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)

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)

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)

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)

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)

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)

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)

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)

This paper models the game process of the value cocreation of enterprises based on evolutionary game theory. The factors influencing value cocreation are found through mathematical analysis. Taking iFLYTEK as an example, a representative enterprise of artificial intelligence in China, six factors affecting value cocreation are verified, which are the excess return rate, the distribution coefficient of the excess return rate, coordination costs in the system, the cost-sharing coefficient, imitation costs, and penalties. These six factors have a (...) profound impact on value cocreation in the ecosystem. Through the case study of iFLYTEK, it is concluded that innovation ecosystems can enable small- and medium-sized AI enterprises to grow. In order to build a sound ecosystem, we need to establish a mechanism to select partners, reduce the costs of cooperation, and strengthen the protection of intellectual property. At the beginning of the cooperation, it is necessary to establish a mechanism with clear responsibilities, rights, and interests. The conclusion is of great significance to the development of AI enterprises. (shrink)

Artificial Intelligence (AI) and machine learning (ML) are rapidly evolving and have already had major impacts on military capabilities in the battlefield, making new kinds of tools and tactics available. A less examined area of application for AI in a military context, however, is its impact on human strategic decision making. This article focuses on the more subtle cognitive influences of AI and how they can be strategically deployed to aid decision making around the state-level resort to force, in (...) particular. I will argue that AI-driven technologies can be used to improve certain critical cognitive resources (e.g. memory, planning, mind-modelling, etc.) of decision makers, thereby providing valuable strategic advantages to those actors who use them successfully. At the same time, I will also caution against the risks of human decision makers becoming overly reliant on AI-support systems. Both the potential advantages and risks are areas that demand further study and consideration. (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 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 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)

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)

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 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)

In 2020, the launch of several high-profile generative AI tools for text, image, and video marked a significant shift in public engagement with artificial intelligence. The moment of the shift is captured by a GPT-3-authored article in _The Guardian_ provocatively titled “A robot wrote this entire article. Are you scared yet, human?” (GPT-3 2020 ). Since 2020, AI tools have become increasingly ubiquitous, eliciting both concern over potential societal threats and excitement for their progressive possibilities. In artistic circles, a (...) pressing question emerged and continues to persist: what forms will culture, art, artists, experiences, and audiences take in a future where AI is omnipresent? Although the partnership between artists and AI predates such recent enquiry, the newfound accessibility of AI tools has thrust art and the artist’s evolving role into the spotlight. Numerous studies dive into the creative capabilities of AI and navigate the implications of AI-led art. This article attempts to pivot the conversation to investigate the phenomenological aspects of human–AI aesthetics. Of particular interest here is the spectator experience of AI-led art within the larger context of human–AI relationships. By examining Sougwen Chung’s _Assembly Lines_ and Sofia Crespo’s _Neural Zoo_, I interrogate the phenomenology of spectatorship in human–AI collaborative aesthetics, shedding light on the nuanced human–AI connections within cultural and domestic realms. (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)

How to embed reflexivity in public participation in techno-science and to open it up to the agency of publics are key concerns in current debates. There is a risk that engagements become limited to “laboratory experiments,” highly controlled and foreclosed by participation experts, particularly in upstream techno-sciences. In this paper, we propose a way to open up the “participation laboratory” by engaging localized, self-assembling publics in ways that respect and mobilize their ecologies of participation. Our innovative reflexive methodology introduced participatory (...) methods to public engagement with upstream techno-science, with the public contributing to both the content and format of the project. Reflecting on the project, we draw attention to the largely overlooked issue of temporalities of participation, and the co-production of futures and publics in participation methodologies. We argue that many public participation methodologies are underpinned by the open futures model, which imagines the future as a space of unrestrained creativity. We contrast that model with the lived futures model typical of localized publics, which respects latency of materials and processes but imposes limits on creativity. We argue that to continue being societally relevant and scientifically important, public participation methods should reconcile the open future of research with the lived futures of localized publics. (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)

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)

