Symbiosis between humans and AI is a two-way relationship that poses several unprecedented challenges not only from the technological viewpoint but also as regards foundational AI research. In this paper we address some philosophical questions about the nature of this symbiosis and argue that a human-centred approach is needed to design symbiotic AI systems in order to ensure their ethical acceptability.
As Artificial Intelligence (AI) systems are becoming a part of our daily lives, how to improve the
current shortcomings and limitations in human-machine collaboration becomes a question more
urgent than ever. The primary challenge of an AI agent functioning alone is how effectively and
flawlessly it achieves its goal. However, in a team of humans and AI assisting each other for a
common goal, the challenges are not limited to the goal itself, since the AI system should have the
ability to reason about the human’s actions while considering their mental models. Symbiotic AI
(also known as human-AI symbiosis) promises to boost human-machine collaboration and
sociotechnical teaming, with mutually beneficial relationships, by augmenting (and valuing) human
cognitive abilities rather than replacing them [
Fostering human-AI symbiosis requires designing AI systems according to a human-centred approach, as developed within the Human-Computer Interaction (HCI) community. These systems take advantage of computer features, such as powerful algorithms, big data management, and advanced sensors. However, they also consider the needs, desires, emotions, intentions, actions, etc., of people who are going to use them, providing high levels of automation along with human control. At the same time, it is also fundamental that humans understand and trust robust and stable AI systems that use important knowledge components and make decisions explainable, also in conditions of noise, uncertainty, and small perturbations. The focus is therefore on the design of new interaction paradigms that can amplify, augment, and enhance human performance, in ways that make systems reliable, safe, and trustworthy. This is the main question of the foundational research done by the University of Bari (together with INFN) within the NRPP-funded project Future AI Research (FAIR).2 It is articulated into several subquestions, among which we here address the following: How to improve the understandability, acceptability, and sustainability of SAI systems? In particular, acceptability is the subject of research for our investigation within a dedicated work package in the project we are involved in. It involves value alignment between AI and humans. It is related to understanding AI decisions, the algorithmic bias, the respect of privacy policies for data collected by AI systems, the struggle between security ensured by AI systems and fundamental freedoms, the mitigation of possible safety and health risks. In FAIR, studies on the acceptability of SAI adopt an interdisciplinary approach involving researchers in AI, Law, and Philosophy. The goal is twofold: (i) to investigate the respect and exercise of fundamental rights of humans supported by SAI (e.g., legal rules in algorithmic decisions, AI and responsibility, robotic subjectivity, workers’ rights, and taxpayer’s rights); (ii) to study the epistemological and ethical aspects of SAI. In this paper, we contribute to the achievement of (ii) by exploring the foundations of human-centred symbiosis with AI from the viewpoint of philosophers.
The structure of the paper is the following. In Section 2 we address some foundational and philosophical aspects of SAI, by analysing first the relationship between life and artefact (Section 2.1) and then the one between intelligence and symbiosis (Section 2.2). In Section 3 we investigate the factors that might enable a symbiosis with AI systems, by exploiting synergies between the fields of Ethics and Human-centred Computing. First, we analyse ethical dilemmas arising from the application of human-centred approaches to SAI (Section 3.1). Then, we introduce our conceptual model for mapping and assessing symbiosis in socio-technical systems (Section 3.2). Additionally, we offer thematic areas for potential policy recommendations to facilitate workshop discussions. Notably, these recommendations arise from our method's conceptualization phase, not from the assessment results (Section 3.3). In Section 4 we conclude the paper with final remarks and directions for future work.
The term 'Symbiotic Artificial Intelligence' adds to an already problematic binomial – that of 'Artificial Intelligence' – an adjective that further complicates the picture, but which seems to constitute one of the very latest frontiers of AI research. In 'symbiosis', it is implicit that these are two or more lives that coexist in order to cooperate (or at least to benefit from coexistence with the other). Therefore, a truly Symbiotic AI should envisage an interaction between man and machine that is no longer that between a controller and a controlled, but rather a 'biunivocal' one between two potentially 'equal' agents in the decision-making process, and in which one can influence the other. Two intelligent agents, then, if perhaps not in the same way. The ‘ex-controller’ – the man – engages in a relationship in which it is the machine itself that can take over the control functions by modifying, correcting, intervening in its choices in real time and in the direction of a common 'goal', based on a broader and more precise knowledge of the data and procedures required to perform the task. In this regard, it should be noted that no matter how asymmetrical this kind of relationship 2 https://future-ai-research.it/ between (intelligent) lives may be, there is always a residue of control that one party exerts over the other, and vice versa. Otherwise the symbiotic relationship would cease to exist altogether. However much, for example, between human beings and Escherichia coli there is a symbiotic relationship strongly unbalanced on the former, the bacterium maintains a certain amount of biological autonomy that, on the one hand, promotes the well-being of the host through a kind of non-mental wisdom while, on the other hand, it always runs the risk of giving rise to a pathological relationship. We do not have complete control over Escherichia coli, although we rely on its expertise for the success of our metabolic activity.
