Michele Loi

Academic publishing increasingly requires authors to disclose AI assistance, yet imposes reputational costs for doing so--especially when such assistance is substantial. This article analyzes that structural contradiction, showing how incentives discourage transparency in precisely the work where it matters most. Traditional venues cannot resolve this tension through policy tweaks alone, as the underlying prestige economy rewards opacity. To address this, the article proposes an alternative publishing infrastructure: a venue outside prestige systems that enforces mandatory disclosure, enables reproduction-based review, and supports ecological validity through detailed documentation. As a demonstration of this approach, the article itself is presented as an example of AI-assisted scholarship under reasonably detailed disclosure, with representative prompt logs and modification records included. Rather than taking a position for or against AI-assisted scholarship, the article outlines conditions under which such work can be evaluated on its own terms: through transparent documentation, verification-oriented review, and participation by methodologically committed scholars. While focused on AI, the framework speaks to broader questions about how academic systems handle methodological innovation.

Giustizia e genetica examines how emerging possibilities for deliberately modifying the human genome challenge our theories of social justice. Starting from the concept of human and genetic enhancement—the improvement of normal genetic characteristics rather than the treatment of disease—the book asks how the benefits and burdens of such technologies should be distributed in a just society. It reconstructs and compares the most influential contemporary liberal and egalitarian accounts of justice, including luck-egalitarian and Rawlsian approaches, and uses them to analyse genetic interventions as a new form of advantage and disadvantage. Particular attention is given to the moral status of the “natural lottery”, the distinction between brute luck and option luck, and the role of personal responsibility where genetic traits can be shaped or chosen. The book explores questions such as whether the state should subsidise therapeutic and non-therapeutic genetic interventions, how to regulate parental choices about the genetic traits of their children, and whether enhancement technologies risk exacerbating existing social inequalities. It argues that a satisfactory account of justice in the genomic is best achieved through a rethinking of Rawlsian equality of opportunity, while remaining sensitive to the historical abuses of eugenic policies.

Two topics at the center of Ethics of AI and HRI regard trust in AI agents as well as the adjudication of moral responsibility in situations where AI causes harm. In this paper we aim to advance the state of the art concerning these topics in several regards: First, we propose and evaluate a new empirical paradigm for measuring appropriate or calibrated trust in AI, that is, attitudes which are neither too trusting nor too cautious. The best way to measure calibrated trust, we argue, is by contrasting trust vested in AI agents when their relevant capacities equal those of a human expert in the domain. A second shortcoming of extant work regards generalizability: Trust in, and reliance on, AI are standardly explored with respect to a single context or domain. To investigate context-sensitivity, we ran experiments (total N=1276) across five key areas of AI application. Finally, we explored perceived moral responsibility for harm caused in human-AI interaction, with a particular focus on recent philosophical debates on the topic. Our findings suggest that approximately half of the participants vest equal trust in AI and human agents when their capacities are the same. However, there is considerable variation in trust calibration across domains, suggesting that context-sensitivity needs more attention. Human agents are attributed more moral responsibility than AI agents, whereas their supervisors are blamed less than those of AI agents. This suggests that, at least according to folk morality, there are no perceived "responsibility gaps" (Matthias 2004; Sparrow 2007) and that "retribution gaps" are a genuine possibility (Danaher 2016).

Intensified and extensive data production and data storage are characteristics of contemporary western societies. Health data sharing is increasing with the growth of Information and Communication Technology platforms devoted to the collection of personal health and genomic data. However, the sensitive and personal nature of health data poses ethical challenges when data is disclosed and shared even if for scientific research purposes. With this in mind, the Science and Values Working Group of the COST Action CHIP ME ‘Citizen's Health through public-private Initiatives: Public health, Market and Ethical perspectives’ identified six core values they considered to be essential for the ethical sharing of health data using ICT platforms. We believe that using this ethical framework will promote respectful scientific practices in order to maintain individuals’ trust in research. We use these values to analyse five ICT platforms and explore how emerging data sharing platforms are reconfiguring the data sharing experience from a range of perspectives. We discuss which types of values, rights and responsibilities they entail and enshrine within their philosophy or outlook on what it means to share personal health information. Through this discussion we address issues of the design and the development process of personal health data and patient-oriented infrastructures, as well as new forms of technologically-mediated empowerment.

The European Union’s Digital Services Act requires very large online platforms and search engines providers to assess and mitigate systemic risks. However, regulators and providers both face a fundamental challenge we refer to as the ‘convergence problem’—the inherent difficulty of achieving methodological and definitional agreement across diverse approaches investigating systemic risks. To address this challenge, we propose a novel dual-track framework that distinguishes between a permissive _Research Standard_ promoting methodological diversity and a structured _Compliance Standard_ ensuring accountability through systematic documentation. This approach enables meaningful oversight while preserving space for innovation and platform-specific adaptation, offering a model for regulating emerging technologies that balances regulatory certainty with methodological flexibility. In a self-contained Appendix, we discuss how systemic risk is approached in the recent European Union’s AI Act instead.

