Human Agents Research Articles

A Conception of Collaborative Decision Support Systems: Approach and Platform Architecture

The paper describes a general conception of collaborative decision support systems, in which teams providing decision support a) are formed flexibly in accordance with the problem and b) consist of both human experts and intelligent agents implementing AI methods and techniques. An analysis of the key problems of creating collaborative decision support systems based on the collaboration of humans and AI is carried out, the following problems are highlighted: ensuring interoperability (mutual understanding) between heterogeneous team members, reconciling differing positions of participants, ensuring trust between participants, ensuring the effectiveness of joint actions planning and maintaining a balance between predefined workflows and self-organization. Principles for constructing such systems have been formed, offering solutions to the identified problems. In particular, it is proposed to employ an ontology-oriented representation of information about the problem (in the form of multi-aspect ontology), a set of methods for monitoring team activities, reputation scheme, elements of explainable AI, as well as mechanisms of limited self-organization. The proposed concept forms the basis of a software platform for the development of collaborative decision support systems, the main architectural provisions of which are also presented in the paper. The use of the platform is illustrated by an example from the field of rational management of road infrastructure and the creation of a collaborative DSS for the development of measures to reduce road accidents.

The human touch vs. AI efficiency: How perceived status, effort, and loyalty shape consumer satisfaction with preferential treatment

This research investigates the impact of service agent type, human versus artificial intelligence (AI), on consumer satisfaction with preferential treatment. Through four studies conducted in both online and field settings, we demonstrate that consumers experience greater satisfaction when preferential treatment comes from a human agent compared to an AI agent. This effect is driven by two underlying mechanisms: the perception of elevated status and the perception of greater effort exerted by the supplier. Additionally, the difference in consumer satisfaction between agent types diminishes when consumers have strong prior loyalty to the company. These findings offer theoretical implications and managerial insights.

Innovative Design Strategy and Research Progress of Anti Human Papilloma Virus Infection Agents

This review summarized the mechanism of a novel fusion protein (PHPV fusion protein) containing membrane penetrating peptide and oligopeptide-1 (EGF). The new PHPV fusion protein contains two kinds of polypeptides: membrane penetrating peptide and oligopeptide-1, so it has a good function of preventing and treating HPV virus. PHPV fusion protein not only has the multi effect function of membrane penetrating peptide and oligopeptide-1, but also the negative charge (anion) C-terminal of the cross-linked and fused new protein surface interacts with the positive charge (cation) N-terminal on HPV particles. Through the combination of the N-terminal of the positive charge on its surface with the negative charge of the hydrophobic region in the middle and the C-terminal of the recognition region site containing peptidase, the membrane translocation signal is coupled with the NLS of the nuclear localization signal, thus blocking the invasion of human papillomavirus into host cells. Through the interaction of cell membrane, it can penetrate the natural barrier of cell membrane and play a role in destroying the viral membrane shell to inactivate the virus, so as to prevent cervical precancerous lesions and treat HPV infection. PHPV fusion protein can repair damaged cells, enhance the elasticity of loose vaginal mucosa, tighten the inner wall of the vagina, improve the vaginal wetness, and reduce the vaginitis reaction.

Owning Decisions: AI Decision-Support and the Attributability-Gap

Artificial intelligence (AI) has long been recognised as a challenge to responsibility. Much of this discourse has been framed around robots, such as autonomous weapons or self-driving cars, where we arguably lack control over a machine’s behaviour and therefore struggle to identify an agent that can be held accountable. However, most of today’s AI is based on machine-learning technology that does not act on its own, but rather serves as a decision-support tool, automatically analysing data to help human agents make better decisions. I argue that decision-support tools pose a challenge to responsibility that goes beyond the familiar problem of finding someone to blame or punish for the behaviour of agent-like systems. Namely, they pose a problem for what we might call “decision ownership”: they make it difficult to identify human agents to whom we can attribute value-judgements that are reflected in decisions. Drawing on recent philosophical literature on responsibility and its various facets, I argue that this is primarily a problem of attributability rather than of accountability. This particular responsibility problem comes in different forms and degrees, most obviously when an AI provides direct recommendations for actions, but also, less obviously, when it provides mere descriptive information on the basis of which a decision is made.

