Models not only represent but may also influence their targets in important ways. While models’ abilities to influence outcomes has been studied in the context of economic models, often under the label ‘performativity’, we argue that this phenomenon also pertains to epidemiological models, such as those used for forecasting the trajectory of the Covid-19 pandemic. After identifying three ways in which a model by the Covid-19 Response Team at Imperial College London (Ferguson et al. 2020) may have influenced scientific advice, policy, and individual responses, we consider the implications of epidemiological models’ performative capacities. We argue, first, that performativity may impair models’ ability to successfully predict the course of an epidemic; but second, that it may provide an additional sense in which these models can be successful, namely by changing the course of an epidemic.

At the beginning of the COVID-19 pandemic, high hopes were placed on digital contact tracing. Digital contact tracing apps can now be downloaded in many countries, but as further waves of COVID-19 tear through much of the northern hemisphere, these apps are playing a less important role in interrupting chains of infection than anticipated. We argue that one of the reasons for this is that most countries have opted for decentralised apps, which cannot provide a means of rapidly informing users of likely infections while avoiding too many false positive reports. Centralised apps, in contrast, have the potential to do this. But policy making was influenced by public debates about the right app configuration, which have tended to focus heavily on privacy, and are driven by the assumption that decentralised apps are “privacy preserving by design”. We show that both types of apps are in fact vulnerable to privacy breaches, and, drawing on principles from safety engineering and risk analysis, compare the risks of centralised and decentralised systems along two dimensions, namely the probability of possible breaches and their severity. We conclude that a centralised app may in fact minimise overall ethical risk, and contend that we must reassess our approach to digital contact tracing, and should, more generally, be cautious about a myopic focus on privacy when conducting ethical assessments of data technologies.

At the beginning of the COVID-19 pandemic, high hopes were put on digital contact tracing, using mobile phone apps to record and immediately notify contacts when a user reports as infected. Such apps can now be downloaded in many countries, but as second waves of COVID-19 are raging, these apps are playing a less important role than anticipated. We argue that this is because most countries have opted for app configurations that cannot provide a means of rapidly informing users of likely infections while avoiding too many false positive reports. Mathematical modelling suggests that differently configured apps have the potential to do this. These require, however, that some pseudonymised data be stored on a central server, which privacy advocates have cautioned against. We contend that their influential arguments are subject to two fallacies. First, they have tended to one-sidedly focus on the risks that centralised data storage entails for privacy, while paying insufficient attention to the fact that inefficient contact tracing involves ethical risks too. Second, while the envisioned system does entail risks of breaches, such risks are also present in decentralised systems, which have been falsely presented as ‘privacy preserving by design’. When these points are understood, it becomes clear that we must rethink our approach to digital contact tracing in our fight against COVID-19.

Background: The burgeoning field of biomedical research involving the mixture of human and animal materials has attracted significant ethical controversy. Due to the many dimensions of potential ethical conflict involved in this type of research, and the wide variety of research projects under discussion, it is difficult to obtain an overview of the ethical debate. This paper attempts to remedy this by providing a systematic review of ethical reasons in academic publications on human-animal chimera research. Methods: We conducted a systematic review of the ethical literature concerning human-animal chimeras based on the research question: "What ethical reasons have been given for or against conducting human-animal chimera research, and how have these reasons been treated in the ongoing debate?" Our search extends until the end of the year 2017, including MEDLINE, Embase, PhilPapers and EthxWeb databases, restricted to peer-reviewed journal publications in English. Papers containing ethical reasons were analyzed, and the reasons were coded according to whether they were endorsed, mentioned or rejected. Results: Four hundred thirty-one articles were retrieved by our search, and 88 were ultimately included and analyzed. Within these articles, we found 464 passages containing reasons for and against conducting humananimal chimera research. We classified these reasons into five categories and, within these, identified 12 broad and 31 narrow reason types. 15% of the retrieved passages contained reasons in favor of conducting chimera research (Category P), while 85% of the passages contained reasons against it. The reasons against conducting chimera research fell into four further categories: reasons concerning the creation of a chimera (Category A), its treatment (Category B), reasons referring to metaphysical or social issues resulting from its existence (Category C) and to potential downstream effects of chimera research (Category D). A significant proportion of identified passages (46%) fell under Category C. Conclusions: We hope that our results, in revealing the conceptual and argumentative structure of the debate and highlighting some its most notable tendencies and prominent positions, will facilitate continued discussion and provide a basis for the development of relevant policy and legislation.

The prospective introduction of autonomous cars into public traffic raises the question of how such systems should behave when an accident is inevitable. Due to concerns with self-interest and liberal legitimacy that have become paramount in the emerging debate, a contractarian framework seems to provide a particularly attractive means of approaching this problem. We examine one such attempt, which derives a harm minimisation rule from the assumptions of rational self-interest and ignorance of one's position in a future accident. We contend, however, that both contractarian approaches and harm minimisation standards are flawed, due to a failure to account for the fundamental difference between those 'involved' and 'uninvolved' in an impending crash. Drawing from classical works on the trolley problem, we show how this notion can be substantiated by reference to either the distinction between negative and positive rights, or to differences in people's claims. By supplementing harm minimisation with corresponding constraints, we can develop crash algorithms for autonomous cars which are both ethically adequate and promise to overcome certain significant practical barriers to implementation.