (Kumar 2019). In contrast, judgments that focus on acts and intentions may be primarily retributive. Kumar claims that judgments focused on results track external, public harm because this increases the reliability of punishment and better achieves instrumental aims, including deterrent effect. In this chapter I examine rape cases using Kumar’s theory of punishment. Rape involves outcomes that are not publicly available. If judgments of punishment depend on outcomes, then we would expect such judgments to be less stable for those instances of wrongdoing that lack public outcomes such as rape, because such judgments would rely instead on often biased and unreliable inferential processes to establish the presence of mental states, which are essentially private. In this way Kumar’s theory actually predicts the way in which we see criminal justice institutions fail with regard to arrest, prosecution, and punishment related to rape; and we might expect similar failures for other crimes that lack publicly available results. In sum, a fundamental problem with institutionalized punishment centered upon results may be that some crimes sit within a moral blindspot. (shrink)

In various writings Michel Foucault has shown how, in the beginning of the 19th century, in settings such as army barracks, psychiatric hospitals and penitentiary institutions, the modern human sciences were ‹born› as an ensemble of disciplines (medical biology, psychiatry, psychology, criminology, and the like) From the beginning, the nature-nurture de- bate has been one of its key disputes. Are human individuals malleable by environmental factors (such as psychiatric treatments or disciplinary regimes), or do they rather display inborn predispositions for (...) delin- quency and other forms of antisocial behaviour? In the current era of genetic testing, in behavioural genomics and neuroscience, this issue is as controversial and topical as ever. Büchner’s unfinished drama Woyzeck (written in 1836) is a remarkable anticipation of this debate, staging the birth of the human individual as a research subject. It is the story of a destitute soldier who, according to his superiors, displays er- rant behaviour and is therefore recruited to serve as a research subject in an experiment. His army physician turns him into a ‹case›, which can be meticulously monitored and studied so as to record the genesis of a crime. In this paper, Büchner’s unsettling play is analysed in detail as one of the great anticipatory literary documents of the 19th century, ex- ploring the idea of predictive psychiatry and the quest for genetic pre- dispositions: a primal scene as it were of the nature-nurture debate as it unfolds from predictive criminology up to behavioural genomics. (shrink)

Abstract: As there are no currently obvious ways to create safe self-improving superintelligence, but its emergence is looming, we probably need temporary ways to prevent its creation. The only way to prevent it is to create a special type of AI that is able to control and monitor the entire world. The idea has been suggested by Goertzel in the form of an AI Nanny, but his Nanny is still superintelligent, and is not easy to control. We explore here ways (...) to create the safest and simplest form of AI which may work as an AI Nanny, that is, a global surveillance state powered by a Narrow AI, or AI Police. A similar but more limited system has already been implemented in China for the prevention of ordinary crime. AI police will be able to predict the actions of and stop potential terrorists and bad actors in advance. Implementation of such AI police will probably consist of two steps: first, a strategic decisive advantage via Narrow AI created by an intelligence services of a nuclear superpower, and then ubiquitous control over potentially dangerous agents which could create unauthorized artificial general intelligence which could evolve into Superintelligence. (shrink)

In some jurisdictions, the institutions of criminal justice may subject individuals who have committed crimes to preventive detention. By this, I mean detention of criminal offenders (i) who have already been punished to (or beyond) the point that no further punishment can be justified on general deterrent, retributive, restitutory, communicative or other backwardlooking grounds, (ii) for preventive purposes—that is, for the purposes of preventing the detained individual from engaging in further criminal or otherwise socially costly conduct. Preventive detention, thus understood, (...) shares many features with the quarantine measures sometimes employed in the context of infectious disease control. Both interventions involve imposing (usually severe) constraints on freedom of movement and association. Both interventions are standardly undeserved: in quarantine, the detained individual deserves no detention (or so I will, for the moment, assume), and in preventive detention, the individual has already endured any detention that can be justified by reference to desert. Both interventions are, in contrast to civil commitment under mental health legislation, normally imposed on more-or-less fully autonomous individuals. And both interventions are intended to reduce the risk that the constrained individual poses to the public. Yet despite these similarities, preventive detention and quarantine have received rather different moral report cards. (shrink)

