This article synthesizes the current theoretical attempts to understand human-machine interactions and introduces seven premises to understand our emerging dynamics with increasingly competent, pervasive, and instantly accessible algorithms. The hope that these seven premises can build toward a social theory of human-AI cocreation. The focus on human-AI cocreation is intended to emphasize two factors. First, is the fact that our machine learning systems are socialized. Second, is the coevolving nature of human mind and AI (...) systems as smart devices form an indispensable part of our cognitive scaffolds, thus, shaping how we perceive the world and ourselves. The seven premises include: primacy of social structures; human’s desire for freedom and autonomy; AI systems will form inseparable parts of our cognitive/affective scaffolds and can change our self-understanding; philosophy and humanistic foundations on human flourishing as a guide to human-AI interaction; mindsponge information-filtering process; acculturative process of how values change and emerge from human-AI interaction; overlapping Venn diagram of the human-human, human-nature, and human-machine interactions. This article concludes with a discussion on human agency in our entanglements with socialized machines and the illusoriness of the Cartesian agent view of the mind. (shrink)

Artificial Intelligence is making rapid and remarkable progress in the development of more sophisticated and powerful systems. However, the acknowledgement of several problems with modern machine learning approaches has prompted a shift in AI benchmarking away from task-oriented testing towards ability-oriented testing, in which AI systems are tested on their capacity to solve certain kinds of novel problems. The Animal-AI Environment is one such benchmark which aims to apply the ability-oriented testing used in comparative psychology to AI systems. Here, we (...) present the first direct human-AI comparison in the Animal-AI Environment, using children aged 6–10. We found that children of all ages were significantly better than a sample of 30 AIs across most of the tests we examined, as well as performing significantly better than the two top-scoring AIs, “ironbar” and “Trrrrr,” from the Animal-AI Olympics Competition 2019. While children and AIs performed similarly on basic navigational tasks, AIs performed significantly worse in more complex cognitive tests, including detour tasks, spatial elimination tasks, and object permanence tasks, indicating that AIs lack several cognitive abilities that children aged 6–10 possess. Both children and AIs performed poorly on tool-use tasks, suggesting that these tests are challenging for both biological and non-biological machines. (shrink)

In some languages every statement must contain a specification of the type of evidence on which it is based: for example, whether the speaker saw it, or heard it, or inferred it from indirect evidence, or learnt it from someone else. This grammatical reference to information source is called 'evidentiality', and is one of the least described grammatical categories. Evidentiality systems differ in how complex they are: some distinguish just two terms (eyewitness and noneyewitness, or reported and everything else), while (...) others have six or even more terms. Evidentiality is a category in its own right, and not a subcategory of epistemic or some other modality, nor of tense-aspect. Every language has some way of referring to the source of information, but not every language has grammatical evidentiality. In English expressions such as I guess, they say, I hear that, the alleged are not obligatory and do not constitute a grammatical system. Similar expressions in other languages may provide historical sources for evidentials. True evidentials, by contrast, form a grammatical system. In the North Arawak language Tariana an expression such as "the dog bit the man" must be augmented by a grammatical suffix indicating whether the event was seen, or heard, or assumed, or reported. This book provides the first exhaustive cross-linguistic typological study of how languages deal with the marking of information source. Examples are drawn from over 500 languages from all over the world, several of them based on the author's original fieldwork. Professor Aikhenvald also considers the role evidentiality plays in human cognition, and the ways in which evidentiality influences human perception of the world.. This is an important book on an intriguing subject. It will interest anthropologists, cognitive psychologists and philosophers, as well as linguists. (shrink)

This collection of quotes demonstrates the elegant simplicity of Ai Weiwei's thoughts on key aspects of his art, politics, and life. A master at communicating powerful ideas in astonishingly few words, Ai Weiwei is known for his innovative use of social media to disseminate his views. The book is organized into six categories: freedom of expression; art and activism; government, power, and moral choices; the digital world; history, the historical moment, and the future; and personal reflections. Together, these quotes span (...) some of the most revealing moments of Ai Weiwei's eventful career-from his risky investigation into student deaths in the 2008 Sichuan earthquake to his arbitrary arrest in 2011-providing a window into the mind of one of the world's most electrifying and courageous contemporary artists. Ai Weiwei is one of China's most influential and inspiring figures. Artist, architect, curator, and activist, he has been an outspoken critic of the Chinese government's stance on human rights and democracy. (shrink)

There is a need recognized by the National Institute of Dental & Craniofacial Research and the National Cancer Institute to advance basic, translational and clinical saliva research. The goal of the Salivaomics Knowledge Base (SKB) is to create a data management system and web resource constructed to support human salivaomics research. To maximize the utility of the SKB for retrieval, integration and analysis of data, we have developed the Saliva Ontology and SDxMart. This article reviews the informatics advances in (...) saliva diagnostics made possible by the Saliva Ontology and SDxMart. (shrink)

