The book lays out a comprehensive professional ethics for criminal intelligence professionals, focusing on personal responsibility and the moral obligation by the state to build sufficient capacity in criminal intelligence operatives to make competent moral decisions while using discretion.

This is a guide for building specific ethics standards for the criminal intelligence service which can be used for in-house arbitration, facilitation or adjudication of ethics issues.

Criminal intelligence data poses problems for conventional database technology. It has little structure or homogeneity and queries may involve looking for unknown associations between entities; such open-ended queries cannot be made in current systems. Finally, the data must be presented in an intuitively simple fashion for both investigative and evidential purposes. We discuss a database system which uses a labelled graph as its data model. This approach obviates the need for schema design, allows queries which look for associations (...) between entities to be implemented and provides the basis for a natural visual representation of the data. (shrink)

Artificial intelligence is increasingly permeating many types of high-stake societal decision-making such as the work at the criminal courts. Various types of algorithmic tools have already been introduced into sentencing. This article concerns the use of algorithms designed to deliver sentence recommendations. More precisely, it is considered how one should determine whether one type of sentencing algorithm (e.g., a model based on machine learning) would be ethically preferable to another type of sentencing algorithm (e.g., a model based on (...) old-fashioned programming). Whether the implementation of sentencing algorithms is ethically desirable obviously depends upon various questions. For instance, some of the traditional issues that have received considerable attention are algorithmic biases and lack of transparency. However, the purpose of this article is to direct attention to a further challenge that has not yet been considered in the discussion of sentencing algorithms. That is, even if is assumed that the traditional challenges concerning biases, transparency, and cost-efficiency have all been solved or proven insubstantial, there will be a further serious challenge associated with the comparison of sentencing algorithms; namely, that we do not yet possess an ethically plausible and applicable criterion for assessing how well sentencing algorithms are performing. (shrink)

In this contribution we will explore some of the implications of the vision of Ambient Intelligence (AmI) for law and legal philosophy. AmI creates an environment that monitors and anticipates human behaviour with the aim of customised adaptation of the environment to a person’s inferred preferences. Such an environment depends on distributed human and non-human intelligence that raises a host of unsettling questions around causality, subjectivity, agency and (criminal) liability. After discussing the vision of AmI we will (...) present relevant research in the field of philosophy of technology, inspired by the post-phenomenological position taken by Don Ihde and the constructivist realism of Bruno Latour. We will posit the need to conceptualise technological normativity in comparison with legal normativity, claiming that this is necessary to develop democratic accountability for the implications of emerging technologies like AmI. Lastly we will investigate to what extent technological devices and infrastructures can and should be used to achieve compliance with the criminal law, and we will discuss some of the implications of non-human distributed intelligence for criminal liability. (shrink)

It has recently been suggested that philosophy – in particular epistemology – has a contribution to make to the analysis of criminal and military intelligence. The present article pursues this suggestion, taking three phenomena that have recently been studied by philosophers, and showing that they have important implications for the gathering and sharing of intelligence, and for the use of intelligence in the determining of military strategy. The phenomena discussed are: (1) Simpson's Paradox, (2) the distinction (...) between resiliency and reliability of data, and (3) the Causal Markov Condition. (shrink)

The topic of the impact of artificial intelligence on law, the legal profession and legal culture, in general, has not yet been sufficiently discussed in the Croatian scientific community. This paper aims to encourage a more comprehensive analysis of the relationship between the increasing use of artificial intelligence in our daily lives and the specifics of practising the legal profession in such an environment. Artificial intelligence is still a broad and heterogeneous field. It is therefore justified to (...) ask the question: Which of the many technologies currently being developed can be applied in the field of law? Which legal matters can be “entrusted” to the new technologies? To answer such and similar questions, it is necessary to have an excellent knowledge of both artificial intelligence and the law, as well as the areas in which they intersect. We have chosen criminal law, which is a part of public law and the ultimate social mechanism for responding to undesirable behaviour, as our object of study. We pay particular attention to its two important aspects: the element of culpability in a criminal offence and the possibility of autonomous decision-making in criminal law. The reflections have shown that Croatian criminal law may have the ability and potential to adapt to the challenges of artificial intelligence when it comes to culpability. On the other hand, the necessity and value of artificially intelligent decisions are questionable given the fundamental characteristics of criminal law. In conclusion, the authors express a departure from views of artificial intelligence as a decision-making substitute in criminal law but support the view that artificially intelligent legal technologies are an aid to decision-making. (shrink)

It is often thought that traditional recidivism prediction tools used in criminal sentencing, though biased in many ways, can straightforwardly avoid one particularly pernicious type of bias: direct racial discrimination. They can avoid this by excluding race from the list of variables employed to predict recidivism. A similar approach could be taken to the design of newer, machine learning-based (ML) tools for predicting recidivism: information about race could be withheld from the ML tool during its training phase, ensuring that (...) the resulting predictive model does not use race as an explicit predictor. However, if race is correlated with measured recidivism in the training data, the ML tool may ‘learn’ a perfect proxy for race. If such a proxy is found, the exclusion of race would do nothing to weaken the correlation between risk (mis)classifications and race. Is this a problem? We argue that, on some explanations of the wrongness of discrimination, it is. On these explanations, the use of an ML tool that perfectly proxies race would (likely) be more wrong than the use of a traditional tool that imperfectly proxies race. Indeed, on some views, use of a perfect proxy for race is plausibly as wrong as explicit racial profiling. We end by drawing out four implications of our arguments. (shrink)