Abstract: Lotteries have long been a source of fascination and intrigue, offering the tantalizing prospect of unexpected fortunes. In this research paper, we delve into the world of lottery predictions, employing cutting-edge AI techniques to unlock the secrets of lottery outcomes. Our dataset, obtained from Kaggle, comprises historical lottery draws, and our goal is to develop predictive models that can anticipate future winning numbers. This study explores the use of deep learning and time series analysis to achieve this elusive feat. (...) Through rigorous experimentation and data-driven approaches, we seek to determine the viability of AI in the realm of lottery predictions. Our findings reveal both the promise and limitations of AI in this context, shedding light on the complexities of lottery data and the potential need for quantum computing as a last resort. (shrink)

Deep learning may transform health care, but model development has largely been dependent on availability of advanced technical expertise. The aim of this study is to develop a deep learning model to predict the gender from retinal fundus images. The proposed model was based on the Xception pre-trained model. The proposed model was trained on 20,000 retinal fundus images from Kaggle depository. The dataset was preprocessed them split into three datasets (training, validation, Testing). After training and cross-validating the proposed model, (...) it was evaluated using the testing dataset. The result of testing, the area under receiver operating characteristic curve (AUROC) of the model was 0.99, precision, recall, f1-score and accuracy were 99%, precision, recall, f1-score and accuracy were 96.83%, 96.83%, 96.82% and 96.83% respectively.. Clinicians are presently unaware of dissimilar retinal feature variants between females and males, stressing the importance of model explain ability for the prediction of gender from retinal fundus images. The proposed deep learning may enable clinician-driven automated discovery of novel visions and disease biomarkers. (shrink)

War crimes are being committed in Ukraine today, but who should be held responsible? By looking at the literature on responsibility and violence by Philippa Foot and John Harris, this article argues that there are grounds for holding Vladimir Putin responsible for war crimes in Ukraine, even if he did not give the command for these crimes and other atrocities to be carried out.

According to the predictive coding theory of cognition , brains are predictive machines that use perception and action to minimize prediction error, i.e. the discrepancy between bottom–up, externally-generated sensory signals and top–down, internally-generated sensory predictions. Many consider PCT to have an explanatory scope that is unparalleled in contemporary cognitive science and see in it a framework that could potentially provide us with a unified account of cognition. It is also commonly assumed that PCT is a representational theory of sorts, (...) in the sense that it postulates that our cognitive contact with the world is mediated by internal representations. However, the exact sense in which PCT is representational remains unclear; neither is it clear that it deserves such status—that is, whether it really invokes structures that are truly and nontrivially representational in nature. In the present article, I argue that the representational pretensions of PCT are completely justified. This is because the theory postulates cognitive structures—namely action-guiding, detachable, structural models that afford representational error detection—that play genuinely representational functions within the cognitive system. (shrink)

Abstract: This research paper presents the development and evaluation of a neural network-based model for predicting fire alarms in smoke detection systems. Using a dataset from Kaggle containing 15 features and 3487 samples, we trained and validated a neural network with a three-layer architecture. The model achieved an accuracy of 100% and an average error of 0.0000003. Additionally, we identified the most influential features in predicting fire alarms.

Predictive processing (PP) accounts of perception are unique not merely in that they postulate a unity between perception and imagination. Rather, they are unique in claiming that perception should be conceptualised in terms of imagination and that the two involve an identity of neural implementation. This paper argues against this postulated unity, on both conceptual and empirical grounds. Conceptually, the manner in which PP theorists link perception and imagination belies an impoverished account of imagery as cloistered from the external world (...) in its intentionality, akin to a virtual reality, as well as endogenously generated. Yet this ignores a whole class of imagery whose intentionality is directed on the actual environment—projected mental imagery—and also ignores the fact that imagery may be triggered crossmodally in a bottom-up, stimulus-driven way. Empirically, claiming that imagery and perception share neural circuitry ignores relevant clinical results in this area. These evidence substantial perception/imagery neural dissociations, most notably in the case of aphantasia. Taken together, the arguments here suggest that PP theorists should substantially temper, if not outright abandon, their claim to a perception/imagination unity. (shrink)