Within current debates about the future impact of Artificial Intelligence on human society, roughly three different perspectives can be recognised: the technology-centric perspective, claiming that AI will soon outperform humankind in all areas, and that the primary threat for humankind is superintelligence; the human-centric perspective, claiming that humans will always remain superior to AI when it comes to social and societal aspects, and that the main threat of AI is that humankind’s social nature is overlooked in technological designs; (...) and the collective intelligence-centric perspective, claiming that true intelligence lies in the collective of intelligent agents, both human and artificial, and that the main threat for humankind is that technological designs create problems at the collective, systemic level that are hard to oversee and control. The current paper offers the following contributions: a clear description for each of the three perspectives, along with their history and background; an analysis and interpretation of current applications of AI in human society according to each of the three perspectives, thereby disentangling miscommunication in the debate concerning threats of AI; and a new integrated and comprehensive research design framework that addresses all aspects of the above three perspectives, and includes principles that support developers to reflect and anticipate upon potential effects of AI in society. (shrink)

The Salivaomics Knowledge Base (SKB) is designed to serve as a computational infrastructure that can permit global exploration and utilization of data and information relevant to salivaomics. SKB is created by aligning (1) the saliva biomarker discovery and validation resources at UCLA with (2) the ontology resources developed by the OBO (Open Biomedical Ontologies) Foundry, including a new Saliva Ontology (SALO). We define the Saliva Ontology (SALO; http://www.skb.ucla.edu/SALO/) as a consensus-based controlled vocabulary of terms and relations dedicated to the salivaomics (...) domain and to saliva-related diagnostics following the principles of the OBO (Open Biomedical Ontologies) Foundry. The Saliva Ontology is an ongoing exploratory initiative. The ontology will be used to facilitate salivaomics data retrieval and integration across multiple fields of research together with data analysis and data mining. The ontology will be tested through its ability to serve the annotation ('tagging') of a representative corpus of salivaomics research literature that is to be incorporated into the SKB. Background Saliva (oral fluid) is an emerging biofluid for non-invasive diagnostics used in the detection of human diseases. The need to advance saliva research is strongly emphasized by the National Institute of Dental and Craniofacial Research (NIDCR), and is included in the NIDCR's 2004- 2009 expert panel long-term research agenda [1]. The ability to monitor health status, disease onset, progression, recurrence and treatment outcome through noninvasive means is highly important to advancing health care management. Saliva is a perfect medium to be explored for personalized individual medicine including diagnostics, offering a non-invasive, easy to obtain means for detecting and monitoring diseases. Saliva testing potentially allows the patient to collect their own saliva samples at home, yielding convenience for the patient and savings in health costs, and facilitating multiple sampling. Specimen collection is less objectionable to patients and easier in children and elderly individuals. Due to these advantages. (shrink)

AI as decision support supposedly helps human agents make ‘better’decisions more efficiently. However, research shows that it can, sometimes greatly, influence the decisions of its human users. While there has been a fair amount of research on intended AI influence, there seem to be great gaps within both theoretical and practical studies concerning unintended AI influence. In this paper I aim to address some of these gaps, and hope to shed some light on the ethical and moral concerns (...) that arise with unintended AI influence. I argue that unintended AI influence has important implications for the way we perceive and evaluate human-AI interaction. To make this point approachable from both the theoretical and practical side, and to avoid anthropocentricallyladen ambiguities, I introduce the notion of decision points. Based on this, the main argument of this paper will be presented in two consecutive steps: i) unintended AI influence doesn’t allow for an appropriate determination of decision points-this will be introduced as decision-point-dilemma, and ii) this has important implications for the ascription of responsibility. (shrink)

We describe the rationale for an application ontology covering the domain of human body fluids that is designed to facilitate representation, reuse, sharing and integration of diagnostic, physiological, and biochemical data, We briefly review the Blood Ontology (BLO), Saliva Ontology (SALO) and Kidney and Urinary Pathway Ontology (KUPO) initiatives. We discuss the methods employed in each, and address the project of using them as starting point for a unified body fluids ontology resource. We conclude with a description of how (...) the body fluids ontology initiative may provide support to basic and translational science. (shrink)

Artificial Intelligence (AI) powered machines are increasingly mediating our work and many of our managerial, economic, and cultural interactions. While technology enhances individual capability in many ways, how do we know that the sociotechnical system as a whole, consisting of a complex web of hundreds of human–machine interactions, is exhibiting collective intelligence? Research on human–machine interactions has been conducted within different disciplinary silos, resulting in social science models that underestimate technology and vice versa. Bringing together these different perspectives (...) and methods at this juncture is critical. To truly advance our understanding of this important and quickly evolving area, we need vehicles to help research connect across disciplinary boundaries.This paper advocates for establishing an interdisciplinary research domain—Collective Human-Machine Intelligence (COHUMAIN). It outlines a research agenda for a holistic approach to designing and developing the dynamics of sociotechnical systems. In illustrating the kind of approach, we envision in this domain, we describe recent work on a sociocognitive architecture, the transactive systems model of collective intelligence, that articulates the critical processes underlying the emergence and maintenance of collective intelligence and extend it to human–AI systems. We connect this with synergistic work on a compatible cognitive architecture, instance-based learning theory and apply it to the design of AI agents that collaborate with humans. We present this work as a call to researchers working on related questions to not only engage with our proposal but also develop their own sociocognitive architectures and unlock the real potential of human–machine intelligence. (shrink)