This type of HCI – which we define as symbiotic – is also different from that of so-called
‘third-order’ technologies [
SAI, on the other hand, could in principle also operate differently from its symbiont, perhaps because the latter has made incorrect assessments. At the same time, this is possible because, conversely, man relies on artificial intelligence, anticipating from the outset that his cooperation with it will take place according to this mode of interaction.
How is it possible to think of a SAI? What kind of technology will it be? Are there already forms
of human-machine interaction that foreshadow their relationship in a symbiotic sense? For instance,
Kai-Fu Lee and Chen Qiufan [
Certainly, to think of an AI means to imagine the possibility for the machine or software to be able not only to learn and respond 'live' (this to some extent already happens with the IoT, for example), but also to be able to learn regularities from our behaviours, intervening in real time, and with a certain amount of 'authority': in the sense that it is potentially capable of modifying human behaviour that, in absence of its artificial counterpart, would be exercised otherwise. Symbiosis in this sense provides for a kind of peer relationship, in which two lives coexist and learn from each other: the machine learns by observing me, and I learn new information that the machine provides from what it has learned by observing me.
“While the computer’s role has shifted from being a passive ‘executor’ to an active learner, the role of the human is still that of an actively involved teacher, because it is the humans that create clean, consumable data for the computer to analyse and hence train itself. However, what if there was a way in which the computer’s role remains that of an active learner, but we humans can passively sit back and go on with our lives, while the computer learns from our actions?” [6, p. 4].
Here, it is no longer a question of 'teaching' machines what to do, but of learning from them: not only in the indirect sense that this can always happen (chatting with Chat-GPT on a subject we know little about, for example), but in the sense that they can give us directives on what to do in order to do it better, and achieve the desired end. Much scientific literature calls this type of HCI "symbiotic," but to what extent is this correct? What meaning of symbiosis is involved in this application of the term to AI? And with what consequences?
The purpose of this Section is to try to give some answers to these questions, which can be summarised as follows: how can a Symbiotic Artificial Intelligence be defined and in what ways, if at all, is it possible? These foundational questions will allow us to address whether and to what extent a symbiotic type of AI is virtually compatible with a human-centred approach.
The first thing to focus on regarding the possibility of SAI from a foundational point of
view is the relationship between life and artefact. These are two elements that are traditionally
opposite. A living being is one thing, an artificial being is another: here, however, they must
somehow be together. Traditionally we trace the great distinction between entities of nature (and
therefore living entities) and artificial entities back to the second book of Aristotle’s Physics. But
this goes all the way to Darwin, who still distinguishes between two great kingdoms, or two great
systems of laws in the world, which are the organic and the inorganic [
According to this traditional paradigm of the life sciences that goes from Aristotle up to modern biology, an artefact, or a machine (an entity that is a product of techne, according to Aristotle’s own definition) is not something alive. What does it mean to be alive for Aristotle? In De anima Aristotle says that to live means to have the capacity to nourish, grow and decay [8, p. 22]. These functions are all united under the sign of movement, specifically spontaneous movement. For Aristotle and then throughout the long course of Western biology, what distinguishes a living thing from an artefact is the ability to move spontaneously. The machine is not endowed with life, it is not "animate" (ensouled) because it does not possess an autonomous principle of movement. It is we who move the machine, whereas a living thing, an insect, or an animal, such as plants themselves (which have a nonlocal motion, but a motion of growth), move autonomously. We do not drive them.
The modern era, especially with the rise of the mechanistic model, will reject such a view of life by coming to conceive even living beings as machines whose movement is always the result of a complex chain of external impacts (think of Descartes, Hobbes, and Spinoza). But a revival of Aristotelian conceptuality will occur in the nineteenth century, with Naturphilosophie and the emergence of an "organicistic" paradigm, which gave birth to modern biology.