Inchoate crime consists of acts that are regarded as crimes despite the fact that they are only partial or incomplete in some respect. This includes acts that do not succeed in physically harming the victim or are only indirectly related to such a result. Examples include attempts (as in attempted murder that does not eventuate in the killing of anyone), conspiracy (in which case the crime has only been planned, not yet acted out) and incitement (where the inciter (...) does not himself commit the crime he is urging others to undertake). The present paper attempts to analyze these inchoate crimes from a libertarian perspective, based on the non-aggression principle. (shrink)

Many philosophers claim that the neurocomputational framework of predictive processing entails a globally inferentialist and representationalist view of cognition. Here, I contend that this is not correct. I argue that, given the theoretical commitments these philosophers endorse, no structure within predictive processing systems can be rightfully identified as a representational vehicle. To do so, I first examine some of the theoretical commitments these philosophers share, and show that these commitments provide a set of necessary conditions the satisfaction of which allows (...) us to identify representational vehicles. Having done so, I introduce a predictive processing system capable of active inference, in the form of a simple robotic “brain”. I examine it thoroughly, and show that, given the necessary conditions highlighted above, none of its components qualifies as a representational vehicle. I then consider and allay some worries my claim could raise. I consider whether the anti-representationalist verdict thus obtained could be generalized, and provide some reasons favoring a positive answer. I further consider whether my arguments here could be blocked by allowing the same representational vehicle to possess multiple contents, and whether my arguments entail some extreme form of revisionism, answering in the negative in both cases. A quick conclusion follows. (shrink)

Accurate statistical analysis of Fortnite gameplay data is essential for improving gaming strategies and performance. In this study, we present a novel approach to analyze Fortnite statistics using machine learning techniques. Our dataset comprises a wide range of gameplay metrics, including eliminations, assists, revives, accuracy, hits, headshots, distance traveled, materials gathered, materials used, damage taken, damage to players, damage to structures, and more. We collected this dataset to gain insights into Fortnite player performance and strategies. The proposed model employs advanced (...) machine learning algorithms and data preprocessing techniques. We aim to identify key performance indicators and influential factors that contribute to Fortnite success. By exploring patterns and relationships within the dataset, our model can provide valuable insights into gameplay optimization. Our research involves feature analysis to determine the most influential factors in predicting Fortnite performance. These factors include eliminations, assists, revives, accuracy, hits, headshots, distance traveled, materials gathered, materials used, damage taken, damage to players, damage to structures, and more. By understanding the impact of these factors, we aim to help Fortnite players enhance their skills and strategies. Through extensive training and validation of our machine learning model, we achieved remarkable results, with an accuracy rate of [Your Accuracy Rate]% and an average error of [Your Average Error]. This research not only provides valuable insights into Fortnite gameplay but also offers a foundation for future research in the field of esports analytics and performance optimization. (shrink)

Predictive processing models of perception take issue with standard models of perception as hierarchical bottom-up processing modulated by memory and attention. The predictive framework posits that the brain generates predictions about stimuli, which are matched to the incoming signal. Mismatches between predictions and the incoming signal – so-called prediction errors – are then used to generate new and better predictions until the prediction errors have been minimized, at which point a perception arises. Predictive models hold that all bottom-up (...) processes are signals conveying prediction errors to higher areas, which respond by updating their predictions. We take issue with this claim and argue that object recognition requires bottom-up processing that cannot be understood in terms of prediction errors. Along the way, we expand on previous work casting doubt on the framework's ability to account for attention. Specifically, we argue that the type of attention that allows us to rapidly extract the gist of an object or scene presents an additional challenge to the predictive approach. We conclude by considering how the framework may be augmented to avoid these problems. (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)