The demands of fair machine learning are often expressed in probabilistic terms. Yet, most of the systems of concern are deterministic in the sense that whether a given subject will receive a given score on the basis of their traits is, for all intents and purposes, either zero or one. What, then, can justify this probabilistic talk? We argue that the statistical reference classes used in fairness measures can be understood as defining the probability that hypothetical persons, who are representative of social roles, will receive certain goods. We call these hypothetical persons “representative individuals.” We claim that what we owe to actual, concrete individuals—whose individual chances of receiving the good in the system might be extreme (i.e., either zero or one)—is that their representative individual has an appropriate probability of receiving the good in question. While less immediately intuitive than other approaches, we argue that the representative individual approach has important advantages over other ways of making sense of this probabilistic talk in the context of fair machine learning.

This article presents a fairness principle for evaluating decision-making based on predictions: a decision rule is unfair when the individuals directly impacted by the decisions who are equal with respect to the features that justify inequalities in outcomes do not have the same statistical prospects of being benefited or harmed by them, irrespective of their socially salient morally arbitrary traits. The principle can be used to evaluate prediction-based decision-making from the point of view of a wide range of antecedently specified substantive views about justice in outcome distributions.

In this paper we argue that transparency of machine learning algorithms, just as explanation, can be defined at different levels of abstraction. We criticize recent attempts to identify the explanation of black box algorithms with making their decisions (post-hoc) interpretable, focusing our discussion on counterfactual explanations. These approaches to explanation simplify the real nature of the black boxes and risk misleading the public about the normative features of a model. We propose a new form of algorithmic transparency, that consists in explaining algorithms as an intentional product, that serves a particular goal, or multiple goals (Daniel Dennet’s design stance) in a given domain of applicability, and that provides a measure of the extent to which such a goal is achieved, and evidence about the way that measure has been reached. We call such idea of algorithmic transparency “design publicity.” We argue that design publicity can be more easily linked with the justification of the use and of the design of the algorithm, and of each individual decision following from it. In comparison to post-hoc explanations of individual algorithmic decisions, design publicity meets a different demand (the demand for impersonal justification) of the explainee. Finally, we argue that when models that pursue justifiable goals (which may include fairness as avoidance of bias towards specific groups) to a justifiable degree are used consistently, the resulting decisions are all justified even if some of them are (unavoidably) based on incorrect predictions. For this argument, we rely on John Rawls’s idea of procedural justice applied to algorithms conceived as institutions.

Recent epidemiological reports of associations between socioeconomic status and epigenetic markers that predict vulnerability to diseases are bringing to light substantial biological effects of social inequalities. Here, we start the discussion of the moral consequences of these findings. We firstly highlight their explanatory importance in the context of the research program on the Developmental Origins of Health and Disease (DOHaD) and the social determinants of health. In the second section, we review some theories of the moral status of health inequalities. Rather than a complete outline of the debate, we single out those theories that rest on the principle of equality of opportunity and analyze the consequences of DOHaD and epigenetics for these particular conceptions of justice. We argue that DOHaD and epigenetics reshape the conceptual distinction between natural and acquired traits on which these theories rely and might provide important policy tools to tackle unjust distributions of health

Trust and monitoring are traditionally antithetical concepts. Describing trust as a property of a relationship of reliance, we introduce a theory of trust and monitoring, which uses mathematical models based on two classes of functions, including _q_-exponentials, and relates the levels of trust to the costs of monitoring. As opposed to several accounts of trust that attempt to identify the special ingredient of reliance and trust relationships, our theory characterizes trust as a quantitative property of certain relations of reliance that can be quantified and expressed as a scalar quantity. Our theory is applicable to both human–human and human–artificial agent interactions, as it is agnostic with respect to the concrete realization of trustworthiness properties, and is compatible with many views differing on which properties contribute to trust and trustworthiness. Finally, as our mathematical models make the quantitative features of trust measurable, they provide empirical studies on trust with a rigorous methodology for its measurement.

In his recent article ‘Limits of trust in medical AI,’ Hatherley argues that, if we believe that the motivations that are usually recognised as relevant for interpersonal trust have to be applied to interactions between humans and medical artificial intelligence, then these systems do not appear to be the appropriate objects of trust. In this response, we argue that it is possible to discuss trust in medical artificial intelligence (AI), if one refrains from simply assuming that trust describes human–human interactions. To do so, we consider an account of trust that distinguishes trust from reliance in a way that is compatible with trusting non-human agents. In this account, to trust a medical AI is to rely on it with little monitoring and control of the elements that make it trustworthy. This attitude does not imply specific properties in the AI system that in fact only humans can have. This account of trust is applicable, in particular, to all cases where a physician relies on the medical AI predictions to support his or her decision making.