In lieu of an abstract, here is a brief excerpt of the content:Toleration and Justice in the Laozi:Engaging with Tao Jiang's Origins of Moral-Political Philosophy in Early ChinaAi Yuan (bio)IntroductionThis review article engages with Tao Jiang's ground-breaking monograph on the Origins of Moral-Political Philosophy in Early China with particular focus on the articulation of toleration and justice in the Laozi (otherwise called the Daodejing).1 Jiang discusses a naturalistic turn and the re-alignment of values in the Laozi, resulting in a naturalization (...) of justice (impartiality) by rejecting artificial humaneness and rigid hierarchical moral-political structures. In this Laozian just world, there is no room for human intervention and rigid top-down enforcement of values, thus leaving justice to naturalized Heaven or Dao as the ultimate source of the cosmos.Based on Jiang's interpretative context, I show that there is a twofold justification for the "paradoxical" and "elusive"2 value of political toleration.3 A negative expression of toleration focuses on non-interference with choices, and non-enforcement of values toward those whom one reasonably disagrees with and regards as morally wrong. Such an attitude of toleration results in practical advantages such as avoiding entrenchment in bloodshed, abandonment, and deprivation.A positive expression of toleration operating in the Laozian ideal world requires equal protection of people as capable knowers, including those to whom we object. Such protection includes their ways of expression and an unbiased recognition of them as equally capable knowers so that they are heard without bias and prejudice. Such a kind of toleration is revealed as a constituent component of justice since it enables equal contribution toward the shaping of society without discrimination. In other words, toleration is seen as naturalized justice in a Laozian world within which no one is "being wronged with the capacity as a knower"—a kind of epistemic justice articulated by Miranda Fricker (Fricker 2007).4 [End Page 466]This article discusses how Jiang's political reconstruction of Laozian philosophy contains the seeds for a discussion of toleration, a value associated with distributive justice and against epistemic injustice. First, it introduces Jiang's arguments on a naturalization of justice and impartiality as Heavenly attributes in the Laozi. Second, it articulates the value of tolerance in the Laozi. Finally, I compare Laozian toleration with Confucian toleration using youwei- wuwei metaethical criticism.Moral-Political Philosophy in the LaoziJiang's interpretative framework about the origins of distributive justice in the Laozi starts with a discussion of the cosmogonic-mystical worldviews that signal "a new understanding of the nature of the cosmos, as well as a broad reorientation in the Heaven-human relationship in the mid-Warring States period" (p. 192). Differing from the interests in the origins of human culture or civilization in the Ru and Mo traditions, Jiang agrees with Franklin Perkins and identifies a "cosmogonic turn" in the late fourth century b.c. that reveals a demotion of anthropomorphic Heaven. By paring Heaven with earth and accordingly decentering human beings in the intellectual discourse, the Laozi directs equal values to the ten thousand things, with humans as just one among them (Perkins 2016, quoted in Jiang 2021, p. 196).5 Laozi's philosophy thus stands out by rejecting Heaven as caring for human affairs while taking Dao as the ultimate origin of the cosmos, as claimed in chapter 5 of the received text: "Heaven and earth are not humane; they treat ten-thousand things in the world as straw dogs; the sage is not humane; he treats people as straw dogs" (p. 199).With Jiang's reading of a naturalistic heaven, we see a re-alignment between cosmos and the human since sage-rulers follow the Heaven-Dao. The Laozian Dao is "self-so-ing" (ziran 自然), "accommodating" (rong 容), and "impartial" (gong 公), and thus marks a sharp contrast to Confucian familial bias resulting in "actively intervening in human affairs on behalf of the (certain) humans by appointing them to carry out its mission" (p. 200). With the sage emulating the ultimate Dao, the Laozi replaces the Confucian anthropocentric Heaven, which focuses on partiality (qin 親) and humaneness (ren 仁).This Laozian understanding of the cosmos also grounds its philosophical departure from Mohism despite both having a political... (shrink)

In their paper ‘Responsibility, second opinions and peer-disagreement: ethical and epistemological challenges of using AI in clinical diagnostic contexts’, Kempt and Nagel discuss the use of medical AI systems and the resulting need for second opinions by human physicians, when physicians and AI disagree, which they call the rule of disagreement.1 The authors defend RoD based on three premises: First, they argue that in cases of disagreement in medical practice, there is an increased burden of proof for the physician (...) in charge, to defend why the opposing view is adopted or overridden. This burden for justification can be understood as an increased responsibility. In contrast, such burden does allegedly not arise, when physicians agree in their judgement. Second, in those medical contexts where humans collaborate with humans such justification can be provided, since human experts can discuss the evidence and reasons that have led them to their judgement, through which the sources of disagreement can be found and a justified decision can be made by the physician in charge. Third, unlike human-to-human collaboration, such communicative exchange is not possible with an AI system. Due to AI’s opacity, the physician in charge has no means of illuminating why the AI disagrees. Conclusively, the authors propose RoD as a solution. RoD suggests that a second human expert should be consulted for advise in cases of human–AI disagreement. Once AI systems become more widespread in clinical practice, it can be expected that such type of disagreement occurs more frequently. AI, after all, is being implemented, because it promises, among others, higher accuracy, which implies that some abnormalities will be detected that the physician would have missed.2 Hence, it is laudable to discuss the moral implications of …. (shrink)