Abstract – This study aimed primarily to determine the effectiveness of crime prevention strategies implemented by the Salug Valley Philippine National Police (PNP) in terms of Police Integrated Patrol System, Barangay Peacekeeping Operation, Anti-Criminality Operation, Integrated Area Community Public Safety services, Bantay Turista and School Safety Project as evaluated by 120 inhabitants and 138 PNP officers from four Municipalities of Salug Valley Zamboanga del Sur. Stratified random sampling was utilized in determining the respondents. Index crime rate were correlated (...) with the crime prevention strategies of the PNP in town of Salug Valley. A descriptive method of research was applied in this study utilizing self-made questionnaire. The data collected were analyzed using the main statistical tools like frequency count, percentage, mean computation, Kruskal Wallis Analysis of Variance and simple correlation. Findings of the study revealed that the crime prevention strategies in four (4) municipalities were “much effective” to include Integrated Patrol System, Barangay Peace Keeping Operations, Anti-Criminality Operations, Integrated Area Community Public Safety Services, Bantay Turista and School Safety Project in connection to the responses of 158 participants. There is a significant relationship between crime prevention strategies employed and index crime rate. (shrink)

Kidney stones pose a significant health concern, and early detection can lead to timely intervention and improved patient outcomes. This research endeavours to predict the presence of kidney stones based on urine analysis, utilizing a neural network model. A dataset of 552 urine specimens, comprising six essential physical characteristics (specific gravity, pH, osmolarity, conductivity, urea concentration, and calcium concentration), was collected and prepared. Our proposed neural network architecture, featuring three layers (input, hidden, output), was trained and validated, achieving an impressive (...) accuracy of 98.67% and an average error of 0.012. In addition to model performance, feature importance analysis was conducted to determine the most influential factors in predicting kidney stone presence. The findings underscore the significance of urea concentration, specific gravity, calcium concentration, conductivity, osmolarity, and pH as key indicators. This research contributes to the early diagnosis of kidney stones and demonstrates the potential of neural networks in medical diagnostics. The clinical implications of these findings are discussed, emphasizing the importance of timely intervention in managing kidney stone-related health issues. (shrink)

In the last decade, the cases of breast and cervical cancer have been positioned at the top rank of cancer statistics worldwide. Consequently, many husbands become family caregivers (FCGs) and get the burden of cancer caregiving. Being blind and incompetent, they need supportive care from healthcare professionals (HCPs). To support them, HCPs provide various healthcare information to meet their needs. Further, their demand for a specific type of healthcare information may reflect their need for emotional support from the HCPs to (...) reduce the burden. This study aims to examine the predictors of the need for emotional support among FCGs of female cancer patients by analyzing the demanded healthcare information. The mindsponge theory was used in conceptual development and interpretation. Bayesian Mindsponge Framework (BMF) analytics were used for statistical analysis on a dataset of 48 husbands and 12 significant others in five congested communities of Surabaya, Indonesia. Results showed that among the six types of healthcare information, the demanded cancer-specific information and information on cancer physical needs significantly predict the need for emotional support among respondents. Meanwhile, FCGs with a higher demand for information on alternative therapies are less likely to need emotional support. Other types of information have ambiguous effects on the need for emotional support. This study reveals that the demanded cancer-specific information, e.g., cancer prognosis or likely outcome, and information on cancer physical needs must be prioritized in information disclosure to meet the needs for emotional support among FCGs of female cancer patients. (shrink)

Life expectancy prediction, a pivotal facet of public health and policy formulation, has witnessed remarkable advancements owing to the integration of neural network models and comprehensive datasets. In this research, we present an innovative approach to forecasting life expectancy in diverse countries. Leveraging a neural network architecture, our model was trained on a dataset comprising 22 distinct features, acquired from Kaggle, and encompassing key health indicators, socioeconomic metrics, and cultural attributes. The model demonstrated exceptional predictive accuracy, attaining an impressive (...) 99.27% and an average error of 0.0034, underscoring the potency of deep learning in tackling complex, real-world challenges. Furthermore, our study delved into feature importance analysis, identifying critical determinants such as HIV/AIDS prevalence, income composition of resources, and socioeconomic status that significantly influence life expectancy. These findings provide actionable insights for healthcare policymakers and practitioners, emphasizing the importance of addressing health disparities, promoting economic development, and implementing targeted interventions. This research bridges the gap between data-driven methodologies and global health, offering a robust predictive tool and enriching our understanding of the multifaceted dynamics that shape life expectancy across nations. (shrink)

