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AbstractAs the perspective on creativity shifts to “how it is expressed,” research has aimed to categorize it by problem-solving style. Since harnessing individual creative traits can positively impact creative performance, there has been an emphasis on designing computer systems that are tailored to personal problem-solving behaviors. AI-CST has opened up the potential to facilitate such customization. In this work, we consider two types of creativity—adaptors and innovators—based on problem-solving styles, and investigate AI-CST designs that both types could flexibly use according to the fashion design process. We identified two main AI-CST functions—determining design direction and receiving design inspiration—of the fashion design process, and developed CoCoStyle to map these functions. Through a user study with 30 fashion professionals (15 adaptors and 15 innovators), we found significant differences between the two groups from survey responses, system usage logs, and interviews. Based on the results, we discuss the theoretical and practical implications of AI and AI-CST where creativity is essential. [ABSTRACT FROM AUTHOR]

Artificial intelligence (AI) provides considerable opportunities to assist human work. However, one crucial challenge of human–AI collaboration is that many AI algorithms operate in a black-box manner where the way how the AI makes predictions remains opaque. This makes it difficult for humans to validate a prediction made by AI against their own domain knowledge. For this reason, we hypothesize that augmenting humans with explainable AI improves task performance in human–AI collaboration. To test this hypothesis, we implement explainable AI in the form of visual heatmaps in inspection tasks conducted by domain experts. Visual heatmaps have the advantage that they are easy to understand and help to localize relevant parts of an image. We then compare participants that were either supported by (a) black-box AI or (b) explainable AI, where the latter supports them to follow AI predictions when the AI is accurate or overrule the AI when the AI predictions are wrong. We conducted two preregistered experiments with representative, real-world visual inspection tasks from manufacturing and medicine. The first experiment was conducted with factory workers from an electronics factory, who performed assessments of whether electronic products have defects. The second experiment was conducted with radiologists, who performed assessments of chest X-ray images to identify lung lesions. The results of our experiments with domain experts performing real-world tasks show that task performance improves when participants are supported by explainable AI with heatmaps instead of black-box AI. We find that explainable AI as a decision aid improved the task performance by 7.7 percentage points (95% confidence interval [CI]: 3.3% to 12.0%, ) in the manufacturing experiment and by 4.7 percentage points (95% CI: 1.1% to 8.3%, ) in the medical experiment compared to black-box AI. These gains represent a significant improvement in task performance. [ABSTRACT FROM AUTHOR]

AbstractAs AI systems become increasingly prevalent in critical domains, ensuring their alignment with stakeholders’ values is essential. However, recent studies have revealed deficiencies in socio-technical design processes and design activities for AI, particularly in eliciting diverse stakeholder values and integrating them into system design and development. To investigate these challenges empirically, we conducted 30 semi-structured interviews with AI practitioners. Our findings reveal several key challenges faced during AI design and development. Firstly, practitioners struggle with identifying and involving stakeholders due to uncertainties regarding user demo-graphics, and a lack of interdisciplinary expertise. Secondly, they encounter obstacles in integrating values into technologies, citing practical complexities, unclear responsibilities, and limited support. This paper presents a concept map detailing these four primary barriers and discusses potential strategies and recommendations for overcoming them. These strategies revolve around improving value elicitation practices, facilitating more meaningful stakeholder engagement, and understanding the impact of stakeholder values By qualitatively describing the barriers to integrating stakeholder values into AI systems, this study contributes to the emerging field of value-sensitive AI. [ABSTRACT FROM AUTHOR]

Legal rules are based on an imagined regulatory scene that contains presumptions about the reality a regulation addresses. Regarding automated decision-making (ADM), these include a belief in the 'good human decision' that is mirrored in the cautious approach in the GDPR. Yet the 'good human decision' defies psychological insight into human weaknesses in decision-making. Instead, it reflects a general unease about algorithmic decisions. Against this background I explore how algorithms become part of human relationships and whether the use of decision systems causes a conflict with human needs, values and the prevailing socio-legal framework. Inspired by the concept of Human-Centered AI, I then discuss how the law may address the apprehension towards decision systems. I outline a human-focused approach to regulating ADM that focuses on improving the practice of decision-making. The interaction between humans and machines is an essential part of the regulation. It must address socio-legal changes caused by decision systems both to integrate them into the existing value system and adapt the latter to changes brought forth by ADM. A human-focused approach thus connects the benefits of technology with human needs and societal values. [ABSTRACT FROM AUTHOR]