Purpose The purpose of this study is to examine public perceptions and attitudes toward using artificial intelligence (AI) in the US criminal justice system. Design/methodology/approach The authors took a quantitative approach and administered an online survey using the Amazon Mechanical Turk platform. The instrument was developed by integrating prior literature to create multiple scales for measuring public perceptions and attitudes. Findings The findings suggest that despite the various attempts, there are still significant perceptions of sociodemographic bias in the (...) criminal justice system and technology alone cannot alleviate them. However, AI can assist judges in making fairer and more objective decisions by using triangulation – offering additional data points to offset individual biases. Social implications Other scholars can build upon the findings and extend the work to shed more light on some problems of growing concern for society – bias and inequality in criminal sentencing. AI can be a valuable tool to assist judges in the decision-making process by offering diverse viewpoints. Furthermore, the authors bridge the gap between the fields of technology and criminal justice and demonstrate how the two can be successfully integrated for the benefit of society. Originality/value To the best of the authors’ knowledge, this is among the first studies to examine a complex societal problem like the introduction of technology in a high-stakes environment – the US criminal justice system. Understanding how AI is perceived by society is necessary to develop more transparent and unbiased algorithms for assisting judges in making fair and equitable sentencing decisions. In addition, the authors developed and validated a new scale that can be used to further examine this novel approach to criminal sentencing in the future. (shrink)

Traditional notions of crime control often position the police against an individual, known or not yet known, who is responsible for the commission of a crime. However, with increasingly sophisticated technology, policing increasingly prioritizes the prevention of crime, making it necessary to ascertain who, or what class of persons, may be the next likely criminal before a crime can be committed, termed predictive policing. This causes a shift from individualized suspicion toward predictive profiling that may sway the expectations of (...) a police patrol. Classically, where a patrol officer forms reasonable suspicion prior to a stop, it is based upon his/her analysis of the situation taken as a whole in context. However, where a predictive profile is employed, information available to the officer accordingly adjusts his/her perception of context and affects the application of the reasonable suspicion standard. This article addresses the way in which new approaches to forming reasonable suspicion affect the due process protection of individuals’ fundamental rights. It argues that while an officer still operates with good faith discretion, using predictive profiling causes reasonable suspicion to be based on an augmented understanding of reality and as a result, due process guarantees are weakened. The rights to non-discrimination and the presumption of innocence are assessed and argued as illustrative of this weakening and shift in policing standards. The article ultimately argues that while predictive policing cannot be categorically labeled as inconsistent with criminal justice, changes as seemingly moderate as the manner in which discretion operates have larger effects on individuals’ rights. (shrink)

Artificial Intelligence research and regulation seek to balance the benefits of innovation against any potential harms and disruption. However, one unintended consequence of the recent surge in AI research is the potential re-orientation of AI technologies to facilitate criminal acts, term in this chapter AI-Crime. AIC is theoretically feasible thanks to published experiments in automating fraud targeted at social media users, as well as demonstrations of AI-driven manipulation of simulated markets. However, because AIC is still a relatively young (...) and inherently interdisciplinary area—spanning socio-legal studies to formal science—there is little certainty of what an AIC future might look like. This chapter offers the first systematic, interdisciplinary literature analysis of the foreseeable threats of AIC, providing ethicists, policy-makers, and law enforcement organisations with a synthesis of the current problems, and a possible solution space. (shrink)

In this article, I ask whether the state, as opposed to its individual members, can intelligibly and legitimately be criminalized, with a focus on the possibility of its domestic criminalization. I proceed by identifying what I take to be the core objections to such criminalization, and then investigate ways in which they can be challenged. First, I address the claim that the state is not a kind of entity that can intelligibly perpetrate domestic criminal wrongs. I argue against it (...) by building upon an account of the modern state as a moral agent proper, capable of both culpable moral and legal wrongdoing. I then consider objections to the intelligibility and legitimacy of subjecting states to domestic criminal processes, which primarily find their source in the assumption that such subjection would necessarily involve the state prosecuting, judging, and punishing itself. I argue that whether this (questionable) assumption is sound or not, it does not create the kinds of unsolvable quandaries its exponents think it does. I then move on to reject the distinct, yet related, objection that, at least in aspiring liberal jurisdictions, treating the state as a criminal objectionably involves extending to it various substantive and procedural guarantees that, given its nature and raison d’être, it should not have. Finally, I discuss three central objections to punishing the state. First, that organizations like states do not have the phenomenal consciousness required to suffer punishment. Second, that the constant possibility of dispersion of state punishment amongst individual members stands in the way of its justification. Lastly, that whatever justification there may be for making things harder for the state in response to its culpable wrongdoing, such treatment need not be understood as punishment. While partially conceding the strength of these objections, I strive to loosen their grip in ways that show that justified punishment of the state, meaningfully understood as such, remains a distinct possibility. I conclude by contrasting supposed alternatives to the criminalization of states, and by contending that my analysis leaves us with enough to keep the possibility of state criminalization on the table as a justifiable response to state wrongdoing. (shrink)

In the digital age, the use of advanced technology is becoming a new paradigm in police work, criminal justice, and the penal system. Algorithms promise to predict delinquent behaviour, identify potentially dangerous persons, and support crime investigation. Algorithm-based applications are often deployed in this context, laying the groundwork for a ‘smart criminal justice’. In this qualitative study based on 32 interviews with criminal justice and police officials, we explore the reasons why and extent to which such a (...) smart criminal justice system has already been established in Switzerland, and the benefits perceived by users. Drawing upon this research, we address the spread, application, technical background, institutional implementation, and psychological aspects of the use of algorithms in the criminal justice system. We find that the Swiss criminal justice system is already significantly shaped by algorithms, a change motivated by political expectations and demands for efficiency. Until now, algorithms have only been used at a low level of automation and technical complexity and the levels of benefit perceived vary. This study also identifies the need for critical evaluation and research-based optimization of the implementation of advanced technology. Societal implications, as well as the legal foundations of the use of algorithms, are often insufficiently taken into account. By discussing the main challenges to and issues with algorithm use in this field, this work lays the foundation for further research and debate regarding how to guarantee that ‘smart’ criminal justice is actually carried out smartly. (shrink)