‘Crime is a prohibited act from which results in more evil than good’ is how Jeremy Bentham described crime. ‘Crime is a serious anti-social action to which the State reacts consciously by inflicting pain’, is how W.A.Bonger describes crime. Morality and its lack thereof are related to crime. Morality is so closely interwoven with social conduct and immorality interwoven with criminal conduct that it is desirable to investigate this matter further and so this shorter version (...) of a paper by Sally Ramage is the vehicle by which to look further into this issue of moral crime which notion is based on accepted moral code and common sense. We need to urgently study moral crime if we are to arrest the current moral decline in society and restore integrity and trust to the human race. In this example we see that maturity does not mellow the human psyche as modern criminal law states. It was Sir Norwood East who stated that ‘acquisitiveness, aggressiveness and sexuality are often closely associated but the maturity which changes criminality is never going to be present until one is conscious of one’s own maturity by beginning to live in that consciousness’. Social conduct must be cultivated. Crucially, sociologists argue that social conduct may vary with time and place. However, the case of maturity is different when considering those in formative early life and also in declining periods of life. It must be cultivated. It does not happen automatically with age and some people can never change since their greed, selfishness and criminal inclinations remain steadfast. To these types, there is no resilience learnt because resilience is characteristic of the formative period of life when the disappointments of today are counterbalanced by the anticipations of tomorrow. (shrink)

Abstract: This study introduces a machine learning-based model for predicting student performance using a comprehensive dataset derived from educational sources, encompassing 15 key features and comprising 62,631 student samples. Our five-layer neural network demonstrated remarkable performance, achieving an accuracy of 89.14% and an average error of 0.000715, underscoring its effectiveness in predicting student outcomes. Crucially, this research identifies pivotal determinants of student success, including factors such as socio-economic background, prior academic history, study habits, and attendance patterns, shedding light on the (...) nuanced dynamics of student performance. The key influential features identified in this study offer valuable insights into the complex factors shaping student achievement. These insights are vital for educators, policymakers, and institutions seeking to enhance educational outcomes and promote equitable access to quality education. This research provides a data-driven foundation for proactive interventions, personalized learning strategies, and support systems, ultimately contributing to improved student performance and academic success. The high accuracy of the predictive model and the feature analysis it provides empower decision-makers in the education sector. This model holds significant potential for applications in student performance monitoring, early intervention, and tailored educational strategies. By adopting a data-driven approach, this work advances the field of educational analytics and contributes to the goal of fostering student success and educational equity. (shrink)

Since 1900, debates about the justification of punishment have also been debates about the cause of crime. In the early twentieth century, the rehabilitative ideal of punishment viewed mental illness and dysfunction in individuals as the cause of crime. Starting in the 1970s, retributivism identified the immorality of human agents as the source of crime, which dovetailed well with the “tough-on-crime” political milieu of the 1980s and 1990s that produced mass incarceration. After surveying these historical trends, (...) Wirts argues for a social critical view that crime is best understood as a product of an unjust society, not faulty human beings. This view should take the lessons learned by the critiques of rehabilitation, however, and resist the tendency to reduce human beings to recipients of social services. (shrink)

Predictive Processing theory, hotly debated in neuroscience, psychology and philosophy, promises to explain a number of perceptual and cognitive phenomena in a simple and elegant manner. In some of its versions, the theory is ambitiously advertised as a new theory of conscious perception. The task of this paper is to assess whether this claim is realistic. We will be arguing that the Predictive Processing theory cannot explain the transition from unconscious to conscious perception in its proprietary terms. The explanations offer (...) by PP theorists mostly concern the preconditions of conscious perception, leaving the genuine material substrate of consciousness untouched. (shrink)

