Abstract This introductory chapter summarizes a key challenge confronting the criminal law and the criminal justice systems in all western nations: how to punish offenders who offend in order to promote a cause (such as alerting governments and the wider public to impending environmental crisis). The conventional criminal law and sentencing frameworks generally address offending which benefits only the offender. The offender is seen as malignantly motivated and is punished accordingly. This approach may be inappropriate when the offence carries no personal benefit for the offender yet may ultimately benefit the wider society. Legislatures, courts, and sentencing councils and commissions are now confronting a wide range of such offending. The chapter provides examples of the kinds of offences and offenders for whom mitigation may be appropriate. It then identifies some of the key issues and challenges for sentencing courts.

Abstract Should offender motives count in the determination of sentences? That is, should a crime committed out of a good (or bad) motive attract a sentence mitigation (or aggravation)? Views on these questions vary significantly. The purpose of this chapter is not to offer a definitive answer but, more modestly, to direct attention to the complexity of assessing offender motives. It is argued that challenges arise both when it comes to the categorization of good and bad motives, and with regard to the determination of the quality of motives within each of these categories. Attention is also directed to challenges arising from the fact than an offender may have conflicting or mixed motives. Finally, it is suggested that human behaviour may sometimes be motivationally over-determined, which means that it may not be possible to identify a real or true motive behind an action. Taken together, these considerations suggest that the idea of regarding motives as a possibly mitigating (or aggravating) factor at sentencing raises many challenges that are not easily addressed.

The ethics of punishment constitutes an area of research that has recently been through a significant expansion, both in breadth and depth. But why is such research important? And how can it be conducted in the most fruitful way? In this article it is argued, first, that the study of penal ethics is important in order to inform penal practice. However, second, it is shown that there are both theoretical and political obstacles to the possibility of delivering genuine action guidance to practitioners. Finally, four recommendations are presented that may help to ensure that research within the ethics of punishment is carried out in a manner that is consistent with the basic justification for its very existence, namely that it is needed to provide moral guidance of penal practices in the real world.

The use of artificial intelligence as an instrument to assist judges in determining sentences in criminal cases is attracting increasing theoretical attention. While many theorists have argued that there may be important advantages to introducing algorithmic sentencing support in criminal cases, almost no one has considered how such systems should actually be implemented. The purpose of this chapter is to fill this void. First, it is argued that current penal practice is non-ideal in the sense that it is dominated by overpunishment of offenders (the overpunishment assumption), and that algorithmic sentencing support systems are unlikely to be introduced in a way that appears to disturb the existing penal order (the preservation assumption). Second, a model called the “Restricted Application Model” is presented for how such algorithms might be used by judges within a framework characterized by the two outlined assumptions. Third, three objections to the model are considered and ultimately rejected. Thus, the model serves as a first attempt at outlining a procedure for the use of sentencing advisory systems by judges within real-life, and ethically non-ideal, penal systems.

The purpose of Adam Kolber's magnificent book is to show that a consequentialist theory of punishment is superior to standard retributivism. In this comment I argue that Kolber is right when he rejects the claim that the traditional punishment of the innocent argument provides sufficient grounds for the rejection of consequentialism, but also that it may be the case that threshold retributivism is more attractive than consequentialism when it comes to intuitive fit. Furthermore, it is suggested that this possibility gives rise to a trilemma which Kolber will have to confront in order to ultimately establish the superiority of consequentialism over retributivism.

Abstract One of the central ideas of modern retributivism is the view that an offender’s desert should be determined by the nature of the crime that has been committed. This cannot be changed by whatever follows the crime when the criminal court metes out the appropriate sentence or, indeed, after the sentence has been determined. But is it plausible to maintain that the desert of an offender should be determined solely by aspects of the crime and, therefore, by something that remains invariant after the crime? Julian V Roberts has, in several works, challenged the static element of standard retributivist theory. What he has proposed is a dynamic desert model according to which various aspects of an offender’s post-crime and post-sentence conduct should be regarded as carrying retributive significance. The purpose of this chapter is to critically examine Roberts’ dynamic desert model. It is argued, on the one hand, that several of the challenges facing traditional static accounts of retributivism are radically exacerbated by introducing a dynamic account of desert, but also, on the other, that Roberts’ considerations cannot just be brushed aside by adherents of static accounts of retributivism.