With an increasing use of artificial intelligence (AI) systems, theorists have analyzed and argued for the promotion of trust in AI and trustworthy AI. Critics have objected that AI does not have the characteristics to be an appropriate subject for trust. However, this argumentation is open to counterarguments. Firstly, rejecting trust in AI denies the trust attitudes that some people experience. Secondly, we can trust other non‐human entities, such as animals and institutions, so why can we not trust AI (...) systems? Finally, human–AI trust is criticized based on a conception of human–human trust, which does not recognize the distinctiveness of the human–AI relationship. This article aims to refute these counterarguments based on the genealogical analyses of ‘trust’ and ‘trustworthiness’ of Karen Jones and Thomas Simpson, who show that trust and trustworthiness help to overcome vulnerabilities. This function of trust gives reason to use human–human trust as a standard. For this function, it is important that trustees are responsive to trust. While animals and institutions could be responsive, narrow AI systems are unable to be responsive to trust. Therefore, we should not apply trust to AI and instead direct our trust to those who can be responsive to and held responsible for our trust. (shrink)

In this chapter, we integrate three distinct strands of thought to argue that the concept of “fairness” varies significantly across cultures. As a result, ensuring that human-AI interactions meet relevant fairness standards requires a deep understanding of the cultural contexts in which AI-enabled systems are deployed. Failure to do so will not only result in the generation of unfair outcomes by an AI-enabled system, but it will also degrade legitimacy of and trust in the system. The first strand concerns (...) the dominant approach taken in the technology industry to ensure that AI-enabled systems are fair. This approach is to reduce fairness to some mathematical formalism that can be applied universally, a quintessentially Western conception of fairness. The second strand concerns alternative conceptions of fairness that have their roots in philosophical traditions of the East, namely Confucian virtue ethics. Understanding how individuals from diverse cultural backgrounds perceive fairness—particularly their beliefs about fairness in human-to-human interactions—is crucial for understanding how they will interpret fairness in human-AI interactions. Building on these philosophical and behavioral differences, as highlighted by empirical research, the third strand integrates insights from political science and interdisciplinary studies. This perspective offers valuable guidance on designing AI-enabled systems to align with contextually relevant standards of fairness. Examining existing beliefs about fairness within the context of institutional decision-making provides valuable insights into what people expect from AI-generated decisions. These expectations often include key elements such as a sufficient degree of transparency, clear lines of accountability, and mechanisms to contest decisions made by the system—all of which are essential components of procedural fairness. Rather than adopting a one-size-fits-all approach to ensuring fairness, the design and deployment of AI systems must carefully account for the operating environment, including the socio-political and cultural context, to ensure that the system aligns with the relevant standards of fairness. (shrink)

In “What Are Humans Doing in the Loop? Co-Reasoning and Practical Judgment When Using Machine Learning-Driven Decision Aids,” Salloch and Eriksen (2024) address the tension between algorithm explai...

The rapid integration of these artificial intelligence (AI) tools into our daily lives is reshaping how we think and make decisions. We propose that data-driven AI systems, by transcending individual artefacts and interfacing with a dynamic, multiartefact ecosystem, constitute a distinct psychological system. We call this ‘system 0’, and position it alongside Kahneman’s system 1 (fast, intuitive thinking) and system 2 (slow, analytical thinking).System 0 represents the outsourcing of certain cognitive tasks to AI, which can process vast amounts of data (...) and perform complex computations beyond human capabilities. It emerges from the interaction between users and AI systems, which creates a dynamic, personalized interface. Authors: Massimo Chiriatti, Marianna Ganapini, Enrico Panai, Mario Ubiali & Giuseppe Riva. (shrink)

As technology improves, artificial systems are increasingly able to behave in human-like ways: holding a conversation; providing information, advice, and support; or taking on the role of therapist, teacher, or counsellor. This enhanced behavioural complexity, we argue, encourages deeper forms of affective engagement on the part of the human user, with the artificial agent helping to stabilise, subdue, prolong, or intensify a person’s emotional condition. Here, we defend a fictionalist account of human/AI interaction, according to which these (...) encounters involve an elaborate practise of imaginative pretence: a make-believe in which the artificial agent is attributed a life of its own. We attend, specifically, to the temporal characteristics of these fictions, and to what we imagine artificial agents are doing when we are not looking at them. (shrink)

Imagine a man chatting with his AI girlfriend app. He looks at his smartphone and says, ‘Finally, I'm being understood’. Is he deceiving himself? Is there anything morally wrong with it? The human tendency to anthropomorphize AI is well established, and the popularity of AI companions is growing. This article answers three questions: (1) How can being charmed by AI's simulated emotions be considered self‐deception? (2) Why might we have an obligation to avoid harmless self‐deception? (3) When is self‐deception (...) in emotional relationships with AI morally questionable, and can it be blameworthy? Regarding question 1, I describe being seduced by AI's simulated emotions as self‐deception, where desires bias beliefs. In response to question 2, I outline two ways to justify a prima facie obligation to avoid harmless self‐deception – instrumental and autotelic. For question 3, I highlight crucial factors to consider in assessing the blameworthiness of harmless self‐deception, such as the emotional and cognitive competences of the self‐deceiver, reasons for self‐deception, and its consequences for one's predispositions, self‐image, and other people. Moreover, I argue that the ethical requirement to avoid self‐deception does not easily translate into attributing blame to others for being self‐deceived. (shrink)