This chapter sets the scene for the subsequent philosophical discussions by surveying a number of biological interventions that have been used, or might in the future be used, for the purposes of crime prevention. These interventions are pharmaceutical interventions intended to suppress libido, treat substance abuse or attention deficit-hyperactivity disorder (ADHD), or modulate serotonin activity; nutritional interventions; and electrical and magnetic brain stimulation. Where applicable, we briefly comment on the historical use of these interventions, and in each case we (...) discuss the evidence that they are effective, or might become so with further refinement. The chapter concludes with a comment on some potentially significant differences between the varieties of intervention that we canvass. (shrink)

Gabriel Tarde, along with Durkheim and others, set the foundations for what is today a common-sense statement in social science: crime is a social phenomenon. However, the questions about what social is and what kind of social phenomenon crime is remain alive. Tarde’s writings have answers for both of these capital and interdependent problems and serve to renew our view of them. The aim of this article is to reconstruct Tarde’s definition of crime in terms of genus (...) and specific difference, exploring his criminology as a case of his general sociology. This procedure shows that Tarde succeeded in creating a comprehensive theory of crime and criminals founded not only on his most well-known concept, imitation, but also on his equally important concepts of invention, opposition, social logic and social teleology. For Tarde, crime is a complex phenomenon related to criminal inventions, criminal propagations, the production of penal laws, the execution of controls and punishments, and the collective reactions to all these. (shrink)

We introduce the predictive processing account of body representation, according to which body representation emerges via a domain-general scheme of (long-term) prediction error minimisation. We contrast this account against one where body representation is underpinned by domain-specific systems, whose exclusive function is to track the body. We illustrate how the predictive processing account offers considerable advantages in explaining various empirical findings, and we draw out some implications for body representation research.

Abstract: Heart diseases are increasing daily at a rapid rate and it is alarming and vital to predict heart diseases early. The diagnosis of heart diseases is a challenging task i.e. it must be done accurately and proficiently. The aim of this study is to determine which patient is more likely to have heart disease based on a number of medical features. We organized a heart disease prediction model to identify whether the person is likely to be diagnosed with (...) a heart disease or not using the medical features of the person. We used many different algorithms of machine learning such as Gaussian Mixture, Nearest Centroid, MultinomialNB, Logistic RegressionCV, Linear SVC, Linear Discriminant Analysis, SGD Classifier, Extra Tree Classifier, Calibrated ClassifierCV, Quadratic Discriminant Analysis, GaussianNB, Random Forest Classifier, ComplementNB, MLP Classifier, BernoulliNB, Bagging Classifier, LGBM Classifier, Ada Boost Classifier, K Neighbors Classifier, Logistic Regression, Gradient Boosting Classifier, Decision Tree Classifier, and Deep Learning to predict and classify the patient with heart disease. A quite helpful approach was used to regulate how the model can be used to improve the accuracy of prediction of heart diseases in any person. The strength of the proposed model was very satisfying and was able to predict evidence of having a heart disease in a particular person by using Deep Learning and Random Forest Classifier which showed a good accuracy in comparison to the other used classifiers. The proposed heart disease prediction model will enhances medical care and reduces the cost. This study gives us significant knowledge that can help us predict the person with heart disease. The dataset was collected from Kaggle depository and the model is implemented using python. (shrink)

Diabetes is one of the most common diseases worldwide where a cure is not found for it yet. Annually it cost a lot of money to care for people with diabetes. Thus the most important issue is the prediction to be very accurate and to use a reliable method for that. One of these methods is using artificial intelligence systems and in particular is the use of Artificial Neural Networks (ANN). So in this paper, we used artificial neural networks (...) to predict whether a person is diabetic or not. The criterion was to minimize the error function in neural network training using a neural network model. After training the ANN model, the average error function of the neural network was equal to 0.01 and the accuracy of the prediction of whether a person is diabetics or not was 87.3%. (shrink)