Artificial intelligence research and regulation seek to balance the benefits of innovation against any potential harms and disruption. However, one unintended consequence of the recent surge in AI research is the potential re-orientation of AI technologies to facilitate criminal acts, term in this article AI-Crime. AIC is theoretically feasible thanks to published experiments in automating fraud targeted at social media users, as well as demonstrations of AI-driven manipulation of simulated markets. However, because AIC is still a relatively young (...) and inherently interdisciplinary area—spanning socio-legal studies to formal science—there is little certainty of what an AIC future might look like. This article offers the first systematic, interdisciplinary literature analysis of the foreseeable threats of AIC, providing ethicists, policy-makers, and law enforcement organisations with a synthesis of the current problems, and a possible solution space. (shrink)

This paper considers the problem of how to evaluate an offender’s criminal record. This evaluation is part of the sentencing process carried out by a judge, and may be complicated in the case of offenders with a heavy record. We give a comprehensive overview of the approach to an offender’s past record in various (Western) countries, considering the two major approaches: desert-based and utilitarian. The paper describes the determination of the parameters involved in the evaluation, and the construction of (...) a decision support system to be used by a judge about to pass sentence in a criminal case. The system may be used as a stand-alone system, but can also be integrated as a component of a general sentencing support system, e.g. a statistical sentencing information system. The system is a knowledge-based system. The knowledge base (in rule-form) was created primarily by eliciting knowledge from experts (judges, lawyers, academics and probation officers), but also by applying statutory law, case law and legal authoritative texts. It is flexible in the sense, that a user can introduce his1 own preferences (expressed as numerical coefficients) and thereby apply different sentencing principles. The prototype is at present undergoing testing by some of the participating judges. (shrink)

Criminal justice systems have traditionally relied heavily on human decision-making, but new technologies are increasingly supplementing the human role in this sector. This paper considers what general limits need to be placed on the use of algorithms in sentencing decisions. It argues that, even once we can build algorithms that equal human decision-making capacities, strict constraints need to be placed on how they are designed and developed. The act of condemnation is a valuable element of criminal sentencing, and (...) using algorithms in sentencing – even in an advisory role – threatens to undermine this value. The paper argues that a principle of “meaningful public control” should be met in all sentencing decisions if they are to retain their condemnatory status. This principle requires that agents who have standing to act on behalf of the wider political community retain moral responsibility for all sentencing decisions. While this principle does not rule out the use of algorithms, it does require limits on how they are constructed. (shrink)

This Article defines four distinct conceptions of cruelty found in underdeveloped form in domestic and international criminal law sources. The definition is analytical, focusing on the types of agency, victimization, causality, and values in each conception of cruelty. But no definition of cruelty will do justice to its object until complemented by the kind of understanding practical reason provides of the implications of the phenomenon of cruelty. -/- No one should be neutral in relation to cruelty. Eminently, cruelty in (...) criminal law, a human-created phenomenon, vigorously calls for responses in the form of preventive and corrective action on the part of private and public actors. It is in this sense that cruelty is a problem of practical reason, one of action preoccupied with its legal or moral obligations, rational grounds, value commitments, and actual consequences. -/- However, the connection between conceptions of cruelty and the implications practical reason can draw from the correct application of a conception of cruelty to phenomena in the world remains too detached to be able to capture and explain people's actual experiences both of seeing cruelty in the world and of confronting the question of what to do about cruelty and how to address its cultural and institutional aspects. Something is missing. -/- What is missing is the integration of conceptions of cruelty and the practical reason implications of detecting cruelty in the world into normative models which operate as meaning matrixes for cognition, meaning, and action. This Article undertakes this explanatory task through an exercise of reconstructing seminal philosophical ideas about cruelty. The result is that the four conceptions of cruelty are placed within three distinct normative models, which ultimately render intelligible legal conceptions of cruelty and legal reactions to it. (shrink)

Modeling legal argumentation is one of the most important research in AI and Law, and a lot of models have been proposed. However, most research has not treated value judgement and debate. In this paper, we introduce a legal reasoning model which covers various aspects of legalreasoning such as making argument, selecting argument and debate.Furthermore, we present how criminal law is described and reasoned inthis model.