This conception, whereby a living being is one that possesses an autonomous principle of
movement, a self-finality (entelechy), has been somewhat challenged by the emergence of robotics
and computer science in the twentieth century. These sciences study the ways in which it is possible
to build machines that can attain a certain level of autonomy. Obviously not the autonomy that
pertains to living beings in general, and to the human being less than ever. But all artificial
intelligence systems, and today especially Large Language Models (LLMs), have shown the
capacity for a kind of self-development. Robotics and computer science in some ways constitute an
exception to the old distinction between physis and techne, between life and artefact. Robots and
instances of artificial intelligence are a special kind of machines: they confront us with
"mechanical" processes that nevertheless exhibit characteristics that somehow have to do with life,
and thus potentially also with symbiosis. Maturana and Varela proposed in the twentieth century the
famous distinction between autopoietic and allopoietic machines: the former machines would be the
living organisms, that is, machines whose process results in the preservation, reproduction, and
development of the machines themselves, while allopoietic machines are machines that are operated
and have something other than the machine itself as a result of their process [
One might ask: If an artefact (a device or technology that is not made of organic parts) possessed all the characteristics that an organism that is made of organic material also possesses, what would remain different between the two? Would they both be considered living? [10 p.55, 11] Can an artefact become an autopoietic machine? Indeed, this was a route attempted as early as the middle of the last century (e.g., by Von Neumann, with his self-replicating machines) and then in the 1980s in the field of A-Life.
A-Life is based on the assumption that life is something that can also be synthesised digitally: that is, it does not matter what the support is, whether it is made of atoms or bits. The important thing is that this support manifests certain characteristics, precisely including that of self-preservation, self-reproducing, having autonomous movement aimed at its own sake, etc. Examples are numerous (Conway's Game of Life, Polyworld, RepRap, Slugbot etc).
These developments seem to challenge the classical distinction between life and artefact, and this is of utmost importance for the purpose of a foundation of a SAI. Indeed, the latter can be thought of as an AI that virtually has the ability to enter into a "life-to-life" relationship.
Are robots "living" entities? Are artificial intelligences such as ChatGPT entities that, although not "material," exhibit some of the characteristics of life?
In the case of robots Lévy [
How do things stand with intelligent life? For the foundation of an SAI, the question is essential, since it is assumed that the two lives that should enter symbiosis are both intelligent lives. And indeed, in the case of the artificial symbiont it might be the case that its life completely coincides with its intelligence. SAI thus allows us to ask important questions about the kind of relationship that exists between life and intelligence.
The problem is extremely difficult, since it depends on what kind of definition of intelligence we start with.
A minimal definition of intelligence as the capacity for organisation has been advocated by
various philosophers and scientists, starting from Schelling and Darwin. In this sense, even a
mushroom or a worm (or our own organs, as Darwin says) possess a minimal degree of intelligence,
since they exhibit a certain amount of organisation aimed at solving problems and adapting to the
environment [15 p. 21]. But this is what software, programs, and instances of artificial intelligence
also do: they have a basic arrangement that starting from certain inputs found in their own context
reconfigure themselves to meet specific demands. From this perspective, which could be called
'homogeneous continuism’, it is clear that a SAI is already there and is already with us: our devices,
through which we interact with generative AI systems, are already in some sense forms of
intelligence with which we live in symbiosis. Clearly, here we are dealing with a definition of
intelligence that flattens discontinuities and reduces them to a mere difference in degree: it is
enough for an entity to exhibit a certain computational strategy to be considered intelligent (from
viruses to humans and AI systems). At this level, a SAI is conceivable first and foremost as a form
of integration between human beings and digital tools [
However, there are less inclusive definitions, such as those that restrict the field of intelligence to
vertebrates with a minimum of brain activity [
In opposition to such weaker forms of "continuism," there are various versions of strong or absolute discontinuism, whereby intelligence is actually a uniquely human capability. To define intelligence as any ability to solve problems or to calculate even the way animals do would be an equivocal way of talking about intelligence, because true intelligence is only our own. Indicative of "true" intelligence would only be capabilities such as universalization, creativity, spontaneity, self-consciousness, emotionality etc.: all characteristics that are the exclusive preserve of human beings. In this case, in order to talk about SAI we would have to approach science fiction or very distant in time perspectives that contemplate the possibility of achieving an AGI, and thus some kind of Singularity. At this level, the foundation of a SAI would be very problematic, and we could use this category only in a metaphorical way: that is, we could talk about some everyday practices of interaction with AI or some forms of HCI only “as if'' they were a symbiosis between human being and AI, but in reality, at the foundational level a real SAI would never occur. Therefore, the identification of objective criteria for being able to speak of SAI should be obtained more on the level of a socio-technical constructivism than on the level of an ontological and/or epistemic foundation.