The purpose of this paper is to show an application of variety engineering in the social realm. It focuses on reducing environmental complexity by catalysing self-organizing processes. This catalysis is based on the use of Sen and Nussbaum’s capabilities approach. By doing this an organization may improve the quality of the relations with their clients by transforming environmental agents into new suppliers. This approach opens a new dimension of social responsibility for organizations. A particular case is presented in which a (...) regional university in Colombia faces the challenge of attending low-income students coming from small municipalities. They designed a strategy to offer low-cost housing and a daily balanced meal without using the university's own resources. Instead, they managed to involve some agents of the community by developing their capabilities. This kind of variety engineering shows the possibility of new forms of social responsibility in universities by incorporating the capabilities approach into their managerial practices. This is something that is currently under study in other universities. (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)

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)

Volume 25, Issue 3, March 2025, Page 148-150.

In the course of recent investigations on artificial intelligence (AI) and its scope in different societal domains and industries, two notable research frontiers have taken center stage: the growing exploration of the interactive relationship between humans and increasingly intelligent systems and the renewed emphasis on integrating a range of social science and humanities perspectives within AI research. This surge in interest, coupled with the proliferation of publications and diverse terminologies, has led to a complex landscape where theoretical inconsistency and conceptual (...) confusion abound. In response, this work aims to bridge critical gaps by (1) exploring how recent studies in the field of human–AI interaction define their terminologies and fundamental concepts (i.e., knowledge, intelligence, and learning) and (2) developing a novel conceptual overview that facilitates navigation through the diversity of concepts, including new paradigms in AI that integrate perspectives from social sciences and humanities. Our findings reveal that researchers in this cross-sectional field understand that intelligence is more frequently seen as intricately linked with knowledge, with a mutually causal relationship between the two. Despite the proliferation of terminologies across different areas of expertise, such as human-centric design, human–AI collaborative arrangements, and synergy as a process of mutual enhancement, the theoretical distinctions among them remain unclear. To address these complexities, we developed a conceptual overview that functions as a paradigm matrix, enabling the identification of tensions arising from the integration of multiple paradigms across different theories and epistemologies from social sciences and humanities. This conceptual overview is designed not to homogenize these perspectives, but to foster dialogue and reflection, allowing researchers to appreciate and leverage the unique strengths and insights each paradigm offers. This paradigm matrix promises to enhance our understanding of the dynamic and multifaceted interaction between humans and AI, paving the way for more coherent and impactful research in this rapidly evolving field. (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)

AI companies are racing to create artificial general intelligence, or “AGI.” If they succeed, the result will be human-level AI systems that can independently pursue high-level goals by formulating and executing long-term plans in the real world. Leading AI researchers agree that some of these systems will likely be “misaligned”–pursuing goals that humans do not desire. This goal mismatch will put misaligned AIs and humans into strategic competition with one another. As with present-day strategic competition between nations with incompatible (...) goals, the result could be violent and catastrophic conflict. Existing legal institutions are unprepared for the AGI world. New foundations for AGI governance are needed, and the time to begin laying them is now, before the critical moment arrives. -/- This Article begins to lay those new legal foundations. It is the first to think systematically about the dynamics of strategic competition between humans and misaligned AGI. The Article begins by showing, using formal game-theoretic models, that, by default, humans and AIs will be trapped in a prisoner’s dilemma. Both parties’ dominant strategy will be to permanently disempower or destroy the other, even though the costs of such conflict would be high. -/- The Article then argues that a surprising legal intervention could transform the game theoretic equilibrium and avoid conflict: AI rights. Not just any AI rights would promote human safety. Granting AIs the right not to be needlessly harmed–as humans have granted to certain non-human animals–would, for example, have little effect. Instead, to promote human safety, AIs should be given those basic private law rights–to make contracts, hold property, and bring tort claims–that law already extends to non-human corporations. Granting AIs these economic rights would enable long-run, small-scale, mutually-beneficial transactions between humans and AIs. This would, we show, facilitate a peaceful strategic equilibrium between humans and AIs for the same reasons economic interdependence tends to promote peace in international relations. Namely, the gains from trade far exceed those from war. Throughout, we argue that human safety, rather than AI welfare, provides the right framework for developing AI rights. This Article explores both the promise and the limits of AI rights as a legal tool for promoting human safety in an AGI world. (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...