With wit and intelligence, Leo Katz seeks to understand the basic rules and concepts underlying the moral, linguistic, and psychological puzzles that plague the criminal law. "_Bad Acts and Guilty Minds_... revives the mind, it challenges superficial analyses, it reminds us that underlying the vast body of statutory and case law, there is a rationale founded in basic notions of fairness and reason.... It will help lawyers to better serve their clients and the society that permits attorneys to (...) hang out their shingles."—Edward N. Costikyan, _New York Times Book Review_. (shrink)

Recent events have highlighted large-scale systemic racial disparities in U.S. criminal justice based on race and other demographic characteristics. Although criminological datasets are used to study and document the extent of such disparities, they often lack key information, including arrestees’ racial identification. As AI technologies are increasingly used by criminal justice agencies to make predictions about outcomes in bail, policing, and other decision-making, a growing literature suggests that the current implementation of these systems may perpetuate racial inequalities. In (...) this paper, we argue that AI technologies should be investigated to understand how they recognize racial categories and whether they can be harnessed to fill missing race data. By bridging this gap, we can work towards better understanding racial inequalities in a wide range of contexts, most notably criminal justice. Using a multidisciplinary perspective, we rethink the design and methodology used in facial processing technology (FPT) based on supervised deep learning model (DLM) image classification. By modifying standard FPT pipelines to tackle multiple sources of DLM bias, we propose an experimental methodology based on ethical AI principles to generate binary (Black and White) racial categories using mugshots. We go beyond simply reporting DLM accuracies and address fundamental issues such as generalizability and interpretability by using a “self-auditing” approach. First, we evaluate the inference performances of 42 fine-tuned DLMs using unseen test images from the same dataset but subject to varying data augmentations. Next, to interpret and validate our methodological approach, we apply gradient-based saliency maps to assess the consistency of facial region relevance and attribution. Finally, drawing upon insights from three areas (computer science, sociology, and law), we investigate the efficacy of our DLM-based method as a tool for detecting racial inequalities in criminal justice. (shrink)

Is it morally acceptable to use artificial intelligence (AI) in the form of computer-driven algorithms in the determination of sentences on those who have broken the law? If so, how should such algorithms be used? This book is the first collective work devoted exclusively to the ethical and penal theoretical considerations of the use of AI at sentencing. It deals with a wide range of highly pertinent issues, such as the following: Should algorithmic-based decision-making be transparent? If so, what (...) does this mean and why is it important? Does a conflict between algorithmic transparency and algorithmic accuracy always constitute a moral dilemma? What does it mean to require that algorithmic sentencing decisions should be fair? When should an algorithm be regarded as discriminatory? Is it acceptable to use algorithms to produce risk assessments of future criminal conduct? Can algorithms ever be used in the exercise of mercy? Should AI only be used to guide judicial decision-making or could it ever be justified to replace a human sentencing judge with a robojudge? (shrink)

The present article proceeds from the mainstream view that the conceptual framework underpinning adversarial systems of criminal adjudication, i.e. a mixture of common-sense philosophy and probabilistic analysis, is unsustainable. In order to provide fact-finders with an operable structure of justification, we need to turn to epistemology once again. The article proceeds in three parts. First, I examine the structural features of justification and how various theories have attempted to overcome Agrippa’s trilemma. Second, I put Inferential Contextualism to the test (...) and show that a defeasible structure of justification allocating epistemic rights and duties to all participants of an inquiry manages to dissolve the problem of scepticism. Third, I show that our epistemic practice already embodies a contextualist mechanism. Our problem was not that our Standard of Proof is inoperable but that it was not adequately conceptualized. Contextualism provides the framework to articulate the abovementioned practice and to treat ‘reasonable doubts’ as a mechanism which we can now describe in detail. The seemingly insurmountable problem with our efforts to define the concept “reasonable doubts” was the fact that we have been conflating the surface features of this mechanism and its internal structure, i.e. the rules for its use. (shrink)

Whether causing flash crashes in financial markets, purchasing illegal drugs, or running over pedestrians, AI is increasingly engaging in activity that would be criminal for a natural person, or even an artificial person like a corporation. We argue that criminal law falls short in cases where an AI causes certain types of harm and there are no practically or legally identifiable upstream criminal actors. This Article explores potential solutions to this problem, focusing on holding AI directly criminally (...) liable where it is acting autonomously and irreducibly. Conventional wisdom holds that punishing AI is incongruous with basic criminal law principles such as the capacity for culpability and the requirement of a guilty mind. -/- Drawing on analogies to corporate and strict criminal liability, as well as familiar imputation principles, we show how a coherent theoretical case can be constructed for AI punishment. AI punishment could result in general deterrence and expressive benefits, and it need not run afoul of negative limitations such as punishing in excess of culpability. Ultimately, however, punishing AI is not justified, because it might entail significant costs and it would certainly require radical legal changes. Modest changes to existing criminal laws that target persons, together with potentially expanded civil liability, are a better solution to AI crime. (shrink)

Automated decision making for sentencing is the use of a software algorithm to analyse a convicted offender’s case and deliver a sentence. This chapter reviews the moral arguments for and against employing automated decision making for sentencing and finds that its use is in principle morally permissible. Specifically, it argues that well-designed automated decision making for sentencing will better approximate the just sentence than human sentencers. Moreover, it dismisses common concerns about transparency, privacy and bias as unpersuasive or inapplicable. The (...) chapter also notes that moral disagreement about theories of just sentencing are plausibly resolved by applying the principle of maximising expected moral choiceworthiness, and that automated decision making is better suited to the resulting ensemble model. Finally, the chapter considers the challenge posed by penal populism. The dispiriting conclusion is that although it is in theory morally desirable to use automated decision-making for criminal sentencing, it may well be the case that we ought not to try. (shrink)

The general mitigating and aggravating circumstances of criminal liability are elements attached to the crime that, when they occur, affect the punishment quantum. Cuban criminal legislation provides a catalog of such circumstances and some general conditions for their application. Such norms give judges broad discretion in assessing circumstances and adjusting punishment based on the intensity of those circumstances. In the interest of broad judicial discretion, the law does not establish specific ways for measuring circumstances’ intensity. This gives judges (...) more freedom and autonomy, but it also imposes on them more social responsibility and challenges them to manage the uncertainty and subjectivity inherent in this complex activity. This paper proposes a model to aid the linguistic assessment of circumstances’ intensity and to provide linguistic and numerical recommendations to determine an appropriate punishment interval. M-LAMAC determines the collective evaluation of circumstances of the same type, determines the prevalence of a type of circumstance by means of a compensation function, recommends the required modification in the input interval, and finally recommends a numerical interval adjusted to the judges’ initially expressed preferences. The model’s applicability is demonstrated by means of several experiments on a fictitious case of bank document forgery. (shrink)