The relationship between social justice and criminal justice gives rise to a range of important ethical questions. The purpose of this paper is to consider an influential argument within this field: namely, the responsibility-based argument for social adversity mitigation. According to this argument, offenders with a ‘rotten social background’ should be given a sentence mitigation because they have less-developed mental capacities than offenders who have not experienced deep social deprivation. However, it is argued, first, that even if the empirical assumptions of this argument are correct, the conclusion is nevertheless premature. Second, it is shown that both the relations between diminished capacities and reduced responsibility, and between reduced responsibility and sentence mitigation give rise to several theoretical challenges. Finally, it is considered whether these challenges can be held to arise from an overambitious view of what should be expected from a penal ethical theory or from a failure to draw on proper standardizations in this sort of theorizing. It is concluded that it constitutes a highly complicated task to underpin the inference from diminished capacities to reduced sentences of socially deprived offenders.

In this article, our aim is to show why increasing the effectiveness of detecting doping fraud in sport by the use of artificial intelligence (AI) may be morally wrong. The first argument in favour of this conclusion is that using AI to make a non-ideal antidoping policy even more effective can be morally wrong. Whether the increased effectiveness is morally wrong depends on whether you believe that the current antidoping system administrated by the World Anti-Doping Agency is already morally wrong. The second argument is based on the possibility of scenarios in which a more effective AI system may be morally worse than a less effective but non-AI system. We cannot, of course, conclude that the increased effectiveness of doping detection is always morally wrong. But our point is that whether the introduction of AI to increase detection of doping fraud is a moral improvement depends on the moral plausibility of the current system and the distribution of harm that will follow from false positive and false negative errors.

Abstract Suppose that computer scientists and engineers have developed a brand-new algorithm which is designed to determine sentences in individual criminal cases. Suppose, furthermore, that it seems that this algorithm is actually very good at doing this job. Would it then be justified to replace human sentencing judges with the sentencing algorithm? The answer to this question obviously depends upon many things. However, in this chapter focus is placed narrowly on the question of how we should compare human judges and the algorithm with regard to how well each is doing the job of determining the severity of the appropriate sentences. It is argued that, due to both theoretical and practical reasons, we do to a large extent currently lack the penal ethical resources to answer this question. The impetus behind these considerations is the assumption that the question may well become urgent in a not very distant future and that, in the absence of the requisite ethical considerations, there is a significant risk that decisions on this matter will be made on insufficient or even ethically arbitrary grounds.

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.

Can it be justified to use neuroscientific technologies for influencing the functioning of human brain as a means of preventing offenders from engaging in future criminal conduct? This is indeed a highly controversial question and one which has a dark prehistory. Moreover, it is also a question that has attracted recent optimistic attention from researchers across different scientific fields. The purpose of this book is to consider various ethical challenges surrounding this question. More precisely, the author discusses issues such as, Is it morally acceptable to offer more lenient sentences to offenders in return for participation in neuroscientific treatment programmes? Would such offers be unacceptably coercive? Can it ever be morally justified to use compulsory neurointerventions as a means of preventing crime? Is it possible to administer neurointerventions as a type of punishment? Would it be acceptable for physicians to participate in the administration of neurointerventions on offenders? What is the moral significance of the sordid history of brain interventions for the present or future use of such treatment options? The author argues, on the one hand, that many of the in-principle objections to neuroscientific treatment are premature but also, on the other, that—given the way criminal justice systems currently function—we are well-advised not to put such treatment methods into practice.

The use of artificial intelligence as an instrument to assist judges in determining sentences in criminal cases is an issue that gives rise to many theoretical challenges. The purpose of this article is to examine one of these challenges known as the “input problem.” This problem arises supposedly due to two reasons: that in order for an algorithm to be able to provide a sentence recommendation, it needs to be inputted with case specific information; and that the task of presenting an adequate picture of a crime often turns out to be highly complex. Even though this problem has been noted since the earliest attempts at developing sentencing support systems, almost no one has considered the ethical nature of this challenge. The aim of this article is to fill that void. First, it is shown that the input problem has been subject to somewhat different interpretations. Second, several possible answers as to when and why the problem constitutes an ethical challenge are considered. Third, a few suggestions are presented as to how undesirable implications of complexity at the input stage might be ameliorated by tailoring the way sentencing algorithms are developed and used in the work of criminal courts.

Abstract We usually regard it as wrong to assault someone physically or to deprive someone of liberty or property. But if this is the case, then why is it morally acceptable (or even required) for the state to impose punishments on citizens who have broken the law? The purpose of this chapter is to give an introduction to the philosophy of punishment as an academic field, to provide an overview of the various topics which are considered in the ensuing chapter of this handbook, and to present a few reasons that have motivated this work.