As artificial intelligence (AI) becomes increasingly integrated into educational environments, adopting a human-centered approach is essential for enhancing student outcomes. This study investigates the role of personality factors in predicting academic performance, emphasizing the need for explainable and ethical AI systems. Utilizing the SAPEx-D (Student Academic Performance Exploration) dataset from Air University, Islamabad, which comprises 494 records, we explore how individual personality traits can impact academic success. We employed advanced regression models, including Gradient Boosting Regressor, K-Nearest Neighbors Regressor, Linear Regression, and Support Vector Regression, to predict students' Cumulative Grade Point Average (CGPA). Our findings reveal that the Gradient Boosting Regressor achieved an R-squared value of 0.63 with the lowest Mean Squared Error (MSE); incorporating personality factors elevated the R-squared to 0.83, significantly improving predictive accuracy. For letter grade classification, the incorporation of personality factors improved the accuracy for distinct classes to 0.67 and to 0.85 for broader class categories. The integration of the Shapley Additive Explanations (SHAPs) technique further allowed for the interpretation of how personality traits interact with other factors, underscoring their role in shaping academic outcomes. This research highlights the importance of designing AI systems that are not only accurate but also interpretable and aligned with human values, thereby fostering a more equitable educational landscape. Future work will expand on these findings by exploring the interaction effects of personality traits and applying more sophisticated machine learning techniques. [ABSTRACT FROM AUTHOR]

The integration of artificial intelligence (AI) in the workplace is at a nascent stage, presenting both substantial opportunities and challenges for productivity growth. We argue that AI's potential will only be truly realized through strategic investments in human skills and comprehensive organizational redesign. Drawing on interdisciplinary insights, we highlight the critical role of AI-human collaboration, continuous workforce skill development, and adaptive organizational practices. We conclude with recommendations to create a human-centered environment conducive to AI-driven productivity gains through its assistance, augmentation, and automation capabilities. [ABSTRACT FROM AUTHOR]

Purpose: This study aims to discuss the impacts of using generative artificial intelligence (GenAI) in education and research in the business and management field, using a virtue ethics lens to reflect on technology's effects on people. Originality/value: Our analysis considers the potential risks and opportunities of using GenAI, particularly ChatGPT. We categorized the effects of generative AI on education and research into groups by mapping agent-centered or action-centered articles and sorting them by the ethical perspective they come from (deontology, utilitarianism, or virtue ethics), keeping in mind that AI ethics addresses mainly utilitarian rules and principles. Our analysis emphasizes the human element to avoid oversimplifying the effects on people's formation. Design/methodology/approach: We conducted a semi-systematic review of recent literature on GenAI in management education and research. We used the PRISMA method to collect and select articles from three academic databases: Scopus, Science Direct, and Web of Science, in addition to Google Scholar. From 45 articles, we mapped three main issues: analysis level, ethical perspective, and GenAI impacts. Findings: We point out that using GenIA for student learning and researcher training in virtues or character is incipient, while ethical issues are mentioned implicitly or superficially. GenAI can enhance or reduce human development and research, depending on its appropriate use in learning and research processes. A solid grounding in virtue ethics is essential to deeply understanding the impact of human-AI relationships. [ABSTRACT FROM AUTHOR]

This paper explores the convergence of Human-Centered AI (HCAI) and Cyber-Physical Social Systems (CPSS) in pursuing advanced Cognitive Situation Awareness (CSA). Integrating HCAI principles within CPSS fosters systems prioritizing human needs, values, and experiences, improving perception, understanding, and responsiveness to complex environments. By incorporating transparency, interpretability, and usability into Artificial Intelligence (AI) systems, the human-centered approach enhances user interaction and cooperation with intelligent systems, leading to more adaptive and efficient CPSS. The study employs a comprehensive approach to explore the intersection of HCAI and CPSS. Moreover, the paper presents case studies to illustrate real-world applications of HCAI and CPSS, such as self-driving cars and smart homes, transportation, healthcare, energy management, social media, and emergency response systems. Nevertheless, technical complexities, privacy concerns, and regulatory considerations must be addressed. The paper demonstrates the practical implications of integrating HCAI into CPSS through case studies in various domains. Furthermore, It highlights the positive impact of CSA systems such as self-driving cars, showcasing improvements in transportation. This paper contributes to advancing CSA and designing intelligent systems, promoting human–machine collaboration and societal well-being. By examining the intersection of HCAI and CPSS, this study advances research in CSA and designing intelligent systems prioritizing human needs, values, and experiences. [ABSTRACT FROM AUTHOR]