A common criticism of the use of algorithms in criminal justice is that algorithms and their determinations are in some sense ‘opaque’—that is, difficult or impossible to understand, whether because of their complexity or because of intellectual property protections. Scholars have noted some key problems with opacity, including that opacity can mask unfair treatment and threaten public accountability. In this paper, we explore a different but related concern with algorithmic opacity, which centers on the role of public trust in (...) grounding the legitimacy of criminal justice institutions. We argue that algorithmic opacity threatens the trustworthiness of criminal justice institutions, which in turn threatens their legitimacy. We first offer an account of institutional trustworthiness before showing how opacity threatens to undermine an institution’s trustworthiness. We then explore how threats to trustworthiness affect institutional legitimacy. Finally, we offer some policy recommendations to mitigate the threat to trustworthiness posed by the opacity problem. (shrink)

Ethicists have for the past 20 years debated the possibility of using neurointerventions to improve intelligence and even moral capacities, and thereby create a safer society. Contributing to a recent debate concerning neurointerventions in criminal rehabilitation, Nicole Vincent and Elizabeth Shaw have separately discussed the possibility of responsibility enhancement. In their ethical analyses, enhancing a convict’s capacity responsibility may be permissible. Both Vincent and Shaw consider self-control to be one of the constituent mental capacities of capacity responsibility. In (...) this paper, we critically examine the promise of improving convicts’ capacity responsibility by neuroenhancements of self-control to see whether the special characteristics of the inmate population make a difference in the analyses. As improving self-control by means of neurointerventions seems plausible, we then ask whether it is or could be a justified measure in court rulings. We conclude that, even if there are cases in which neurointerventions were warranted in the context of the stated goals of the criminal court, i.e., decreasing recidivism and rehabilitating the offenders to the society, due to the range of individual variability in the constitution of self-control, the prescription of specific neurointerventions of self-control falls outside the scope of legitimate court rulings. (shrink)

This article explores the trend of increasing automation in law enforcement and criminal justice settings through three use cases: predictive policing, machine evidence and recidivism algorithms. The focus lies on artificial-intelligence-driven tools and technologies employed, whether at pre-investigation stages or within criminal proceedings, in order to decode human behaviour and facilitate decision-making as to whom to investigate, arrest, prosecute, and eventually punish. In this context, this article first underlines the existence of a persistent dilemma between the goal (...) of increasing the operational efficiency of police and judicial authorities and that of safeguarding fundamental rights of the affected individuals. Subsequently, it shifts the focus onto key principles of criminal procedure and the presumption of innocence in particular. Using Article 6 ECHR and the Directive (EU) 2016/343 as a starting point, it discusses challenges relating to the protective scope of presumption of innocence, the burden of proof rule and the _in dubio pro reo_ principle as core elements of it. Given the transformations law enforcement and criminal proceedings go through in the era of algorithms, big data and artificial intelligence, this article advocates the adoption of specific procedural safeguards that will uphold rule of law requirements, and particularly transparency, fairness and explainability. In doing so, it also takes into account EU legislative initiatives, including the reform of the EU data protection _acquis_, the E-evidence Proposal, and the Proposal for an EU AI Act. Additionally, it argues in favour of revisiting the protective scope of key fundamental rights, considering, _inter alia_, the new dimensions suspicion has acquired. (shrink)

The present article proceeds from the mainstream view that the conceptual framework underpinning adversarial systems of criminal adjudication, i.e. a mixture of common-sense philosophy and probabilistic analysis, is unsustainable. In order to provide fact-finders with an operable structure of justification, we need to turn to epistemology once again. The article proceeds in three parts. First, I examine the structural features of justification and how various theories have attempted to overcome Agrippa’s trilemma. Second, I put Inferential Contextualism to the test (...) and show that a defeasible structure of justification allocating epistemic rights and duties to all participants of an inquiry manages to dissolve the problem of scepticism. Third, I show that our epistemic practice already embodies a contextualist mechanism. Our problem was not that our Standard of Proof is inoperable but that it was not adequately conceptualized. Contextualism provides the framework to articulate the abovementioned practice and to treat ‘reasonable doubts’ as a mechanism which we can now describe in detail. The seemingly insurmountable problem with our efforts to define the concept “reasonable doubts” was the fact that we have been conflating the surface features of this mechanism and its internal structure, i.e. the rules for its use. (shrink)

This study aims to identify artificial intelligence (AI) technologies that are applied in climate-smart agricultural practices and address ethical concerns of deploying those technologies from legal perspectives. As climate-smart agricultural AI, the study considers those AI-based technologies that are used for precision agriculture, monitoring peat lands, deforestation tracking, and improved forest management. The study utilized a systematic literature review approach to identify and analyze AI technologies employed in climate-smart agriculture and associated ethical and legal concerns. The study findings indicate (...) several ethical concerns for deploying AI in climate-smart agricultural practices pertaining to data inaccuracy, other technical errors based on wrong recommendations or wrongful acts, data ownership and intellectual property issues, and economic issues resulting in digital division and privacy and security related issues. In this study, the ethical concerns were further examined based on criminal law, tort law, privacy and data protection law, and intellectual property law. In this regard, the study finds that the current tort law pattern is more suitable than the criminal law pattern to address some major ethical concerns, such as data inaccuracy and other technical errors based on wrong recommendations or wrongful acts. Finally, the study recommends that at the global level, all countries need to fill up the current gap of international law on climate-smart agriculture through agreeing on a standard set of legal provisions and enhancing collaboration in innovation and deployment of climate-smart agricultural AI. It further recommends that at the local level, countries need to adopt suitable regulations addressing multi-stakeholders’ interests associated with the deployment of climate-smart agricultural AIs. (shrink)

