Marco Innocenti

In this article, I examine how artificial intelligence can support decision-making and thus promote moral autonomy and meaningful work in innovation environments. In particular, I focus on R&D teams developing new technologies and address the challenge of integrating the value commitments of different members without compromising each innovator’s ability to pursue their own moral agendas. Drawing on a contractualist approach, I propose a heuristic process for resolving conflicting value commitments by identifying converging lines of technological development, while presenting our contribution to other theories on this subject. I then explore the prospects of an AI system supporting this process, describing an Artificial Moral Advisor that helps working teams map feasible solutions to moral dilemmas based on value commitments expressed by the various members. I consider the stakes of this tool for developers’ autonomy, arguing that engaging in conversation with it need not undermine meaningfulness if users retain control over their moral input and final judgment.

This paper suggests using Agent-Based Models augmented by Large Language Models in applied ethics research devoted to organisational and sector-wide governance. Through parallels with other forms of AI-assisted moral inquiry, it argues that these tools can support research grounded in rule consequentialism and principle contractualism by facilitating exploratory approaches that guide further empirical analysis. These exploratory studies can contribute to assessing and formulating rules and principles that resist the Ideal World Objection (IWO), as well as to developing strategies for implementing them at optimal acceptance rates. To this end, these simulations must allow researchers to observe and act upon variables such as the expected value of different acceptance rates for a rule or principle, their respective probabilities, and how these vary over time. These are the variables I identify as central to the evaluation of rules and principles from the perspective of the IWO, and as suitable for guiding a transition from one rule to another. Additional variables may be relevant depending on the specific study undertaken, the normative theory on which it relies, and the possible solution to the IWO that the research might endorse or wish to test.

In this paper, we formulate a second-order moral principle, called 'Model Consistency Principle', designed to guide the moral reasoning of organisational decision-makers whose actions may contribute to the training of artificial intelligence (AI) models. Unlike conventional approaches in AI ethics that primarily address developers, regulators, or users of AI systems, this inquiry has focused on those moral agents who, while not directly involved in developing these technologies, nonetheless shape the rules that AI systems learn through their institutional or corporate decisions. The core claim of this principle is that when actions are liable to become part of an AI training set, a moral agent cannot simply act according to their own principles and criteria. Instead, they should also consider what rules an AI model might infer from their behaviour, ensuring that these inferences align with the criteria they would otherwise have followed. A decision that takes this principle into account, while aimed at reducing the weight of variables considered irrelevant in the rules inferred by the AI, will nonetheless need to consider these very variables precisely to avoid spurious correlations. Actions that would otherwise be morally permissible may cease to be so when considered within the context of AI training, not because they violate the agent's principles, but because they fail to adequately guide the AI model toward inferring the correct decision rule.

This paper investigates the ethical challenges faced by AI-driven start-ups, where the rapid pace of innovation and limited resources often preclude team members from fully understanding the product under development or its societal implications. We propose the concept of “swarm moral reflexivity”, where ethical reflection emerges collectively from the interactions of individuals focused on their specific tasks. Drawing on Swarm Intelligence theories and Alasdair MacIntyre's framework of moral deliberation, this approach enables teams to engage with ethical issues through daily encounters with conflicting responsibilities, rather than relying on top-down value systems or comprehensive ethical oversight. Our model suggests that decentralised, collective moral awareness can effectively support Responsible Innovation in AI start-ups, ensuring that ethical concerns are recognised and addressed throughout the development process, even in fast-paced and resource-constrained environments.

This study adopts a hermeneutic, practice-based approach to Responsible Innovation to explore how a reflective and proactive attitude can be implemented in a start-up context. We hypothesised that a moral hermeneutics framework - rooted in post-phenomenology and theories on technology-induced value change - could provide a way to understand how practitioners in a start-up make sense of the different kinds of responsibilities in their work, balancing professional demands and standards of excellence with broader ecological and social commitments. Using in-depth interviews with the team members of a start-up R&D laboratory, we explored how they interpret their responsibilities-as-(moral)-obligations. Our findings suggest that the syntactical ways team members make sense of the relationship between these responsibilities can be useful for understanding how reflexivity can surface in this environment. We conclude by proposing that less conciliatory interpretations of conflicting responsibilities may lead to a collective search for practical solutions addressing these tensions, as long as it is embedded in a collective dialogue involving the other members’ moral perspectives and technical expertise.

This paper aims to engineer the concept of biomimetic design for its application in agricultural technology as an innovation strategy to sustain non-human species’ adaptation to today’s rapid environmental changes. By questioning the alleged intrinsic morality of biomimicry, a formulation of it is sought that goes beyond the sharp distinction between nature as inspiration and the human field of application of biomimetic technologies. After reviewing the main literature on Responsible Innovation, we support Vincent Blok’s “eco-centric” perspective on biomimicry, which considers the human and natural worlds as indistinguishable parts of a shared Earth. We propose this approach as a complement to the “evomimetic” critique, which warns biomimetic research against the limits of adaptationism. By merging these two reframing of the biomimicry concept, we thus pave the way for a new understanding of the use of human-inspired technology (such as artificial intelligence) to help the “evolution” of domesticated species into (semi)autonomous natural-technological hybrids. In particular, the examples we consider concern the potential of AI-enabled robotic bases in technological beekeeping.

This paper aims to analyse how COVID-19 pandemic is changing our perception of reality. It starts looking at our situation from the point of view of Riegl’s distinction between optical and tactile, and then it compares the nature of the relationship between these two approaches to Lévi-Strauss’s description of bricolage. Our current world-view turns out to be not only an optic one, because the optical approach is just the means by which we can articulate a private and social life messed up by Coronavirus. Thereby, optical takes care of tactile without replacing it, and this article draws parallels between this aspect and language as described by Heidegger. Finally, after having argued the presence of an aura in this “optical house of tactile” in both Walter Benjamin’s and Hito Steyerl’s forms, this article tries to figure out how this perspective could last beyond the end of this emergency.