This is the perspective taken in the rest of the paper.
The present Section embarks on an exploration of the intricate dynamics existing between distinct yet interrelated paradigms. Principally, it elucidates the inherent tension at the nexus of human-machine symbiosis and methodologies aligned with human-centeredness, as progressively mandated or suggested by diverse international entities, prominently exemplified by the EU as documented in the Ethics guidelines for trustworthy AI (2019) and the White Paper on Artificial Intelligence (2020).
The ensuing discourse unfolds in a tripartite structure, each section contributing distinct layers of comprehension. Section 3.1 dissects the dichotomy between the evolving imperatives of human-centric approaches, underscored by an assortment of international organisations, and the trajectory of SAI systems' advancement. Section 3.2 proposes an innovative conceptual framework – an intellectual scaffold, as it were – for apprehending the symbiosis hypothesis between the human cohort and AI systems. The ultimate tier of our discussion emerges in Section 3.3. Herein, a compendium of strategic policy alternatives takes form, with the central purpose of harmonising the intricate interplay between ethical considerations and the domain of human-centred computing. 3.1.
As comprehensively expounded in Section 2, the multifaceted concept of symbiosis
accommodates diverse definitions and foundational concepts within its ambit when applied to the
intricate nexus between humans and machines [
However, regardless of the interpretative avenue pursued, an essential quandary emerges: the notion of symbiosis within the context of artificial intelligence seemingly collides with the ethical pursuit of human-centred AI. The tension resides in the dilemma of reconciling symbiosis – whose essence is entwined with the realm of machines; entities inherently distinct from humans – with the ethical imperative of constructing systems attuned to human essence (whatever this essence is). In this light, symbiosis assumes the role of an apparent contradiction against the backdrop of human-centeredness.
This contradiction gains further traction and efficacy – transforming into more than mere logical negation – due to the burgeoning emergence of normative guidelines across the global landscape. These guidelines overtly or subtly endorse human-centredness as an ethical cornerstone for the evaluation and standardisation of AI. Notable references in this sphere include The Montreal Declaration for a Responsible Development of Artificial Intelligence (2018), OECD Principles on Artificial Intelligence (2019), Singapore's Model AI Governance Framework (2019), European Commission's White Paper on Artificial Intelligence (2020), and UNESCO's Recommendation on the Ethics of Artificial Intelligence (2021).
Crossing this intricate confluence, we propose a potential avenue to harmonise the development
of SAI systems with the underpinnings of human-centeredness. It involves viewing the manifold
semantic dimensions of symbiosis as integral elements of human-centricity. Realising one
configuration of SAI over another hinges upon our choices, from machine construction to
purposeful production, from autonomous machine learning to delineating the bounds of this
machinic self-learning. These choices, particularly those concerning symbiosis, crystallise
human-centeredness into a practical embodiment of responsibility. Augmenting the
symbiosis-human-centricity nexus is the realisation that symbiosis with technology embodies a
discourse of perspective – an intellectual trajectory – rather than an entrenched domain of
techno-scientific exploration. This presents SAI systems as akin to what the German sociologist and
philosopher Max Weber delineates as an “ideal type”. An ideal type functions as an abstract and
hypothetical construct used in social science to dissect and comprehend complex social phenomena.