The idea that humans might one day form persistent and dynamic relationships in professional, social, and even romantic contexts is a longstanding one. However, developments in machine learning and especially natural language processing over the last five years have led to this possibility becoming actualised at a previously unseen scale. Apps like Replika, Xiaoice, and CharacterAI boast many millions of active long-term users, and give rise to emotionally complex experiences. In this paper, I provide an overview of these developments, beginning (...) in Section 1 with historical and technical context. In Section 2, I lay out a basic theoretical framework for classifying human-AI relationships and their specific dynamics. Section 3 turns to ethical issues, with a focus on the core philosophical question of whether human-AI relationships can have similar intrinsic value to that possessed by human-human relationships. Section 4 extends to the discussion of ethical issues to the more empirical matter of harms and benefits of human-AI relationships. The paper concludes by noting potentially instructive parallels between the nascent field of ‘Social AI’ and the recent history of social media. (shrink)

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. (shrink)

In recent years, research in robotics and artificial intelligence (AI) has made rapid progress. It is expected that robots and AI will play a part in the field of nursing and their role might broaden in the future. However, there are areas of nursing practice that cannot or should not be entrusted to robots and AI, because nursing is a highly humane practice, and therefore, there would, perhaps, be some practices that should not be replicated by robots or AI. Therefore, (...) this paper focuses on several ethical concepts (advocacy, accountability, cooperation, and caring) that are considered important in nursing practice, and examines whether it is possible to implement these ethical concepts in robots and AI by analyzing the concepts and the current state of robotics and AI technology. Advocacy: Among the components of advocacy, safeguarding and apprising can be more easily implemented, while elements that require emotional communication with patients, such as valuing and mediating, are difficult to implement. Accountability: Robotic nurses with explainable AI have a certain level of accountability. However, the concept of explanation has problems of infinite regression and attribution of responsibility. Cooperation: If robot nurses are recognized as members of a community, they require the same cooperation as human nurses. Caring: More difficulties are expected in care-receiving than in caregiving. However, the concept of caring itself is ambiguous and should be explored further. Accordingly, our analysis suggests that, although some difficulties can be expected in each of these concepts, it cannot be said that it is impossible to implement them in robots and AI. However, even if it were possible to implement these functions in the future, further study is needed to determine whether such robots or AI should be used for nursing care. In such discussions, it will be necessary to involve not only ethicists and nurses but also an array of society members. (shrink)

In the wake of recent advancements in the field of AI, this paper investigates the impact of recommender systems and generative models on human decisional and creative autonomy. For this purpose, we adopt Dennett’s conception of autonomy as self-control. We show that recommender systems can play a double role in relation to decisional autonomy: as information filter, they can augment self-control in decision-making, but also act as mechanisms of remote control that clamp degrees of freedom. As for generative models (...) in AI, we show that they can be seen as a powerful system of selection and suggestion (similar to standard recommender systems) but also as an instrument for information production. We suggest that the latter perspective opens new possibilities in terms of creative autonomy. Additionally, for both systems we propose a distinction between “extrinsic” and “intrinsic” mechanisms and effects. Through Dennett’s theory of self-control, this paper offers new insights into the relation between AI and human autonomy by framing it in terms of remote or self-control and by addressing the impact of generative models on creative autonomy. (shrink)

Predictability is a prerequisite for effective human control of artificial intelligence (AI). The inability to predict malfunctioning of AI, for example, impedes timely human intervention. In this paper, we empirically investigate how AI’s predictability compares to the predictability of humans in a real-effort task. We show that humans are worse at predicting AI performance than at predicting human performance. Importantly, participants are not aware of the differences in relative predictability of AI and overestimate their prediction skills. These (...) results raise doubts about the human ability to effectively exercise control of AI — at least in certain contexts. (shrink)

Using artificial intelligence (AI) to make decisions in human resource management (HRM) raises questions of how fair employees perceive these decisions to be and whether they experience respectful treatment (i.e., interactional justice). In this experimental survey study with open-ended qualitative questions, we examine decision making in six HRM functions and manipulate the decision maker (AI or human) and decision valence (positive or negative) to determine their impact on individuals’ experiences of interactional justice, trust, dehumanization, and perceptions of decision-maker (...) role appropriate- ness. In terms of decision makers, the use of human decision makers over AIs generally resulted in better perceptions of respectful treatment. In terms of decision valence, people experiencing positive over negative decisions generally resulted in better perceptions of respectful treatment. In instances where these cases conflict, on some indicators people preferred positive AI decisions over negative human decisions. Qualitative responses show how people identify justice concerns with both AI and human decision making. We outline implications for theory, practice, and future research. (shrink)