The data mining field in computer science specializes in extracting implicit information that is distributed across the stored data records and/or exists as associations among groups of records. Criminal databases contain information on the crimes themselves, the offenders, the victims as well as the vehicles that were involved in the crime. Among these records lie groups of crimes that can be attributed to serial criminals who are responsible for multiple criminal offenses and usually exhibit patterns in their operations, (...) by specializing in a particular crime category (i.e., rape, murder, robbery, etc.), and applying a specific method for implementing their crimes. Discovering serial criminal patterns in crime databases is, in general, a clustering activity in the area of data mining that is concerned with detecting trends in the data by classifying and grouping similar records. In this paper, we report on the different statistical and neural network approaches to the clustering problem in data mining in general, and as it applies to our crime domain in particular. We discuss our approach of using a cascaded network of Kohonen neural networks followed by heuristic processing of the networks outputs that best simulated the experts in the field. We address the issues in this project and the reasoning behind this approach, including: the choice of neural networks, in general, over statistical algorithms as the main tool, and the use of Kohonen networks in particular, the choice for the cascaded approach instead of the direct approach, and the choice of a heuristics subsystem as a back-end subsystem to the neural networks. We also report on the advantages of this approach over both the traditional approach of using a single neural network to accommodate all the attributes, and that of applying a single clustering algorithm on all the data attributes. (shrink)

Vast research has sought to better understand the origins and development of rape myth beliefs given the problematic influence of such misconceptions throughout global societies and criminal justice pathways. The current research aims to build on this body of literature by examining the contribution that psychopathic personality traits (affective responsiveness, cognitive responsiveness, interpersonal manipulation, egocentricity) and emotional intelligence may have upon rape myth beliefs. Furthermore, this study will investigate the extent to which sociodemographic characteristics (age, gender, ethnicity, education), (...) and prior experience of sexual victimisation, contribute to variance in rape myth acceptance scores. In total 251 participants (M Age = 31.66) completed an online, self-report questionnaire which included contemporary measures of psychopathy and rape myth acceptance, never previously tested in combination. Results of a hierarchical multiple regression analysis indicate that egocentricity, age, and gender were significantly associated with rape myth beliefs. Emotional intelligence, as well as affective and interpersonal traits of psychopathy, were not directly related with rape mythology. Findings are interpreted alongside previous research, where we suggest there is an urgent need for larger, nationally representative samples, systematically recruited from the general population to help clarify uncertainty in existing literature emerging from small-scale opportunistic datasets. (shrink)

This paper presents a theory of reasoning with evidence in order to determine the facts in a criminal case. The focus is on the process of proof, in which the facts of the case are determined, rather than on related legal issues, such as the admissibility of evidence. In the literature, two approaches to reasoning with evidence can be distinguished, one argument-based and one story-based. In an argument-based approach to reasoning with evidence, the reasons for and against the occurrence (...) of an event, e.g., based on witness testimony, are central. In a story-based approach, evidence is evaluated and interpreted from the perspective of the factual stories as they may have occurred in a case, e.g., as they are defended by the prosecution. In this paper, we argue that both arguments and narratives are relevant and useful in the reasoning with and interpretation of evidence. Therefore, a hybrid approach is proposed and formally developed, doing justice to both the argument-based and the narrative-based perspective. By the formalization of the theory and the associated graphical representations, our proposal is the basis for the design of software developed as a tool to make sense of the evidence in complex cases. (shrink)

This article describes the creation of a lightweight ontology of European Union (EU) criminal procedural rights in judicial cooperation. The ontology is intended to help legal practitioners understand the precise contextual meaning of terms as well as helping to inform the creation of a rule ontology of criminal procedural rights in judicial cooperation. In particular, we started from the problem that directives sometimes do not contain articles dedicated to definitions. This issue provided us with an opportunity to explore (...) a phenomenon typically neglected in the construction of domain-specific legal ontologies. Whether classical definitions are present or absent, laws and legal sources in general are typically peppered with a number of hidden definitions (in the sense that they are not clearly marked out as such) as well as incomplete definitions, which may nevertheless help legal practitioners (and legal reasoning systems) to reason on the basis of analogy or teleology. In this article we describe the theoretical basis for building an analogical lightweight ontology in the framework of an EU project called _CrossJustice_. We present our methodology for collecting the data, extracting the data fields and creating the ontology with WebProtégé, followed by our conclusions and ideas for future work. (shrink)

The present article proceeds from the mainstream view that the conceptual framework underpinning adversarial systems of criminal adjudication, i.e. a mixture of common-sense philosophy and probabilistic analysis, is unsustainable. In order to provide fact-finders with an operable structure of justification, we need to turn to epistemology once again. The article proceeds in three parts. First, I examine the structural features of justification and how various theories have attempted to overcome Agrippa’s trilemma. Second, I put Inferential Contextualism to the test (...) and show that a defeasible structure of justification allocating epistemic rights and duties to all participants of an inquiry manages to dissolve the problem of scepticism. Third, I show that our epistemic practice already embodies a contextualist mechanism. Our problem was not that our Standard of Proof is inoperable but that it was not adequately conceptualized. Contextualism provides the framework to articulate the abovementioned practice and to treat ‘reasonable doubts’ as a mechanism which we can now describe in detail. The seemingly insurmountable problem with our efforts to define the concept “reasonable doubts” was the fact that we have been conflating the surface features of this mechanism and its internal structure, i.e. the rules for its use. (shrink)