Situating complexity: information behaviour in the contact zone

Introduction. This short paper addresses the problematic un-situated nature of the holistic framework proposed by Polkinghorne and Given. In 2021, Polkinghorne and Given called for a substantive shift in the information field to a holistic research paradigm. However, framing studies in a contextualised, complex manner has remained an enduring challenge, particularly regarding where information experiences occur. Method. The author identifies a spatial gap in the holistic framework, traces existing literature on place/space in the information field, and explores M. L. Pratt’s contact zone theory. Analysis and results. The holistic paradigm holds an obfuscated view of place/space and its role in shaping information behaviour. Existing studies and theories that incorporate place/space and its impact on information practices underscore the importance of the spatial. The paper looks to contact zone theory as a useful tool for conceptualising information behaviour in particular sites. Conclusion. This paper argues that contact and zone are both key aspects of information behaviour, as information encounters take place in specific sites, between diverse human and nonhuman agents with different levels of power and influence. The contact zone is suggested as a critical lens to examine spatially bound interactions that could offer a more anchored, multi-agentic vision for researching information behaviour.

Sodium-Glucose Co-Transporter-2 Inhibitor Empagliflozin Attenuates Sorafenib-Induced Myocardial Inflammation and Toxicity.

Environmental antineoplastics such as sorafenib may pose a risk to humans through water recycling, and the increased risk of cardiotoxicity is a clinical issue in sorafenib users. Thus, developing strategies to prevent sorafenib cardiotoxicity is an urgent work. Empagliflozin, as a sodium-glucose co-transporter-2 (SGLT2) inhibitor for type 2 diabetes control, has been approved for heart failure therapy. Still, its cardioprotective effect in the experimental model of sorafenib cardiotoxicity has not yet been reported. Real-time quantitative RT-PCR (qRT-PCR), immunoblot, and immunohistochemical analyses were applied to study the effect of sorafenib exposure on cardiac SGLT2 expression. The impact of empagliflozin on cell viability was investigated in the sorafenib-treated cardiomyocytes using Alamar blue assay. Immunoblot analysis was employed to delineate the effect of sorafenib and empagliflozin on ferroptosis/proinflammatory signaling in cardiomyocytes. Ferroptosis/DNA damage/fibrosis/inflammation of myocardial tissues was studied in mice with a 28-day sorafenib ± empagliflozin treatment using histological analyses. Sorafenib exposure significantly promoted SGLT2 upregulation in cardiomyocytes and mouse hearts. Empagliflozin treatment significantly attenuated the sorafenib-induced cytotoxicity/DNA damage/fibrosis in cardiomyocytes and mouse hearts. Moreover, GPX4/xCT-dependent ferroptosis as an inducer for releasing high mobility group box 1 (HMGB1) was also blocked by empagliflozin administration in the sorafenib-treated cardiomyocytes and myocardial tissues. Furthermore, empagliflozin treatment significantly inhibited the sorafenib-promoted NFκB/HMGB1 axis in cardiomyocytes and myocardial tissues, and sorafenib-stimulated proinflammatory signaling (TNF-α/IL-1β/IL-6) was repressed by empagliflozin administration. Finally, empagliflozin treatment significantly attenuated the sorafenib-promoted macrophage recruitments in mouse hearts. In conclusion, empagliflozin may act as a cardioprotective agent for humans under sorafenib exposure by modulating ferroptosis/DNA damage/fibrosis/inflammation. However, further clinical evidence is required to support this preclinical finding.

Healthcare Violence and the Potential Promises and Harms of Artificial Intelligence.