Even though morally competent artificial agents have yet to emerge in society, we need insights from empirical science into how people will respond to such agents and how these responses should inform agent design. Three survey studies presented participants with an artificial intelligence agent, an autonomous drone, or a human drone pilot facing a moral dilemma in a military context: to either launch a missile strike on a terrorist compound but risk the life of a child, or to cancel (...) the strike to protect the child but risk a terrorist attack. Seventy-two percent of respondents were comfortable making moral judgments about the AI in this scenario and fifty-one percent were comfortable making moral judgments about the autonomous drone. These participants applied the same norms to the two artificial agents and the human drone pilot. However, people ascribed different patterns of blame to humans and machines as a function of the agent’s decision of how to solve the dilemma. These differences in blame seem to stem from different assumptions about the agents’ embeddedness in social structures and the moral justifications those structures afford. Specifically, people less readily see artificial agents as embedded in social structures and, as a result, they explained and justified their actions differently. As artificial agents will perform many actions with moral significance, we must heed such differences in justifications and blame and probe how they affect our interactions with those agents. (shrink)

Despite the importance of human blood to clinical practice and research, hematology and blood transfusion data remain scattered throughout a range of disparate sources. This lack of systematization concerning the use and definition of terms poses problems for physicians and biomedical professionals. We are introducing here the Blood Ontology, an ongoing initiative designed to serve as a controlled vocabulary for use in organizing information about blood. The paper describes the scope of the Blood Ontology, its stage of development and (...) some of its anticipated uses. (shrink)

Can AI solve the ethical, moral, and political dilemmas of warfare? How is artificial intelligence (AI)-enabled warfare changing the way we think about the ethical-political dilemmas and practice of war? This article explores the key elements of the ethical, moral, and political dilemmas of human-machine interactions in modern digitized warfare. It provides a counterpoint to the argument that AI “rational” efficiency can simultaneously offer a viable solution to human psychological and biological fallibility in combat while retaining “meaningful” (...) class='Hi'>human control over the war machine. This Panglossian assumption neglects the psychological features of human-machine interactions, the pace at which future AI-enabled conflict will be fought, and the complex and chaotic nature of modern war. The article expounds key psychological insights of human-machine interactions to elucidate how AI shapes our capacity to think about future warfare's political and ethical dilemmas. It argues that through the psychological process of human-machine integration, AI will not merely force-multiply existing advanced weaponry but will become de facto strategic actors in warfare – the “AI commander problem.”. (shrink)

Recent advances in AI raise the possibility that AI systems will one day be able to do anything humans can do, only better. If artificial general intelligence (AGI) is achieved, AI systems may be able to understand, reason, problem solve, create, and evolve at a level and speed that humans will increasingly be unable to match, or even understand. These possibilities raise a natural question as to whether AI will eventually become superior to humans, a successor “digital species”, with a (...) rightful claim to assume leadership of the universe. However, a deeper consideration suggests the overlooked differentiator between human beings and AI is not the brain, but the central nervous system (CNS), providing us with an immersive integration with physical reality. It is our CNS that enables us to experience emotion including pain, joy, suffering, and love, and therefore to fully appreciate the consequences of our actions on the world around us. And that emotional understanding of the consequences of our actions is what is required to be able to develop sustainable ethical systems, and so be fully qualified to be the leaders of the universe. A CNS cannot be manufactured or simulated; it must be grown as a biological construct. And so, even the development of consciousness will not be sufficient to make AI systems superior to humans. AI systems may become more capable than humans on almost every measure and transform our society. However, the best foundation for leadership of our universe will always be DNA, not silicon. (shrink)

The common “human in the loop” narrative in artificial intelligence (AI) implementation is in critical need of analysis and explanation, as Salloch and Eriksen (2024) rightfully argue. Researchers...

Time and technology permeate the fabric of teamwork across a variety of settings to affect outcomes which have a wide range of consequences. However, there is a limited understanding about the interplay between these factors for teams, especially as applied to artificial intelligence (AI) technology. With the increasing integration of AI into human teams, we need to understand how environmental factors such as time scarcity interact with AI technology to affect team behaviors. To address this gap in the literature, (...) we investigated the interaction between the availability of intelligent technology and time scarcity in teams. Drawing from the theoretical perspective of computers are social actors and extant research on the use of heuristics and human–AI interaction, this study uses behavioral data from 56 teams who participated in a between-subjects 2 (intelligent assistant available × control/no intelligent assistant) × 2 (time scarcity × control/no time scarcity) lab experiment. Results show that teams working under time scarcity used the intelligent assistant more often and underperformed on a creative task compared to teams without the temporal constraints. Further, teams who had an intelligent assistant available to them had fewer interactions between members compared to teams who did not have the technology. Implications for research and applications are discussed. (shrink)