Sentiment analysis (SA) is an effective method for determining public opinion. Social media posts have been the subject of much research, due to the platforms' enormous and diversified user bases that regularly share thoughts on nearly any subject. However, on posts composed by a text–image pair, the written description may or may not convey the same sentiment as the image. The present study uses machine learning models for the automatic sentiment evaluation of pairs of text and image(s). The sentiments derived from the image and text are evaluated independently and merged (or not) to form the overall sentiment, returning the sentiment of the post and the discrepancy between the sentiments represented by the text–image pair. The image sentiment classification is divided into four categories—"indoor" (IND), "man-made outdoors" (OMM), "non-man-made outdoors" (ONMM), and "indoor/outdoor with persons in the background" (IOwPB)—and then ensembled into an image sentiment classification model (ISC), that can be compared with a holistic image sentiment classifier (HISC), showing that the ISC achieves better results than the HISC. For the Flickr sub-data set, the sentiment classification of images achieved an accuracy of 68.50% for IND, 83.20% for OMM, 84.50% for ONMM, 84.80% for IOwPB, and 76.45% for ISC, compared to 65.97% for the HISC. For the text sentiment classification, in a sub-data set of B-T4SA, an accuracy of 92.10% was achieved. Finally, the text–image combination, in the authors' private data set, achieved an accuracy of 78.84%. [ABSTRACT FROM AUTHOR]

The Hybrid Intelligence Technology Acceptance Model (HI-TAM) presented in this paper offers a novel framework for training and adopting generative design (GD) assistants, facilitating co-creation between human experts and AI systems. Despite the promising outcomes of GD, such as augmented human cognition and highly creative design products, challenges remain in the perception, adoption, and sustained collaboration with AI, especially in creative design industries where personalized and specialized assistance is crucial for individual style and expression. In this two-study paper, we present a holistic hybrid intelligence (HI) approach for individual experts to train and personalize their GD assistants on-the-fly. Culminating in the HI-TAM, our contribution to human-AI interaction is 4-fold including (i) domain-specific suitability of the HI approach for real-world application design, (ii) a programmable common language that facilitates the clear communication of expert design goals to the generative algorithm, (iii) a human-centered continual training loop that seamlessly integrates AI training into the expert's workflow, (iv) a hybrid intelligence narrative that encourages the psychological willingness to invest time and effort in training a virtual assistant. This approach facilitates individuals' direct communication of design objectives to AI and fosters a psychologically safe environment for adopting, training, and improving AI systems without the fear of job-replacement. To demonstrate the suitability of HI-TAM, in Study 1 we surveyed 41 architectural professionals to identify the most preferred workflow scenario for an HI approach. In Study 2, we used mixed methods to empirically evaluate this approach with 8 architectural professionals, who individually co-created floor plan layouts of office buildings with a GD assistant through the lens of HI-TAM. Our results suggest that the HI-TAM enables professionals, even non-technical ones, to adopt and trust AI-enhanced co-creative tools.

AbstractThe increased prevalence of human-AI collaboration is reshaping the manufacturing sector, fundamentally changing the nature of human work and training needs. While high automation improves performance when functioning correctly, it can lead to problematic human performance (e.g., defect detection accuracy, response time) when operators are required to intervene and assume manual control of decision-making responsibilities. As AI capability reaches higher levels of automation and human–AI collaboration becomes ubiquitous, addressing these performance issues is crucial. Proper worker training, focusing on skill-based, cognitive, and affective outcomes, and nurturing motivation and engagement, can be a mitigation strategy. However, most training research in manufacturing has prioritized the effectiveness of a technology for training, rather than how training design influences motivation and engagement, key to training success and longevity. The current study explored how training workers using an AI system affected their motivation, engagement, and skill acquisition. Specifically, we manipulated the level of automation of decision selection of an AI used for the training of 102 participants for a quality control task. Findings indicated that fully automated decision selection negatively impacted perceived autonomy, self-determined motivation, behavioral task engagement, and skill acquisition during training. Conversely, partially automated AI-enhanced motivation and engagement, enabling participants to better adapt to AI failure by developing necessary skills. The results suggest that involving workers in decision-making during training, using AI as a decision aid rather than a decision selector, yields more positive outcomes. This approach ensures that the human aspect of manufacturing work is not overlooked, maintaining a balance between technological advancement and human skill development, motivation, and engagement. These findings can be applied to enhance real-world manufacturing practices by designing training programs that better develop operators’ technical, methodological, and personal skills, though companies may face challenges in allocating substantial resources for training redevelopment and continuously adapting these programs to keep pace with evolving technology. [ABSTRACT FROM AUTHOR]