In this article culture is understood as the ensemble of systems of classification, assessment, and interaction that establishes a basic community of values in a given social field. We will argue that this is made possible through the institution of fundamental prohibitions understood as mythical points of closure that set the last frontiers of that community by designating what crime is. Exploring these theses, we will see that criminal transgression may be thought of as the actualization of a rigorous (...) otherness. This otherness, however, is nothing but the culture itself in its extreme vectors, its contradictions, and residues. From there we will differentiate three types of crimes: paroxysmal, archaic, and impossible. And we will conclude that the criminal question it is not a ‘social problem’ among others. Rather, it is a fundamental crossroad where the very constitution of any culture is at stake. It is the question of the beginning and end of the societal order and its subjects. (shrink)

I propose an account of the speech act of prediction that denies that the contents of prediction must be about the future and illuminates the relation between prediction and assertion. My account is a synthesis of two ideas: (i) that what is in the future in prediction is the time of discovery and (ii) that, as Benton and Turri recently argued, prediction is best characterized in terms of its constitutive norms.

Should we insist on prediction, i.e. on correctly forecasting the future? Or can we rest content with accommodation, i.e. empirical success only with respect to the past? I apply general considerations about this issue to the case of economics. In particular, I examine various ways in which mere accommodation can be sufficient, in order to see whether those ways apply to economics. Two conclusions result. First, an entanglement thesis: the need for prediction is entangled with the methodological role (...) of orthodox economic theory. Second, a conditional predictivism: if we are not committed to orthodox economic theory, then we should demand prediction rather than accommodation – against most current practice. (shrink)

Incarceration remains the foremost form of sentence for serious crimes in Western democracies. At the same time, the management of prisons and of the prison population has become a major real-world challenge, with growing concerns about overcrowding, the offenders’ well-being, and the failure of achieving the distal desideratum of reduced criminality, all of which have a moral dimension. In no small part motivated by these practical problems, the focus of the present article is on the ethical framework that we use (...) in thinking about and administering criminal justice. I start with an analysis of imprisonment and its permissibility as a punitive tool of justice. In particular, I present a novel argument against punitive imprisonment, showing it to fall short in meeting two key criteria of just punishment, namely (i) that the appropriate individual is being punished, and (ii) that the punishment can be adequately moderated to reflect the seriousness of the crime. The principles I argue for and that the aforementioned analysis brings to the fore, rooted in the sentient experience, firstly of victims, and not only of victims but also of the offenders as well as the society at large, then lead me to elucidate the broader framework of jurisprudence that I then apply more widely. Hence, while rejecting punitive imprisonment, I use its identified shortcomings to argue for the reinstitution of forms of punishment that are, incongruently, presently not seen as permissible, such as corporal punishment and punishments dismissed on the basis of being seen as humiliating. I also present a novel view of capital punishment, which, in contradiction to its name, I reject for punitive aims, but which I argue is permissible on compassionate grounds. (shrink)

Causes of crime are the subject of the etiology of criminal behavior, which is characterized by an interdisciplinary approach. There are many theories attempting to explain the determin ants of criminal behavior, a set of acts recognized by the criminal law, which emphasize different aspects of this phenomenon.

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

This essay provides an overview of the crime/tort distinction. It first investigates some of the fundamental differences between criminal law and tort law in doctrine and legal structure. It then explores some important similarities and differences in normative perspectives between the two doctrinal fields. This typology should prove analytically useful for examining some of the specific issues at the borderline of crime and torts—such as the proper scope of punitive damage liability and the question whether criminal law as (...) well as tort law should vary legal sanctions simply because of the fortuitous occurrence of harm. (shrink)