Currently, the healthcare workplace is one of the most dangerous in the United States. Over a 3-month period in 2022, two nurses were assaulted every hour. Artificial intelligence (AI) has the potential to prevent workplace violence by developing unique patient insights through accessing almost instantly a patient's medical history, past institutional encounters, and possibly even their social media posts. De-escalating dialog can then be formulated, and hot-button topics avoided. AIs can also monitor patients in waiting areas for potential confrontational behavior.Many have concerns implementing AIs in healthcare. AIs are not expected to be 100% accurate, their performance is not compared with a computer but instead measured against humans. However, AIs are outperforming humans in many tasks. They are especially adept at taking standardized examinations, such as Board Exams, the Uniform Bar Exam, and the SAT and Graduate Record Exam. AIs are also performing diagnosis. Initial reports found that newer models have been observed to equal or outperform physicians in diagnostic accuracy and in the conveyance of empathy.In the area of interdiction, AI robots can both navigate and monitor for confrontational and illegal behavior. A human security agent would then be notified to resolve the situation. Our military is fielding autonomous AI robots to counter potential adversaries. For many, this new arms race has grave implications because of the potential of fielding this same security technology in healthcare and other civil settings.The healthcare delivery sector must determine the future roles of AI in relationship to human workers. AIs should only be used to support a human employee. AIs should not be the primary caregiver and a single human should not be monitoring multiple AIs simultaneously. Similar to not being copyrightable, disinformation produced by AIs should not be afforded 'free speech' protections. Any increase in productivity of an AI will equate with a loss of jobs. We need to ask, If all business sectors utilize AIs, will there be enough paid workers for the purchasing of services and products to keep our economy and society a float?

A Taxonomy of Explanation Types and Need Indicators in Human–Agent Collaborations

In recent years, explanations have become a pressing matter in AI research. This development was caused by the increased use of black-box models and a realization of the importance of trustworthy AI. In particular, explanations are necessary for human–agent interactions to ensure that the user can trust the agent and that collaborations are effective. Human–agent interactions are complex social scenarios involving a user, an autonomous agent, and an environment or task with its own distinct properties. Thus, such interactions require a wide variety of explanations, which are not covered by the methods of a single AI discipline, such as computer vision or natural language processing. In this paper, we map out what types of explanations are important for human–agent interactions, surveying the field via a scoping review. In addition to the typical introspective explanation tackled by explainability researchers, we look at assistive explanations, aiming to support the user with their task. Secondly, we survey what causes the need for an explanation in the first place. We identify a variety of human–agent interaction-specific causes and categorize them by whether they are centered on the agent’s behavior, the user’s mental state, or an external entity. Our overview aims to guide robotics practitioners in designing agents with more comprehensive explanation-related capacities, considering different explanation types and the concrete times when explanations should be given.

Find the Gap: AI, Responsible Agency and Vulnerability

The responsibility gap, commonly described as a core challenge for the effective governance of, and trust in, AI and autonomous systems (AI/AS), is traditionally associated with a failure of the epistemic and/or the control condition of moral responsibility: the ability to know what we are doing and exercise competent control over this doing. Yet these two conditions are a red herring when it comes to understanding the responsibility challenges presented by AI/AS, since evidence from the cognitive sciences shows that individual humans face very similar responsibility challenges with regard to these two conditions. While the problems of epistemic opacity and attenuated behaviour control are not unique to AI/AS technologies (though they can be exacerbated by them), we show that we can learn important lessons for AI/AS development and governance from how philosophers have recently revised the traditional concept of moral responsibility in response to these challenges to responsible human agency from the cognitive sciences. The resulting instrumentalist views of responsibility, which emphasize the forward-looking and flexible role of agency cultivation, hold considerable promise for integrating AI/AS into a healthy moral ecology. We note that there nevertheless is a gap in AI/AS responsibility that has yet to be extensively studied and addressed, one grounded in a relational asymmetry of vulnerability between human agents and sociotechnical systems like AI/AS. In the conclusion of this paper we note that attention to this vulnerability gap must inform and enable future attempts to construct trustworthy AI/AS systems and preserve the conditions for responsible human agency.

Communication for Specific Purposes as translingual

Recent theoretical developments suggest that meaning making involves the relationality of human and nonhuman agents and diverse semiotic resources working together. “Translingual” is an inclusive term to refer to how different languages and modalities work together in communication. I draw from enregisterment perspectives in linguistic anthropology to demonstrate how a corpus of semiotic features becomes sedimented to identify specialized communicative activities. I illustrate from the Research Group Meeting of international STEM scholars in a midwestern American university. Despite their variable grammatical proficiency in English, the international scholars communicate effectively because they draw from a translingual assemblage that is diversified, and collaborate for joint outcomes adopting reciprocal communicative strategies. Outcomes are not defined by the grammatical mastery of individual speakers, but how participants collaborate through embodied translingual semiotic resources in their setting and community, facilitated by suitable ethical dispositions. The pedagogical alternative proposed will focus on cultivating the dispositions to negotiate translingual repertoires, material ecologies, and social networks for more inclusive outcomes in communication for specific purposes.