User control and human-AI collaboration are two related directions of research in the modern stream of work on human-centered AI. The field of AI in education was an early pioneer in this area of research, but now it lags behind the work on user control and human-AI collaboration in other areas of AI. This paper attempts to motivate further research on learner control and human-AI collaboration in educational applications of AI by presenting a review of the current work and comparing it with similar work in the field of recommender systems. [ABSTRACT FROM AUTHOR]

Providing educators with understandable, actionable, and trustworthy insights drawn from large-scope heterogeneous learning data is of paramount importance in achieving the full potential of artificial intelligence (AI) in educational settings. Explainable AI (XAI)—contrary to the traditional "black-box" approach—helps fulfilling this important goal. We present a case study of building prediction models for undergraduate students' learning achievement in a Computer Science course, where the development process involves the course instructor as a co-designer, and with the use of XAI technologies to explain the underlying reasoning of several machine learning predictions. The explanations enhance the transparency of the predictions and open the door for educators to share their judgments and insights. It further enables us to refine the predictions by incorporating the educators' contextual knowledge of the course and of the students. Through this human-AI collaboration process, we demonstrate how to achieve a more accountable understanding of students' learning and drive towards transparent and trustworthy student learning achievement prediction by keeping instructors in the loop. Our study highlights that trustworthy AI in education should emphasize not only the interpretability of the predicted outcomes and prediction process, but also the incorporation of subject-matter experts throughout the development of prediction models. [ABSTRACT FROM AUTHOR]

This paper delineates the contemporary landscape, challenges, and prospective developments in human-centred artificial intelligence (AI) within the ambit of smart farming, a pivotal element of the emergent Agriculture 5.0, supplanting Agriculture 4.0. Analogous to Industry 4.0, agriculture has witnessed a trend towards comprehensive automation, often marginalizing human involvement. However, this approach has encountered limitations in agricultural contexts for various reasons. While AI’s capacity to assume human tasks is acknowledged, the inclusion of human expertise and experiential knowledge (human-in-the-loop) often proves indispensable, corroborated by the Moravec’s Paradox: tasks simple for humans are complex for AI. Furthermore, social, ethical, and legal imperatives necessitate human oversight of AI, a stance strongly reflected in the European Union’s regulatory framework. Consequently, this paper explores the advancements in human-centred AI focusing on their application in agricultural processes. These technological strides aim to enhance crop yields, minimize labor and resource wastage, and optimize the farm-to-consumer supply chain. The potential of AI to augment human decision-making, thereby fostering a sustainable, efficient, and resilient agri-food sector, is a focal point of this discussion - motivated by the current worldwide extreme weather events. Finally, a framework for Agriculture 5.0 is presented, which balances technological prowess with the needs, capabilities, and contexts of human stakeholders. Such an approach, emphasizing accessible, intuitive AI systems that meaningfully complement human activities, is crucial for the successful realization of future Agriculture 5.0.

Aim/purpose – The study delves into the creation and the experience of interactive children’s narratives based on poetry, examining the emerging role of artificial intelligence (AI) as a collaborative partner in storytelling for children. The research questions are: 1) What are the experiences of readers, specifically children’s guardians, with interactive narratives based on children’s poetry?; 2) How do children’s guardians experience inter-active stories co-generated in real-time through conversations with artificial intelligence?; 3) Is it feasible to create a satisfying narrative for children from a specific set of images through the use of AI technology?