It captures essential characteristics while transcending strict realism, serving as a conceptual
scaffold for understanding intricate realities. In light of this, the pivotal query shifts from “What
does symbiosis signify?” to probe the sociotechnical milieu in which symbiosis is tenable [
To embark upon the conceptual elucidation of a theoretical underpinning for SAI systems from a human-centred standpoint necessitates a foundational premise – an intellectual scaffold, if you will. It is essential to cast aside conventional notions of symbiosis as an intrinsic, preordained state or an aspirational endpoint. Instead, we must reframe symbiosis as an ontological potentiality, a prospect rather than a static reality. This resituating of symbiosis within the domain of possibility underscores its human-centred essence. The symbiosis between human cognition and algorithms exists not as an ineluctable biotechnological or post-humanist outcome but as a plausible discourse – an avenue to articulate the burgeoning socio-technical infrastructure that increasingly interlaces human existence with technological rationality. This socio-technical constructivism offers us different benefits: ● Flexibility in definition. Artificial symbiosis is a concept that evolves alongside technological advancements and societal shifts. A socio-technical constructivist approach recognises that the definition of artificial symbiosis can be problematic and should not be fixed in stone. It acknowledges that our understanding of this symbiosis is contingent upon the current state of knowledge, technology, and society. This approach allows for ongoing adaptation and refinement of the definition based on empirical testing and real-world implementation scenarios, ensuring it remains relevant and adaptable to changing circumstances. ● Anthropocentric value clarification. The socio-technical constructivist approach acknowledges that the anthropocentric value of artificial intelligence is not absolute but ● ● rather a perspective rooted in human interests and values. It doesn’t seek to impose one fixed viewpoint but encourages the exploration of various value systems and their interactions with technology. By recognising the subjectivity of anthropocentrism, this approach facilitates a more inclusive and open discussion about the roles and regulations of human-machine symbiosis. It allows us to incorporate diverse ethical and cultural perspectives within a framework that does not privilege any single viewpoint but recognises the value of coexistence and coevolution between humans and machines.
Adaptability to emerging AI-based technologies. As AI evolves rapidly, a socio-technical constructivist approach can accommodate the emergence of new technologies and their impact on the symbiotic relationship between humans and machines. It enables us to consider and adapt to unforeseen developments, ensuring our understanding and regulations remain relevant and practical. This flexibility is essential in an era where technology is advancing at an unprecedented pace.
A cautious approach to unbridled techno-ideology. Contemporary understanding
underscores that technological potency is anything but neutral – it assumes a role in shaping
and co-determining societal structures [
How can we effectively evaluate a socio-technical system? What criteria should we consider when identifying and assessing the level of symbiosis within the system? Evaluating a socio-technical system is notably more challenging than evaluating an individual technology due to numerous variables, contextual variations, scenarios, and changing user dynamics. In our ongoing research as part of the FAIR project, we are developing a model that thoroughly defines the key components of a given socio-technical system. These components represent the conditions that allow us to detect and subsequently consider the system for further assessments. We have conceptualised these conditions as a continuum, transitioning from a predominantly machine-oriented symbiosis to a more human-centred one. These conditions include Technical Functional Performance, Reality-Characteristic Replication, Human-Machine Interaction Type, and Subjective User Experience. Our approach involves using indicators placed on value scales to (a) measure the socio-technical landscape along each of these axes and (b) provide a general framework for mapping the degree and depth of symbiosis within the socio-technical construct under investigation.. Details are reported in Table 1.
This condition pertains to the operational efficacy of the SAI system as a technical entity. The system comprises diverse components, each endowed with distinct technical functionalities. Through their orchestrated interplay, the system attains operability and usability.
This condition delves into the capacity of the SAI system to emulate specific attributes of (human and non-human) reality through its technical operations. The system's technical prowess facilitates replicating, simulating, and reproducing key real-world features.
Relationship Type: Machine-to-Machine (M2M) Artificial Centrality: High (++++) Human Centrality: Minimal (+) Relationship Type: Machine-to-Human-to-Machine (M2H2M) Artificial Centrality: Significant (+++) Human Centrality: Moderate (++) Human-Machine Interaction Type Subjective User
Experience .
This condition addresses the nature of interaction between the human and the machine, irrespective of the subjective experience of the former. It underscores the intricacies of the interaction itself, devoid of the human's subjective engagement.
This condition centres on the subjective encounter of users within the AI system's ambit.
It delves into the depth of experiential engagement individuals undergo while interfacing with the technology.
Relationship Type: Human-to-Machine (H2M) Artificial Centrality: Moderate (++) Human Centrality: Substantial (+++) Relationship Type: Human-to-Machine-to-Human (H2M2H) Artificial Centrality: Minimal (+) Human Centrality: Profound (+++) 3.3.