AI-Enhanced Fintech communication: Leveraging Chatbots and NLP for efficient banking support

The convergence of artificial intelligence (AI) and financial technology (fintech) has revolutionized the way banks and financial institutions communicate with customers. This paper explores the use of AI-enhanced fintech communication, focusing on the utilization of chatbots and natural language processing (NLP) to provide efficient banking support. AI-powered chatbots have become indispensable tools for banks seeking to enhance customer service and streamline communication channels. By leveraging NLP algorithms, these chatbots can understand and respond to customer queries in real-time, providing personalized assistance round-the-clock. The integration of AI into fintech communication enables banks to offer seamless and efficient support, improving customer satisfaction and loyalty. The key to the effectiveness of AI-enhanced fintech communication lies in the ability of chatbots to interpret and respond to natural language input accurately. NLP algorithms enable chatbots to analyze and understand the intent behind customer queries, allowing them to provide relevant and contextually appropriate responses. This capability enhances the overall customer experience by reducing response times and ensuring that customers receive accurate and helpful information. Furthermore, AI-powered chatbots can handle a wide range of inquiries, from basic account inquiries to complex financial transactions. By automating routine tasks and inquiries, banks can free up human agents to focus on more complex and value-added activities. This not only improves operational efficiency but also allows banks to deliver faster and more responsive customer service. In addition to providing support to customers, AI-enhanced fintech communication can also help banks gather valuable insights into customer preferences and behavior. By analyzing interactions between customers and chatbots, banks can identify trends, anticipate customer needs, and tailor their products and services accordingly. This data-driven approach enables banks to offer more personalized and targeted offerings, leading to increased customer satisfaction and loyalty. In conclusion, AI-enhanced fintech communication, powered by chatbots and NLP, offers significant benefits for banks and financial institutions. By leveraging AI technology, banks can provide efficient and personalized support to customers, improve operational efficiency, and gain valuable insights into customer behavior. As AI continues to advance, the future of fintech communication promises even greater efficiency, personalization, and innovation in banking support. Keywords: Al- Enhanced, Fintech Communication, Leveraging, Chatbots, NLP.

Organization philosophy: a study of organizational goodness in the age of human and artificial intelligence collaboration

AbstractThis study challenges the conventional boundaries of philosophy by asserting that organizations can function as legitimate subjects within philosophical discourse. Western philosophy, epitomized by Descartes, has long assumed that individual human beings are the fundamental units of thought and moral agency. However, in a significant oversight, this belief overlooks the idea that organizations can think independently, leading to both virtuous and malevolent results. Epistemology lacks a clear prioritization of morally sound knowledge over potentially harmful knowledge. The advent of artificial intelligence (AI) has also transformed the landscape of cognition from a primarily individual human endeavor into a joint undertaking where humans collaborate with AI entities in collective thinking and decision-making. Nevertheless, AI lacks the innate ability to discern between ethical and unethical actions. This symbiosis creates organizations as fundamental philosophical subjects, marking a significant shift in philosophical emphasis where the organization takes the center stage. Accordingly, within this emerging field, we advance four propositions as the core subjects of organization philosophy: organizations can think, organizations can create knowledge, AI can be part of an organization, and humans in the organization control moral discipline. These propositions guide the ethical coexistence of AI and human agents in organizations, emphasizing the importance of ethical deliberation in knowledge use. Moreover, the malicious use of profound knowledge hinders human progress, as illustrated in this study by an ethical examination of military institutions within organization philosophy.