The present article proceeds from the mainstream view that the conceptual framework underpinning adversarial systems of criminal adjudication, i.e. a mixture of common-sense philosophy and probabilistic analysis, is unsustainable. In order to provide fact-finders with an operable structure of justification, we need to turn to epistemology once again. The article proceeds in three parts. First, I examine the structural features of justification and how various theories have attempted to overcome Agrippa’s trilemma. Second, I put Inferential Contextualism to the test (...) and show that a defeasible structure of justification allocating epistemic rights and duties to all participants of an inquiry manages to dissolve the problem of scepticism. Third, I show that our epistemic practice already embodies a contextualist mechanism. Our problem was not that our Standard of Proof is inoperable but that it was not adequately conceptualized. Contextualism provides the framework to articulate the abovementioned practice and to treat ‘reasonable doubts’ as a mechanism which we can now describe in detail. The seemingly insurmountable problem with our efforts to define the concept “reasonable doubts” was the fact that we have been conflating the surface features of this mechanism and its internal structure, i.e. the rules for its use. (shrink)

In this paper a new use case for legal argumentation support tools is considered: supporting discussions about analyses of complex criminal cases with the help of Bayesian probability theory. By way of a case study, two actual discussions between experts in court cases are analysed on their argumentation structure. In this study the usefulness of several recognised argument schemes is confirmed, a new argument scheme for arguments from statistics are proposed, and an analysis is given of debates between experts (...) about the validity of their arguments. From a practical point of view the case study yields insights into the design of support software for discussions about Bayesian analyses of complex criminal cases. (shrink)

In this paper, I have two primary goals. First, I show that the causal-based story approach in A hybrid formal theory of arguments, stories and criminal evidence is ill suited to negative causation. In the literature, the causal-based approach requires that hypothetical stories be causally linked to the explanandum. Many take these links to denote physical or psychological causation, or temporal precedence. However, understanding causality in those terms, as I will show, cannot capture cases of negative causation, which are (...) of interest to the Law. In keeping with this, I also discuss some of the difficulties Hybrid Theory invites by remaining silent on the nature of the causal links. In my second aim, I sketch a way for Hybrid Theory to overcome this problem. By replacing the original, underlying causal structure with contrastive causation in the law, Hybrid Theory can represent reasoning in which the evidence that is appealed to is causally linked via negative causation to the explananda. (shrink)

Why is that? The stakes are now considerably higher. Darwinism: Science or Philosophy? is the proceedings of a symposium that took place at Southern Methodist University in the spring of 1992. The focus of that symposium was Phillip Johnson’s then recently published book Darwin on Trial. At the time, Johnson was a novelty -- a respected professor of criminal law at Cal Berkeley who was raising doubts about evolution. All harmless, good fun, no doubt. And Berkeley has an illustrious (...) history of harboring eccentrics, kooks, and oddballs. (shrink)

In trying to make sense out of our behavior, we reach a point at which we stop talking about what we did and start talking about what we wish we had done, about what we mean to do next. But we think we are still talking about our motives and intentions in what we did. How do we know when we cross the line between finding out what actually happened and ascribing to a situation what we think ought to have (...) happened?This is a serious question, precisely because we usually do not know the difference between what we do and what we imagine that we do. For since all action is governed by our conception of “how we would have it” and “what we would bring about,” we can always honestly claim to have meant what we did for the best. We frequently find ourselves in the situation in which we don't know why we did what we did, but by casting about, we can always turn up a purpose for which our action must have been meant - a purpose which we now perceive.The reasons we ascribe to our actions, like our excuses, are governed by the demands made on us now, rather than by the factual content of what we did. Thus the behavior of reason-giving or excusing is meant to satisfy the social situation in which we presently find ourselves: the demand for explanation, the need to be “in the right,” the need to reinforce our convictions. The verbal account of what we did is thus literally about (as representing and serving) where we are now rather than what we did then.Our verbal account is about the context of social obligation in which we have to explain ourselves, not about the behavior we claim to interpret. This is why, proverbially, we will give any excuse which will satisfy the accuser and why “understanding” i.e. inventing reasons for our conduct, is no corrective. The addict, alcoholic, neurotic and criminal recidivist are frequently rational and even persuasive in giving reasons for what they do. And we wonder why if they understand their problems so well they can't do something about them. The problem is that the habitué doesn't understand his behavior at all. He is simply trained to give a certain after the fact explanation as a conditioned response to his habitual behavior. What he “understands” is not what makes him go back to the pill, the bottle or to crime, but what to tell people each time after he does. Naturally, when the habit-triggering occasion arises, it is never that reason - the stated one - which leads to repetition, but some new, special crisis, difficulty or impulse.Bluntly stated, our reasons and justifications have little or nothing to do with our actual behavior. We continue to suppose that we intend to do this or that immediately before we do it. But insofar as this intention is expressed it is a separate verbal act having its own conditions and consequences; insofar as it is unexpressed (and in general) intention is disclosed only through conduct. It is only the powerful social convention of justification that supports the illusion that verbal expressions are causal antecedents of acts.Two conclusions follow. First, the “sense” of our activity is either evident in the activity or else it is nonexistent, since not yet discovered meaning is meaning not yet achieved. Second, our verbal justifications are acts having a peculiar sense of their own. They express the relation of our convictions and objectives to the convictions and objectives of those to whom our explanations are addressed. Here is where, in the process of making sense of our activity, the crossover from discovery to invention occurs. This is why we may never discover the extent to which our justifications are inventions superimposed on the conduct they are supposed to represent. Insofar as our justification satisfies those to whom it is addressed, (including ourselves) it is right. But we take this rightness, which is actually the the compatibility of our values and objectives, to be the rightness of the act in question. The reasons we give for our actions make great sense, but despite our invincible “rightness” our actions, taken in themselves, make no more sense.What are our options? They are either to put ourselves in the power of those to whom we explain ourselves, or to take responsibility for our own nonsense. Take our conduct out of the medium of talk - of habitual justification - in which it is suspended and sustained, and what is it? Whatever has become of the “importance” of our appointments, our meetings, our appearances, our obligations, our consistency? Experience, not persuasion and reasoning, is the corrective. Only the direct experience of futility is powerful enough to break our addiction to justification. (shrink)