Evidence suggests divergent thinking is the cognitive basis of creative thoughts. Neuroimaging literature using resting-state functional connectivity has revealed network reorganizations during divergent thinking. Recent studies have revealed the changes of network organizations when performing creativity tasks, but such brain reconfigurations may be prolonged after task and be modulated by the trait of creativity. To investigate the dynamic reconfiguration, 40 young participants were recruited to perform consecutive Alternative Uses Tasks for divergent thinking and two resting-state scans were used for mapping (...) the brain reorganizations after AUT. We split participants into high- and low-creative groups based on creative achievement questionnaire and targeted on reconfigurations of the two brain networks: default-mode network and the network seeded at the left inferior frontal gyrus because the between-group difference of AUT-induced brain activation located at the left IFG. The changes of post-AUT RSFCs indicated the prolonged effect of divergent thinking. More specifically, the alterations of RSFCIFG−AG and RSFCIFG−IPL in the high-creative group had positive relationship with their AUT performances, but not found in the low-creative group. Furthermore, the RSFC changes of DMN did not present significant relationships with AUT performances. The findings not only confirmed the possibility of brain dynamic reconfiguration following divergent thinking, but also suggested the distinct IFGN reconfiguration between individuals with different creativity levels. (shrink)

GraphicalPatients with early- to middle-stage PD were enrolled for C-Gait assessment and traditional walking ability assessments. The correlation of C-Gait assessment and traditional walking tests were studied. Two models were established based on C-Gait assessment and traditional walking tests to explore the value of C-Gait assessment in predicting freezing of gait.ObjectiveEfficient methods for assessing walking adaptability in individuals with Parkinson’s disease are urgently needed. Therefore, this study aimed to assess C-Gait for detecting freezing of gait in patients with early- to (...) middle-stage PD.MethodPeople with PD diagnosis were recruited from April 2019 to November 2019 in Beijing Rehabilitation Hospital. The participants performed six items of walking adaptability on an instrumented treadmill augmented with visual targets and obstacles. The patient’s walking adaptability was evaluated by C-Gait assessment and traditional walking tests, and FOG-related indexes were collected as outcome measures. Two discriminant models were established by stepwise discriminant analysis; area under the receiver operating characteristic curve was used to validate the models.ResultIn total, 53 patients were included in this study. Most C-Gait assessment items had no or low correlations with traditional walking tests. The obstacle avoidance and speed of adaptation items could lead to FOG with high sensitivity. In addition, the C-Gait assessment model had slightly better discrimination of freezers from non-freezers compared with traditional walking test models ; specifically, obstacle avoidance and speed of adaptation have uniquely discriminant potential.ConclusionC-gait assessment could provide additional value to the traditional walking tests for PD. Gait adaptability assessment, as measured by C-Gait, may be able to help identify freezers in a PD population. (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)

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)

Language is the foundational grammar of human life. As an evolutionary feat, the development of language in Homo Sapien societies meant an elaborate structure of intricate discourse formation through fiction and myths, which naturalises over time to build a system of culture. The narrativisation of fiction involves complex devices that require meticulous examination to understand cultural codes and their role in shaping collective ideology. Language as a means of interaction with non-human entities like Artificial Intelligence as depicted in (...) films, also plays a vital role in shaping the perception of the self and identity. Employing Deleuze and Guattari’s notion of “becoming” and the rhizomatic self, this paper will study the dialogues and narrativity of Jonze’s Her to critically analyse the fluid transformation of Theodore through his interaction with the operating system Samantha, reflecting on the broader implications for human-AI interaction and identity formation. (shrink)