In and Out of Plain Sight: Interrogating Power in the Mekong Riverscape

The Mekong River has long conjured images of power: cascading waterfalls, impenetrable rapids, and pervading spirits. But power is always malleable and shifting, a fact not lost on the human and nonhuman agents who live with the river and its tributaries. Rises and drops in water levels that once fluctuated in accordance with the seasons have now become beholden to the electricity needs of urban centres like Bangkok: a shift from watershed to powershed. The emerging techno-political realities of the Mekong give rise to new ways of seeing and managing the river, often at the cost of long- held local understandings and configurations of power. The recent boom in hydropower development in the Mekong Basin has been alarming, and rapid change has had adverse impacts on the riparian peoples who depend on the river for their livelihoods. Despite the countless negative social and ecological impacts from dams, especially on the mainstream Mekong, hydropower investments and construction continue. New hegemonic scales and configurations of power have emerged in the Mekong Basin, but older local ones have not simply disappeared. Rather, power shifts in and out of plain sight. In this special issue, we trace the flows, scales, and reconfigurations of power from the management of Mekong water flows and the financing of hydropower projects through to the ruptures and (un) intended consequences of hydropower dam projects on living human and nonhuman worlds in the region. Through a multi-scaled and multi-sited lens, we bring to light emerging worlds in the Mekong Basin.

Generative AI as Economic Agents

Traditionally, AI has been modeled within economics as a technology that impacts payoffs by reducing costs or refining information for human agents. Our position is that, in light of recent advances in generative AI, it is increasingly useful to model AI itself as an economic agent. In our framework, each user is augmented with an AI agent and can consult the AI prior to taking actions in a game. The AI agent and the user have potentially different information and preferences over the communication, which can result in equilibria that are qualitatively different than in settings without AI.

How Being Outvoted by AI Teammates Impacts Human-AI Collaboration

Recent advances in artificial intelligence (AI) enable AI agents to go beyond simply supporting human activities and, instead, take more control in team decision-making. While significant literature has studied human-AI collaboration through the lens of AI as a “second opinion system,” this type of interaction is not fully representative of many human-human team collaboration scenarios, such as scenarios where each decision maker is granted equal voting rights for the team decision. In this research, we explore how imparting AI agents with equal voting rights to the human impacts human-AI decision-making and team performance. Using a human subjects experiment in which participants collaborate with two AI teammates for truss structure (aka, bridge) design, we manipulate a series of voting scenarios (e.g., AI agents outvoting the human vs. AI agents agreeing with the human) and AI performance levels (high vs. low performing). The results indicate that changes in human self-confidence are not consistent with whether the quality of the final team-voted design action is advantageous or disadvantageous relative to their own actions. The results also show that when humans are outvoted by their AI teammates, they do not show strong negative emotional reactions if the team-voted decision has an advantageous outcome. Additionally, AI performance significantly influences the human-AI team decision-making process and even one low-performing AI (i.e., an AI that is frequently incorrect) on the team can significantly deteriorate team performance. Taken together, this research provides empirical evidence on the effects of AI voting with equal decision authority on human-AI collaboration, as well as valuable insights supporting real-world applications of human-AI collaboration via voting.

‘Someone call a global health lawyer!’: global health law as an emerging community of practice

Despite the recent expansion of efforts to delineate the normative and institutional contours of global health law, relatively little has been said of the human agents shaping this young field. This article offers a first descriptive account of global health law as an emerging community of practice, composed of, among others, scholars and teachers, advisers, practitioners and advocates who engage international legal norms in an effort to influence global health. Given the potential sway this community holds over the making, interpretation and application of norms that have bearing on global public health, the article calls for a practice of reflexivity and an ethic of care by self-professed ‘global health lawyers’.

The importance of understanding the multiple dimensions of power in stakeholder participation for effective biodiversity conservation