Using an interdisciplinary approach, this book explores the emerging topics and rapid technological developments of robotics and artificial intelligence through the lens of the evolving role of sex robots, and how they should best be designed to serve human needs. An international panel of authors provides the most up-to-date, evidence-based empirical research on the potential sexual applications of artificial intelligence. Early chapters discuss the objections to sexual activity with robots while also providing a counterargument to each objection. Subsequent chapters (...) present the implications of robot sex as well as the security and data privacy issues associated with sexual interactions with artificial intelligence. The book concludes with a chapter highlighting the importance of a scientific, multidisciplinary approach to the study of human - robot sexuality. Topics featured in this book include: The Sexual Interaction Illusion Model. The personal companion system, Harmony, designed by RealbotixTM. An exposition of the challenges of personal data control and protection when dealing with artificial intelligence. The current and future technological possibilities of projecting three-dimensional holograms. Expert discussion notes from an international workshop on the topic. AI Love You will be of interest to academic researchers in psychology, robotics, ethics, medical science, sociology, gender studies as well as clinicians, policy makers, and the business sector. (shrink)

This article provides a course of correction in the discourse surrounding human-centric AI by elucidating the philosophical underpinning that serves to create a view that AI is divorced from human-centric values. Next, we espouse the need to explicitly designate stakeholder- or community-centric values which are needed to resolve the issue of alignment. To achieve this, we present two frameworks, Ubuntu and maximum feasible participation. Finally, we demonstrate how employing the aforementioned frameworks in AI can benefit society by flattening (...) the current top-down social hierarchies as AI is currently being utilized. Implications are discussed. (shrink)

Many artificial intelligence (AI) systems currently used for decision-making are opaque, i.e., the internal factors that determine their decisions are not fully known to people due to the systems’ computational complexity. In response to this problem, several researchers have argued that human decision-making is equally opaque and since simplifying, reason-giving explanations (rather than exhaustive causal accounts) of a decision are typically viewed as sufficient in the human case, the same should hold for algorithmic decision-making. Here, I contend that (...) this argument overlooks that human decision-making is sometimes significantly more transparent and trustworthy than algorithmic decision-making. This is because when people explain their decisions by giving reasons for them, this frequently prompts those giving the reasons to govern or regulate themselves so as to think and act in ways that confirm their reason reports. AI explanation systems lack this self-regulative feature. Overlooking it when comparing algorithmic and human decision-making can result in underestimations of the transparency of human decision-making and in the development of explainable AI that may mislead people by activating generally warranted beliefs about the regulative dimension of reason-giving. (shrink)

:Any space program involving long-term human missions will have to cope with serious risks to human health and life. Because currently available countermeasures are insufficient in the long term, there is a need for new, more radical solutions. One possibility is a program of human enhancement for future deep space mission astronauts. This paper discusses the challenges for long-term human missions of a space environment, opening the possibility of serious consideration of human enhancement and a (...) fully automated space exploration, based on highly advanced AI. The author argues that for such projects, there are strong reasons to consider human enhancement, including gene editing of germ line and somatic cells, as a moral duty. (shrink)

As AI-driven solutions continue to revolutionise the tech industry, scholars have rightly cautioned about the risks of ‘ethics washing’. In this paper, we make a case for adopting a human rights-based ethical framework for regulating AI. We argue that human rights frameworks can be regarded as the common denominator between law and ethics and have a crucial role to play in the ethics-based legal governance of AI. This article examines the extent to which human rights-based regulation has (...) been achieved in the primary example of legislation regulating AI governance, i.e., the EU AI Act 2024/1689. While the AI Act has a firm commitment to protect human rights, which in the EU legal order have been given expression in the Charter of Fundamental Rights, we argue that this alone does not contain adequate guarantees for enforcing some of these rights. This is because issues such as EU competence and the principle of subsidiarity make the idea of protection of fundamental rights by the EU rather than national constitutions controversial. However, we argue that human rights-based, ethical regulation of AI in the EU could be achieved through contextualisation within a values-based framing. In this context, we explore what are termed ‘European values’, which are values on which the EU was founded, notably Article 2 TEU, and consider the extent to which these could provide an interpretative framework to support effective regulation of AI and avoid ‘ethics washing’. (shrink)

The use of Artificial Intelligence and Big Data in health care opens up new opportunities for the measurement of the human. Their application aims not only at gathering more and better data points but also at doing it less invasive. With this change in health care towards its extension to almost all areas of life and its increasing invisibility and opacity, new questions of transparency arise. While the complex human-machine interactions involved in deploying and using AI tend to (...) become non-transparent, the use of these technologies makes the patient seemingly transparent. Papers on the ethical implementation of AI plead for transparency but neglect the factor of the “transparent patient” as intertwined with AI. Transparency in this regard appears to be Janus-faced: The precondition for receiving help - e.g., treatment advice regarding the own health - is to become transparent for the digitized health care system. That is, for instance, to donate data and become visible to the AI and its operators. The paper reflects on this entanglement of transparent patients and (non-) transparent technology. It argues that transparency regarding both AI and humans is not an ethical principle per se but an infraethical concept. Further, it is no sufficient basis for avoiding harm and human dignity violations. Rather, transparency must be enriched by intelligibility following Judith Butler’s use of the term. Intelligibility is understood as an epistemological presupposition for recognition and the ensuing humane treatment. Finally, the paper highlights ways to testify intelligibility in dealing with AI in health care ex ante, ex post, and continuously. (shrink)