This article examines the role of employee participation in AI implementation, focusing on a case study from the German telecommunications sector. Theoretical discussions highlight concepts of employee participation and workplace democracy, emphasizing the normative basis for human-centered AI in Europe. The empirical analysis of the case study demonstrates social practices of human-centered AI and the importance of employee representatives and labor policies in sustainable technology. The contribution is structured into two main parts: first, discussing sociological concepts of employee participation and summarizing the role of works councils in shaping digital technology implementation. Second, focusing on a case study of AI regulations at Deutsche Telekom, highlighting the significant effects of employee participation and co-determination by the group works council in promoting socially sustainable AI implementation which is done via qualitative case analysis. The article highlights the significance of participation and negotiations and gives an example for social partnership relations in AI implementations.

In an era where Artificial Intelligence (AI) integration into business processes is crucial for maintaining competitiveness, there is a growing need for structured guidance on designing AI solutions that align with human needs. To this end, we present “technical assistance concerning human-centered AI development” (tachAId), an interactive advisory tool which comprehensively guides AI developers and decision makers in navigating the machine learning lifecycle with a focus on human-centered design. tachAId motivates and presents concrete technical advice to ensure human-centeredness across the phases of AI development. The tool's effectiveness is evaluated through a catalog of criteria for human-centered AI in the form of relevant challenges and goals, derived from existing methodologies and guidelines. Lastly, tachAId and one other comparable advisory tool were examined to determine their adherence to these criteria in order to provide an overview of the human-centered aspects covered by these tools and to allow interested parties to quickly assess whether the tools meet their needs.

The advancements in human-centered artificial intelligence (HCAI) systems for Industry 5.0 is a new phase of industrialization that places the worker at the center of the production process and uses new technologies to increase prosperity beyond jobs and growth. HCAI presents new objectives that were unreachable by either humans or machines alone, but this also comes with a new set of challenges. Our proposed method accomplishes this through the knowlEdge architecture, which enables human operators to implement AI solutions using a zero-touch framework. It relies on containerized AI model training and execution, supported by a robust data pipeline and rounded off with human feedback and evaluation interfaces. The result is a platform built from a number of components, spanning all major areas of the AI lifecycle. We outline both the architectural concepts and implementation guidelines and explain how they advance HCAI systems and Industry 5.0. In this article, we address the problems we encountered while implementing the ideas within the edge-to-cloud continuum. Further improvements to our approach may enhance the use of AI in Industry 5.0 and strengthen trust in AI systems. [ABSTRACT FROM AUTHOR]

The integration of chatbots in oncology underscores the pressing need for human-centered artificial intelligence (AI) that addresses patient and family concerns with empathy and precision. Human-centered AI emphasizes ethical principles, empathy, and user-centric approaches, ensuring technology aligns with human values and needs. This review critically examines the ethical implications of using large language models (LLMs) like GPT-3 and GPT-4 (OpenAI) in oncology chatbots. It examines how these models replicate human-like language patterns, impacting the design of ethical AI systems. The paper identifies key strategies for ethically developing oncology chatbots, focusing on potential biases arising from extensive datasets and neural networks. Specific datasets, such as those sourced from predominantly Western medical literature and patient interactions, may introduce biases by overrepresenting certain demographic groups. Moreover, the training methodologies of LLMs, including fine-tuning processes, can exacerbate these biases, leading to outputs that may disproportionately favor affluent or Western populations while neglecting marginalized communities. By providing examples of biased outputs in oncology chatbots, the review highlights the ethical challenges LLMs present and the need for mitigation strategies. The study emphasizes integrating human-centric values into AI to mitigate these biases, ultimately advocating for the development of oncology chatbots that are aligned with ethical principles and capable of serving diverse patient populations equitably. [ABSTRACT FROM AUTHOR]

This document discusses the impact of artificial intelligence (AI) on day-to-day life and various applications and innovations in the field. It covers topics such as machine learning, computer vision, data analysis, robotics, and natural language processing. The document presents examples of AI advancements in areas such as opinion assessment, stock price prediction, beach monitoring, health diagnosis, energy consumption optimization, and identification. It also highlights future trends in AI, including explainable AI, AI ethics, edge AI, generative models, AI in healthcare, natural language processing, autonomous systems, AI in cybersecurity and finance, and the integration of quantum computing and AI. The document emphasizes the need to stay updated on the latest developments in AI to keep pace with the evolving field. [Extracted from the article]