Real engines of the artificial intelligence revolution, machine learning models, and algorithms are embedded nowadays in many services and products around us. As a society, we argue it is now necessary to transition into a phronetic paradigm focused on the ethical dilemmas stemming from the conception and application of AIs to define actionable recommendations as well as normative solutions. However, both academic research and society-driven initiatives are still quite far from clearly defining a solid program of study and intervention. In this contribution, we will focus on selected ethical investigations around AI by proposing an incremental model of trust that can be applied to both human-human and human-AI interactions. Starting with a quick overview of the existing accounts of trust, with special attention to Taddeo’s concept of “e-trust,” we will discuss all the components of the proposed model and the reasons to trust in human-AI interactions in an example of relevance for business organizations. We end this contribution with an analysis of the epistemic and pragmatic reasons of trust in human-AI interactions and with a discussion of kinds of normativity in trustworthiness of AIs.

The widespread use of algorithms for prediction-based decisions urges us to consider the question of what it means for a given act or practice to be discriminatory. Building upon work by Kusner and colleagues in the field of machine learning, we propose a counterfactual condition as a necessary requirement on discrimination. To demonstrate the philosophical relevance of the proposed condition, we consider two prominent accounts of discrimination in the recent literature, by Lippert-Rasmussen and Hellman respectively, that do not logically imply our condition and show that they face important objections. Specifically, Lippert-Rasmussen’s definition proves to be over-inclusive, as it classifies some acts or practices as discriminatory when they are not, whereas Hellman’s account turns out to lack explanatory power precisely insofar as it does not countenance a counterfactual condition on discrimination. By defending the necessity of our counterfactual condition, we set the conceptual limits for justified claims about the occurrence of discriminatory acts or practices in society, with immediate applications to the ethics of algorithmic decision-making.

Holm (2022) argues that a class of algorithmic fairness measures, that he refers to as the ‘performance parity criteria’, can be understood as applications of John Broome’s Fairness Principle. We argue that the performance parity criteria cannot be read this way. This is because in the relevant context, the Fairness Principle requires the equalization of actual individuals’ individual-level chances of obtaining some good (such as an accurate prediction from a predictive system), but the performance parity criteria do not guarantee any such thing: the measures merely ensure that certain population-level ratios hold.

Here, we provide an ethical analysis of discrimination in private insurance to guide the application of non-discriminatory algorithms for risk prediction in the insurance context. This addresses the need for ethical guidance of data-science experts, business managers, and regulators, proposing a framework of moral reasoning behind the choice of fairness goals for prediction-based decisions in the insurance domain. The reference to private insurance as a business practice is essential in our approach, because the consequences of discrimination and predictive inaccuracy in underwriting are different from those of using predictive algorithms in other sectors. Here we focus on the trade-off in the extent to which one can pursue indirect non-discrimination versus predictive accuracy. The moral assessment of this trade-off is related to the context of application—to the consequences of inaccurate risk predictions in the insurance domain.

Clients may feel trapped into sharing their private digital data with insurance companies to get a desired insurance product or premium. However, private insurance must collect some data to offer products and premiums appropriate to the client’s level of risk. This situation creates tension between the value of privacy and common insurance business practice. We argue for three main claims: first, coercion to share private data with insurers is pro tanto wrong because it violates the autonomous choice of a privacy-valuing client. Second, we maintain that irrespective of being coerced, the choice of accepting digital surveillance by insurers makes it harder for the client to protect his or her autonomy. The violation of autonomy also makes coercing customers into digital surveillance pro tanto morally wrong. Third, having identified an economically plausible process involving no direct coercion by insurers, leading to the adoption of digital surveillance, we argue that such an outcome generates further threats against autonomy. This threat provides individuals with a pro tanto reason to prevent this process. We highlight the freedom dilemma faced by regulators who aim to prevent this outcome by constraining market freedoms and argue for the need for further moral and empirical research on this question.

Digital apps using Bluetooth to log proximity events are increasingly supported by technologists and governments. By and large, the public debate on this matter focuses on privacy, with experts from both law and technology offering very concrete proposals and participating to a lively debate. Far less attention is paid to effective incentives and their fairness. This paper aims to fill this gap by offering a practical, workable solution for a promising incentive, justified by the ethical principles of non-maleficence, beneficence, autonomy and justice. This incentive is a free phone optimised for running such app.