This paper offers the first careful analysis of the possibility that AI and humanity will go to war. The paper focuses on the case of artificial general intelligence, AI with broadly human capabilities. The paper uses a bargaining model of war to apply standard causes of war to the special case of AI/human conflict. The paper argues that information failures and commitment problems are especially likely in AI/human conflict. Information failures would be driven by the difficulty of (...) measuring AI capabilities, by the uninterpretability of AI systems, and by differences in how AIs and humans analyze information. Commitment problems would make it difficult for AIs and humans to strike credible bargains. Commitment problems could arise from power shifts, rapid and discontinuous increases in AI capabilities. Commitment problems could also arise from missing focal points, where AIs and humans fail to effectively coordinate on policies to limit war. In the face of this heightened chance of war, the paper proposes several interventions. War can be made less likely by improving the measurement of AI capabilities, capping improvements in AI capabilities, designing AI systems to be similar to humans, and by allowing AI systems to participate in democratic political institutions. (shrink)

We cooperate with other people despite the risk of being exploited or hurt. If future artificial intelligence (AI) systems are benevolent and cooperative toward us, what will we do in return? Here we show that our cooperative dispositions are weaker when we interact with AI. In nine experiments, humans interacted with either another human or an AI agent in four classic social dilemma economic games and a newly designed game of Reciprocity that we introduce here. Contrary to the hypothesis (...) that people mistrust algorithms, participants trusted their AI partners to be as cooperative as humans. However, they did not return AI's benevolence as much and exploited the AI more than humans. These findings warn that future self-driving cars or co-working robots, whose success depends on humans' returning their cooperativeness, run the risk of being exploited. This vulnerability calls not just for smarter machines but also better human-centered policies. (shrink)

Does AI conform to humans, or will we conform to AI? An ethical evaluation of AI-intensive companies will allow investors to knowledgeably participate in the decision. The evaluation is built from nine performance indicators that can be analyzed and scored to reflect a technology’s human-centering. When summed, the scores convert into objective investment guidance. The strategy of incorporating ethics into financial decisions will be recognizable to participants in environmental, social, and governance investing, however, this paper argues that conventional ESG (...) frameworks are inadequate for AI-intensive companies. To fully account for contemporary technology, the following categories of evaluation will be developed and featured as vital investing criteria: autonomy, dignity, privacy, performance. With these priorities established, the larger goal is a model for humanitarian investing in AI-intensive companies that is intellectually robust, manageable for analysts, useful for portfolio managers, and credible for investors. (shrink)

The hypergrowth and hyperconnectivity of networks of artificial intelligence (AI) systems and algorithms increasingly cause our interactions with the world, socially and environmentally, more technologically mediated. AI systems start interfering with our choices or making decisions on our behalf: what we see, what we buy, which contents or foods we consume, where we travel to, who we hire, etc. It is imperative to understand the dynamics of human-computer interaction in the age of progressively more competent AI. This essay presents (...) five premises to serve as starting points for building our understanding of the “interactions’ in human-computer interactions. (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)

This paper delves into the significance and challenges of Artificial Intelligence (AI) ethics and justice in terms of Common Good and Well-being, fairness and non-discrimination, rational public deliberation, and autonomy and control. Initially, the paper establishes the groundwork for subsequent discussions using the Academia Sinica LLM incident and the AI Technology R&D Guidelines of the National Science and Technology Council(NSTC) as a starting point. In terms of justice and ethics in AI, this research investigates whether AI can fulfill human (...) common interests and welfare. Taking AI injustice as an example, I analyze the practical assessment of AI regarding regional, industrial, and social impacts. Further, this paper discusses the challenges of fairness and non-discrimination in AI, specifically addressing the issue of training on biased data, discussing the acquisition of bias by AI and post-processing supervision issues, and emphasizing the importance of rational public deliberation in this process. Then, this research examines the challenges and countermeasures the rational public faces in public deliberation, such as the importance of education in STEM scientific literacy and technological capabilities. Finally, in discussing AI and autonomy, I propose a 'Human-Centered Approach’ rather than relying solely on the 'Technological Utility Maximization' brought by AI to achieve substantial AI justice. -/- Keywords: AI Ethics and Justice, Fairness and Non-Discrimination, Biased Data Training, Public Deliberation, Autonomy, Human-Centered Approach . (shrink)