In this paper we investigate risk prediction of criminal re-offense among juvenile defendants using general-purpose machine learning algorithms. We show that in our dataset, containing hundreds of cases, ML models achieve better predictive power than a structured professional risk assessment tool, the Structured Assessment of Violence Risk in Youth, at the expense of not satisfying relevant group fairness metrics that SAVRY does satisfy. We explore in more detail two possible causes of this algorithmic bias that are related to biases (...) in the data with respect to two protected groups, foreigners and women. In particular, we look at the differences in the prevalence of re-offense between protected groups and the influence of protected group or correlated features in the prediction. Our experiments show that both can lead to disparity between groups on the considered group fairness metrics. We observe that methods to mitigate the influence of either cause do not guarantee fair outcomes. An analysis of feature importance using LIME, a machine learning interpretability method, shows that some mitigation methods can shift the set of features that ML techniques rely on away from demographics and criminal history which are highly correlated with sensitive features. (shrink)

One of the main challenges for computational legal research is drawing up innovative heuristics to derive actionable knowledge from legal documents. While a large part of the research has been so far devoted to the extraction of purely legal information, less attention has been paid to seeking out in the texts the clues of more complex entities: legally relevant facts whose detection requires to link and interpret, as a unified whole, legal information and results of empirical analyses. This paper presents (...) an ongoing research that points in this direction, trying to devise new ways to support public prosecutors in assessing the dangerousness of individuals and groups under investigation, an activity that precisely relies on the cross-sectional evaluation of legal and empirical data. A knowledge mining strategy will be outlined that lines up, into a single metaheuristic model, information extraction, network-based inference, machine learning and visual analytics. We will focus, in particular, on the integration of graph-based inference and machine learning methods used both to support classification tasks and to explore new forms of man-machine cooperation. Experiments made involving public prosecutors from the Italian Anti-Mafia Investigation Directorate and using data from real investigations have not only shown the potentialities of our approach but also offered an opportunity to reflect on the role we could assign to AI when thinking about the future of legal science and practice. (shrink)

In the court of law, a person can be punished for attempting to commit a crime. An open issue in the study of Artificial Intelligence and Law is whether the law of attempts could be formally modelled. There are distinct legal rules for determining attempted crime whereas the last-act rule (also called proximity rule) represents the strictest approach. In this paper, we provide a formal model of the last-act rule using structured argumentation.

Cryptocurrencies like Bitcoin are offering new avenues for economic empowerment to individuals around the world. However, they also provide a powerful tool that facilitates criminal activities such as human trafficking and illegal weapons sales that cause great harm to individuals and communities. Cryptocurrency advocates have argued that the ethical dimensions of cryptocurrency are not qualitatively new, insofar as money has always been understood as a passive instrument that lacks ethical values and can be used for good or ill purposes. (...) In this paper, we challenge such a presumption that money must be ‘value-neutral.’ Building on advances in artificial intelligence, cryptography, and machine ethics, we argue that it is possible to design artificially intelligent cryptocurrencies that are not ethically neutral but which autonomously regulate their own use in a way that reflects the ethical values of particular human beings – or even entire human societies. We propose a technological framework for such cryptocurrencies and then analyse the legal, ethical, and economic implications of their use. Finally, we suggest that the development of cryptocurrencies possessing ethical as well as monetary value can provide human beings with a new economic means of positively influencing the ethos and values of their societies. (shrink)

AI-driven decisions can draw data from virtually any area of your life to make a decision about virtually any other area of your life. That creates fairness issues. Effective regulation to ensure fairness requires that AI systems be transparent. That is, regulators must have sufficient access to the factors that explain and justify the decisions. One approach to transparency is to require that systems be explainable, as that concept is understood in computer science. A system is explainable if one can (...) provide a human-understandable explanation of why it makes any particular prediction. Explainability should not be equated with transparency. To address transparency and characterize its relation to explainability, we define transparency for a regulatory purpose. A system is transparent for a regulatory purpose (r-transparent) when and only when regulators have an explanation, adequate for that purpose, of why it yields the predictions it does. Explainability remains relevant to transparency but turns out to be neither necessary nor sufficient for it. The concepts of explainability and r-transparency combine to yield four possibilities: explainable and either r-transparent or not; and not explainable and either not r-transparent or r-transparent. Combining r-transparency with ideas from the Harvard computer scientist Cynthia Dwork, we propose four requirements on AI systems. (shrink)