In the nascent stage of our study, we are actively honing the criteria and dimensions essential for the evaluation of symbiotic AI as a socio-technical construct. As we wrap up our contribution, we present a set of policy recommendations. It is important to note that these recommendations do not constitute finalised conclusions based on pending analysis results; instead, they serve as a pertinent policy framework for discussion that we plan to delve into more deeply during our upcoming workshop. We believe that these recommendations lay the groundwork for potential regulatory guidelines that can inform the subsequent assessments in the remainder of our FAIR study. These policy frameworks encompass:: (a) Science and education, (b) Ethics of technology, (c) Design of technology, (d) Sustainable development goals (SDGs), and (e) Citizen participation. The policies are described in Table 2.
assumes development, hindering its assessment for
cessation. The freedom to question and decide is crucial;
responsible innovation can mask maintaining the status
quo [
AI ethics faces resistance from some in the field who
view it negatively. Embracing ethics involves short-term
trade-offs but leads to long-term sustainability. Proactive
ethics aids policy with comprehensive approaches, not
just bans, offering support and incentives [
[
including ethical shopping, bluewashing, lobbying,
dumping, and avoidance, which can hinder even
well-intentioned efforts [
human behaviour, psychology, and evolving social dynamics. The human-centric approach aligns AI with values, but turning this into specific guidelines is challenging. Defining ethics across contexts requires ongoing dialogue among stakeholders.
and fairness, but serving human needs often means trade-offs. Protecting privacy may reduce accuracy.
integrated into mainstream development, often overshadowed by technical and market concerns.
significantly shape AI's impact on SDGs. Balancing profit and social-environmental goals is crucial to avoid unintended harm and prioritise the public interest.
frameworks with metrics to measure efficacy, fairness, and sustainability, ensuring benefits outweigh risks.
evaluate the robustness and potential vulnerabilities of
In this paper we considered an emerging form of AI, called Symbiotic AI, that is able to engage in a symbiotic relationship with humans. Human-AI symbiosis poses not only several technological challenges but also many philosophical questions. Here we addressed the latter.
In Section 2 we highlighted the main issues concerning the possible foundation of SAI. Crucial philosophical questions were raised about the possibility of thinking about a form of AI capable of entering symbiosis with humans, starting from the specificity that such a technology should exhibit compared to more traditional AI. In particular, in Section 2.1 we questioned the application of biological categories – such as symbiosis – to artefacts. While robotics and AI challenge the traditional distinction between living and non-living based on the capacity of organisms to move autonomously, self-develop and reproduce, there remain some differences that currently appear insurmountable. Also, in Section 2.2, we offered a scan into three patterns of SAI based on as many definitions of intelligent life: the kind of SAI we can conceive of strictly depends on how we think about the continuity between organisation and intelligence. The less continuist our position, the less chance we have of putting down a foundation of SAI in a strong sense, namely from a biological or ontological point of view.
In Section 3 we emphasised the need to redefine our understanding of symbiosis with AI systems to bridge ethics and human-centred computing. Symbiosis, we argue, is more than a static state; it represents a harmonious coexistence between humans and AI, naturally emerging from interactive experiences. We stress the importance of innovative conceptual frameworks that inform strategic policy alternatives. By fostering deeply ingrained symbiosis guided by ethics, we can envision a future where AI enhances the human experience while respecting our values. This perceived beneficial nature of AI, jointly with the value alignment, are necessary preconditions for making AI technologies ethically acceptable.
For the future, the research will involve jurists in order to address the theme of acceptability not
only from the ethical but also from the legal viewpoint. Then, legal and ethical acceptability of SAI
will need to go through an operationalization process in order to be of practical relevance for the
design and implementation of SAI applications. High-level principles will be therefore turned into
operational definitions that pave the way to technical solutions, e.g., for (partially) automated
compliance testing. Operationalization will be accompanied by appropriate modelling of the SAI
application in hand. Notably, socio-technical systems (STS) are widely recognized as a valuable
approach to complex organisational work design that stresses the interaction between people and
technology in workplaces [
This work was partially supported by the project FAIR - Future AI Research (PE00000013), under the NRRP MUR program funded by the NextGenerationEU.
40. Dyoub, A., Costantini, S. & Lisi, F.A. Learning Domain Ethical Principles from Interactions with Users. Digital Society 1, 28 (2022). https://doi.org/10.1007/s44206-022-00026-y