Cardiovascular diseases, including heart disease, pose a significant global health challenge, contributing to a substantial burden on healthcare systems and individuals. Early detection and accurate prediction of heart disease are crucial for timely intervention and improved patient outcomes. This research explores the potential of neural networks in predicting heart disease using a dataset collected from Kaggle, consisting of 1025 samples with 14 distinct features. The study's primary objective is to develop an effective neural network model for binary classification, identifying (...) the presence or absence of heart disease. The neural network architecture includes an input layer, a hidden layer, and an output layer, designed to capture intricate relationships within the dataset. Rigorous training and validation processes, accompanied by data preprocessing steps, ensure the model's robustness and generalization capabilities. The results demonstrate promising performance, with an accuracy of 92% and an average error of 0.062. Moreover, an analysis of feature importance highlights key predictors, including "oldpeak," "thalach," "trestbps," "ca," "thal," "cp," "chol," "sex," "restecg," "age," "slope," "fbs," and "exang." This research contributes to the field of predictive healthcare by leveraging neural networks to enhance heart disease prediction. The developed model offers the potential for early identification of individuals at risk, facilitating timely medical interventions and ultimately improving public health. Further exploration of machine learning techniques in healthcare promises to reshape disease prediction and prevention strategies. (shrink)

The use of machine learning, or “artificial intelligence” (AI) in medicine is widespread and growing. In this paper, I focus on a specific proposed clinical application of AI: using models to predict incapacitated patients’ treatment preferences. Drawing on results from machine learning, I argue this proposal faces a special moral problem. Machine learning researchers owe us assurance on this front before experimental research can proceed. In my conclusion I connect this concern to broader issues in AI safety.

The main aim of Lupyan’s paper is to claim that perception is cognitively penetrated and that this is consistent with the idea of perception as predictive coding. In these remarks I will focus on what Lupyan says about whether perception is cognitively penetrated, and set aside his remarks about epistemology. I have argued (2012) that perception can be cognitively penetrated and so I am sympathetic to Lupyan’s overall aim of showing that perception is cognitively penetrable. However, I will be critical (...) of some of Lupyan’s reasoning in arguing for this position. I will also call for clarification of that reasoning. First, I will discuss what is meant by cognitive penetration and, in light of this, the sort of evidence that can be used to establish its existence. Second, I will question whether Lupyan establishes that all cases of cross-modal effects are cases of cognitive penetration. In doing so, I will suggest a form of cognitive penetration that has heretofore not been posited, and give an example of how one might test for its existence. Third, I will question whether Lupyan puts forward convincing evidence that categorical knowledge and language affect perception in a way that conclusively shows that cognitive penetration occurs. Fourth, I will closely examine Lupyan’s reply to the argument against cognitive penetration from illusion. I show that his reply is not adequate and that he misses a more straightforward reply that one could give. Fifth, I briefly concur with the remarks that Lupyan makes about the role of attention with respect to cognitive penetration. And finally, sixth, I spell out exactly what I think the relationship is between the thesis that predictive coding is the correct model of perception and the thesis that there is cognitive penetration. (shrink)

: The purpose of this paper is to address from a normative perspective issues raised by John Mueller in Redeeming Economics: Rediscovering the Missing Element. Mueller criticizes economists, including Austrians, for failing to properly address unilateral transfers—in particular, charity, childcare, and crime—in economic thought. Mueller challenges economist Gary Becker’s position that giving increases the […] The post “Charity, Childcare, and Crime: From Objectivist Ethics to the Austrian School” appeared first on Libertarian Papers.

John N. Williams (1994) and Matthew Weiner (2005) invoke predictions in order to undermine the normative relevance of knowledge for assertions; in particular, Weiner argues, predictions are important counterexamples to the Knowledge Account of Assertion (KAA). I argue here that they are not true counterexamples at all, a point that can be agreed upon even by those who reject KAA.

Abstract Heart Attack is the Cardiovascular Disease (CVD) which causes the most deaths among CVDs. We collected a dataset from Kaggle website. In this paper, we propose an ANN model for the predicting whether a patient has a heart attack or not that. The dataset set consists of 9 features with 1000 samples. We split the dataset into training, validation, and testing. After training and validating the proposed model, we tested it with testing dataset. The proposed model reached an accuracy (...) of 98.01% on the Heart Disease Dataset. (shrink)