Abstract Biodiversity conservation are increasingly focused on involving stakeholder engagement, making power a key concept in understanding its success and failure. Power is often conceptualized as unidimensional and coercive, but a multidimensional view better reflects structural power, as well as its productive and enabling potential. This paper investigates how different dimensions of power in participatory processes affect biodiversity conservation objectives. Six case studies from Europe and Asia‐Pacific were analysed using an adapted framework that explores the interlinkages between ‘power over’ and ‘transformative power’, looking at the scale and space in which power occurs, and analysing in which arenas of power and under which form of expression it appears. The framework distinguishes between the different ways to exert influence (‘power to’, ‘power with’, ‘power within’, ‘power for’), as well as the dynamics of domination and resistance observed in decision‐making (visible power), hidden biases and exclusionary experiences (hidden power), and actions that either reinforce or resist social norms and beliefs (invisible and systematic power). Focusing on biodiversity, the different arenas of power allow us to go deeper than the surface issues and conflicting interests of diverse participants, regarding for example wildlife, to question underlying power dynamics. Different expressions of power, more specifically the ‘power for’ dimension, allow an understanding of how participants integrate nature and biodiversity in their aspirations. The different levels of power also highlight the need to focus not only on the local level but to analyse how participatory processes are embedded in national, or even international governance in a globalized world. Finally, they shed light on two challenges in participatory processes regarding biodiversity: the representation of non‐human interests (designated here as ‘beyond‐human’ voices), and the integration of multiple forms of knowledge systems. Synthesis and applications: Integrating power into biodiversity issues involves deconstructing normalized discourses that focus solely on certain more powerful human agents, their interests and scientific knowledge, and creating new narratives, knowledge and embodied practice of learning and action to encompass a wider diversity of voices and views. Read the free Plain Language Summary for this article on the Journal blog.

Complete genome sequences of Cytobacillus sp., Domibacillus sp., Enterobacter sp., Neisseria sp., Pseudomonas sp., and Streptococcus sp. strains from human clinical infections collected at diagnostic units in 2020.

Members of Bacillota and Pseudomonadota phyla are frequently considered bacterial infectious agents in humans. As part of a large sequencing project of clinically relevant pathogens, we hybrid-assembled complete genomes of Cytobacillus, Domibacillus, Enterobacter, Neisseria, Pseudomonas, and Streptococcus species isolated from clinical specimens.

Implementing artificial intelligence across task types: constraints of automation and affordances of augmentation

PurposeThis study aims to uncover the constraints of automation and the affordances of augmentation related to implementing artificial intelligence (AI)-powered systems across different task types: mechanical, thinking and feeling.Design/methodology/approachQualitative study involving 45 interviews with various stakeholders in artistic gymnastics, for which AI-powered systems for the judging process are currently developed and tested. Stakeholders include judges, gymnasts, coaches and a technology vendor.FindingsWe identify perceived constraints of automation, such as too much mechanization, preciseness and inability of the system to evaluate artistry or to provide human interaction. Moreover, we find that the complexity and impreciseness of the rules prevent automation. In addition, we identify affordances of augmentation such as speedier, fault-less, more accurate and objective evaluation. Moreover, augmentation affords to provide an explanation, which in turn may decrease the number of decision disputes.Research limitations/implicationsWhile the unique context of our study is revealing, the generalizability of our specific findings still needs to be established. However, the approach of considering task types is readily applicable in other contexts.Practical implicationsOur research provides useful insights for organizations that consider implementing AI for evaluation in terms of possible constraints, risks and implications of automation for the organizational practices and human agents while suggesting augmented AI-human work as a more beneficial approach in the long term.Originality/valueOur granular approach provides a novel point of view on AI implementation, as our findings challenge the notion of full automation of mechanical and partial automation of thinking tasks. Therefore, we put forward augmentation as the most viable AI implementation approach. In addition, we developed a rich understanding of the perception of various stakeholders with a similar institutional background, which responds to recent calls in socio-technical research.

Can we build a relationship through artificial intelligence (AI)? Understanding the impact of AI on organization-public relationships

The purpose of this study is to explore the role of agent type and voice tone on publics’ perceived organization-public relationships and behavioral intention towards an organization. Through a 2 (agent type: human vs. AI) x 2 (voice tone: conversational human vs. organizational) between-subjects experiment, the study shows that organizational messages communicated by a human agent generated significantly higher perceived control mutuality among participants in a Twitter setting than messages communicated by an AI agent. The use of conversational human voice resulted in significantly stronger relational outcomes. A significant interaction effect was found between agent type and voice tone on control mutuality. This study contributes to the public relations literature and practice by examining the potential role of AI agents in organization-public relationships and supportive behavioral intention towards an organization in the setting of Twitter.