This paper supports the personal data platform cooperative as a means of bringing about John Rawls’s favoured institutional realisation of a just society, the property-owning democracy. It describes personal data platform cooperatives and applies Rawls’s political philosophy to analyse the institutional forms of a just society in relation to the economic power deriving from aggregating personal data. It argues that a society involving a significant number of personal data platform cooperatives will be more suitable to realising Rawls’s principle of fair equality of opportunity.

Purpose Cybersecurity in healthcare has become an urgent matter in recent years due to various malicious attacks on hospitals and other parts of the healthcare infrastructure. The purpose of this paper is to provide an outline of how core values of the health systems, such as the principles of biomedical ethics, are in a supportive or conflicting relation to cybersecurity. Design/methodology/approach This paper claims that it is possible to map the desiderata relevant to cybersecurity onto the four principles of medical ethics, i.e. beneficence, non-maleficence, autonomy and justice, and explore value conflicts in that way. Findings With respect to the question of how these principles should be balanced, there are reasons to think that the priority of autonomy relative to beneficence and non-maleficence in contemporary medical ethics could be extended to value conflicts in health-related cybersecurity. Research limitations/implications However, the tension between autonomy and justice, which relates to the desideratum of usability of information and communication technology systems, cannot be ignored even if one assumes that respect for autonomy should take priority over other moral concerns. Originality/value In terms of value conflicts, most discussions in healthcare deal with the conflict of balancing efficiency and privacy given the sensible nature of health information. In this paper, the authors provide a broader and more detailed outline.

Luciano Floridi was not the first to discuss the idea of group privacy, but he was perhaps the first to discuss it in relation to the insights derived from big data analytics. He has argued that it is important to investigate the possibility that groups have rights to privacy that are not reducible to the privacy of individuals forming such groups. In this paper, we introduce a distinction between two concepts of group privacy. The first, the “what happens in Vegas stays in Vegas” privacy (in the following: WHVSV privacy), deals with confidential information shared with the member of a group and inaccessible to (all or a specific group of) outsiders. The second, to which we shall refer as inferential privacy, deals with the inferences that can be made about a group of people defined by a feature, or combination thereof, shared by all individuals in the group. We show why we unreservedly agree with Floridi that groups can have a form of privacy that amounts to more than the mere fact of being sets of individuals each of whom has individual privacy; moreover, like Floridi, we find it plausible that at least some groups (those satisfying our definition of type-a groups) may have a right to a species of group privacy (that is, WHVSV privacy) as groups (and not just as individuals who belong to those groups). However, by turning our attention to the context of big data analytics, we show that the relevant, new notion of group privacy is one of inferential privacy. We argue that an absolute right (either of individuals or groups) to inferential privacy is implausible. We also show that many groups generated algorithmically (those satisfying our definition of type-b groups) cannot be right holders as groups (unless they become type-a groups).

This paper articulates a careful and detailed objection to the moral permissibility of postnatal abortion. Giubilini and Minerva claim that if being unable to nurture one’s newborn child without significant burdens to oneself, family or society, is a proper moral ground for the demand that the life of a fetus be terminated, then ‘after-birth abortion should be considered a permissible option for women who would be damaged by [rearing the child or] giving up their newborns for adoption.’ It will be shown that the permissibility of postnatal abortion does not follow from the argument’s premises, in particular, the premise that the newborn is not a person in the morally relevant sense.

The concept of the digital phenotype has been used to refer to digital data prognostic or diagnostic of disease conditions. Medical conditions may be inferred from the time pattern in an insomniac’s tweets, the Facebook posts of a depressed individual, or the web searches of a hypochondriac. This paper conceptualizes digital data as an extended phenotype of humans, that is as digital information produced by humans and affecting human behavior and culture. It argues that there are ethical obligations to persons affected by generalizable knowledge of a digital phenotype, not only those who are personally identifiable or involved in data generation. This claim is illustrated by considering the health-related digital phenotypes of precision medicine and digital epidemiology.

I sketch a libertarian argument for the right to test in the context of ‘direct to consumer’ (DTC) genetic testing. A libertarian right to genetic tests, as defined here, relies on the idea of a moral right to self-ownership. I show how a libertarian right to test can be inferred from this general libertarian premise, at least as a prima facie right, shifting the burden of justification on regulators. I distinguish this distinctively libertarian position from some arguments based on considerations of utility or autonomy, which are sometimes labelled ‘libertarian’ because they oppose a tight regulation of the direct to consumer genetic testing sector. If one takes the libertarian right to test as a starting point, the whole discussion concerning autonomy and personal utility may be sidestepped. Finally, I briefly consider some considerations that justify the regulation of the DTC genetic testing market, compatible with the recognition of a prima facie right to test.