The use of a ‘human-centred’ artificial intelligence approach (HCAI) has substantially increased over the past few years in academic texts (1600 +); institutions (27 Universities have HCAI labs, such as Stanford, Sydney, Berkeley, and Chicago); in tech companies (e.g., Microsoft, IBM, and Google); in politics (e.g., G7, G20, UN, EU, and EC); and major institutional bodies (e.g., World Bank, World Economic Forum, UNESCO, and OECD). Intuitively, it sounds very appealing: placing human concerns at the centre of AI development (...) and use. However, this paper will use insights from the works of Michel Foucault (mostly _The Order of Things_) to argue that the HCAI approach is deeply problematic in its assumptions. In particular, this paper will criticise four main assumptions commonly found within HCAI: human–AI hybridisation is desirable and unproblematic; humans are not currently at the centre of the AI universe; we should use humans as a way to guide AI development; AI is the next step in a continuous path of human progress; and increasing human control over AI will reduce harmful bias. This paper will contribute to the field of philosophy of technology by using Foucault's analysis to examine assumptions found in HCAI [it provides a Foucauldian conceptual analysis of a current approach (human-centredness) that aims to influence the design and development of a transformative technology (AI)], it will contribute to AI ethics debates by offering a critique of human-centredness in AI (by choosing Foucault, it provides a bridge between older ideas with contemporary issues), and it will also contribute to Foucault studies (by using his work to engage in contemporary debates, such as AI). (shrink)

Artificial Intelligence (AI) is transforming our world: today machines not only can mimic human actions but out-perform human agents in many activities, including learning and thinking. AI offers revolutionary solutions and new possibilities in transportation, business, communication, medicine, law, and other domains. While some welcome this brave new world, others fear the threats AI pose to people’s livelihoods, social relations, individuality, freedom, and perhaps even the very survival of the human species. No doubt some of this existential (...) angst is exaggerated, but AI does raise questions for our understanding of the world and ourselves that require serious reflection, including questions about adequacy of education in various aspects. This chapter offers, from the perspectives of John Dewey’s Pragmatism as well as Confucianism, some reflections on the role of the humanities in education in response to the opportunities and challenges of the development and widespread use of AI. It shows that Dewey and Confucius share similar views regarding the humanistic purpose of education and their philosophies of education offer arguments for why humanities education will be relevant, if not more important, when many jobs we are familiar with become obsolete. Their attitudes toward the economic motive in education will help us rethink the meaning of work in a “world without work.” At the same time, they offer a critical evaluation of contemporary humanities education, which have failed to realize their visions of personal cultivation and growth. Among its failings is the continued dichotomy between the humanities and the sciences. In the age of AI, it has become even more vital to integrate them to avoid science and technology from becoming materialistic and anti-human while the humanities become merely literary and without any means to transform the world. (shrink)

Autonomy has been central to moral and political philosophy for millenia, and has been positioned as a critical aspect of both justice and wellbeing. Research in psychology supports this position, providing empirical evidence that autonomy is critical to motivation, personal growth and psychological wellness. Responsible AI will require an understanding of, and ability to effectively design for, human autonomy (rather than just machine autonomy) if it is to genuinely benefit humanity. Yet the effects on human autonomy of digital (...) experiences are neither straightforward nor consistent, and are complicated by commercial interests and tensions around compulsive overuse. This multi-layered reality requires an analysis that is itself multidimensional and that takes into account human experience at various levels of resolution. We borrow from HCI and psychological research to apply a model (“METUX”) that identifies six distinct spheres of technology experience. We demonstrate the value of the model for understanding human autonomy in a technology ethics context at multiple levels by applying it to the real-world case study of an AI-enhanced video recommender system. In the process we argue for the following three claims: 1) There are autonomy-related consequences to algorithms representing the interests of third parties, and they are not impartial and rational extensions of the self, as is often perceived; 2) Designing for autonomy is an ethical imperative critical to the future design of responsible AI; and 3) Autonomy-support must be analysed from at least six spheres of experience in order to approriately capture contradictory and downstream effects. (shrink)

My aim in this article is to set out some counter-intuitive claims about the challenges posed by artificial intelligence (AI) applications to the protection and enjoyment of human rights and to be your guide through my unorthodox ideas. While there are familiar human rights issues raised by AI and its applications, these are perhaps the easiest of the challenges because they are already recognized by the human rights regime as problems. Instead, the more pernicious challenges are those (...) that have yet to be identified or articulated, because they arise from new affordances rather than directly through AI modeled as a technology. I suggest that we need to actively explore the potential problem space on this basis. I suggest that we need to adopt models and metaphors that systematically exclude the possibility of applying the human rights regime to AI applications. This orientation will present us with the difficult, intractable problems that most urgently require responses. There are convincing ways of understanding AI that lock out the very possibility for human rights responses and this should be grounds for serious concern. I suggest that responses need to exploit both sets of insights I present in this paper: first that proactive and systematic searches of the potential problem space need to be continuously conducted to find the problems that require responses; and second that the monopoly that the human rights regime holds with regards to addressing harm and suffering needs to be broken so that we can deploy a greater range of barriers against failures to recognize and remedy AI-induced wrongs. (shrink)

1. Michael A PetersBeijing Normal UniversityChatGPT is an AI chatbot released by OpenAI on November 30, 2022 and a ‘stable release’ on February 13, 2023. It belongs to OpenAI’s GPT-3 family (genera...