No abstract

Normative thinking about addiction has traditionally been divided between, on the one hand, a medical model which sees addiction as a disease characterized by compulsive and relapsing drug use over which the addict has little or no control and, on the other, a moral model which sees addiction as a choice characterized by voluntary behavior under the control of the addict. Proponents of the former appeal to evidence showing that regular consumption of drugs causes persistent changes in the brain structures and functions known to be involved in the motivation of behavior. On this evidence, it is often concluded that becoming addicted involves a transition from voluntary, chosen drug use to non-voluntary compulsive drug use. Against this view, proponents of the moral model provide ample evidence that addictive drug use involves voluntary chosen behavior. In this article we argue that although they are right about something, both views are mistaken. We present a third model that neither rules out the view of addictive drug use as compulsive, nor that it involves voluntary chosen behavior.

Artificial intelligence holds great promise in terms of beneficial, accurate and effective preventive and curative interventions. At the same time, there is also awareness of potential risks and harm that may be caused by unregulated developments of artificial intelligence. Guiding principles are being developed around the world to foster trustworthy development and application of artificial intelligence systems. These guidelines can support developers and governing authorities when making decisions about the use of artificial intelligence. The High-Level Expert Group on Artificial Intelligence set up by the European Commission launched the report Ethical guidelines for trustworthy artificial intelligence in 2019. The report aims to contribute to reflections and the discussion on the ethics of artificial intelligence technologies also beyond the countries of the European Union (EU). In this paper, we use the global health sector as a case and argue that the EU's guidance leaves too much room for local, contextualized discretion for it to foster trustworthy artificial intelligence globally. We point to the urgency of shared globalized efforts to safeguard against the potential harms of artificial intelligence technologies in health care.

Does addiction to heroin undermine the voluntariness of heroin addicts' consent to take part in research which involves giving them free and legal heroin? This question has been raised in connection with research into the effectiveness of heroin prescription as a way of treating dependent heroin users. Participants in such research are required to give their informed consent to take part. Louis C. Charland has argued that we should not presume that heroin addicts are competent to do this since heroin addiction by nature involves a loss of ability to resist the desire for heroin. In this article, I argue that Charland is right that we should not presume that heroin addicts are competent to consent, but not for the reason he thinks. In fact, as Charland's critics correctly point out, there is plenty of evidence showing that heroin addicts can resist their desire for heroin. These critics are wrong, however, to conclude from this that we should presume that heroin addicts are competent to give their voluntary consent. There are, I shall argue, other conditions associated with heroin addiction that might constrain heroin addicts' choice in ways likely to undermine the voluntariness of their consent. In order to see this, we need to move beyond the focus on the addicts' desires for heroin and instead consider the wider social and psychological circumstances of heroin addiction, as well as the effects these circumstances may have on the addicts' beliefs about the nature of their options.

Background In order to improve interventions for problem gambling, there is a need for studies that can highlight psychological factors that support the desire to reduce gambling. Objective To explore online problem gamblers' motivation for change by studying participants' reactions to an online treatment referral website designed to motivate at-risk gamblers to seek help. Design A qualitative evaluation study, combining focus groups and in-depth interviews. Data were analyzed using the general inductive approach. Informants The informants included 19 male, treatment- and non-treatment seeking, online gamblers who played a variety of games, including poker, sports betting and online casino. Results Motivation to change emerged as two processes including (a) empathy with others, which included projection of their thoughts and feelings onto others, and (b) dissonance between gambling behavior and ideal self-image. Dissonance included two subthemes: (i) dissonance due to positive feelings towards sports and athletics, and (ii) dissonance due to gambling among family. Conclusions The findings have implications for interventions designed to evoke motivation early in treatment of online problem gambling. Inducing problem gamblers to reflect on the thoughts and feelings of concerned significant others, real or fictional, could be a viable strategy to motivate online problem gamblers to consider change.

No abstract

ObjectiveTo demonstrate what it takes to reconcile the idea of fairness in medical algorithms and machine learning (ML) with the broader discourse of fairness and health equality in health research.MethodThe methodological approach used in this paper is theoretical and ethical analysis.ResultWe show that the question of ensuring comprehensive ML fairness is interrelated to three quandaries and one dilemma.DiscussionAs fairness in ML depends on a nexus of inherent justice and fairness concerns embedded in health research, a comprehensive conceptualisation is called for to make the notion useful.ConclusionThis paper demonstrates that more analytical work is needed to conceptualise fairness in ML so it adequately reflects the complexity of justice and fairness concerns within the field of health research.

No abstract