As AI systems become increasingly prevalent in our daily lives and work, it is essential to contemplate their social role and how they interact with us. While functionality and increasingly explainability and trustworthiness are often the primary focus in designing AI systems, little consideration is given to their social role and the effects on human-AI interactions. In this paper, we advocate for paying attention to social roles in AI design. We focus on an AI healthcare application and present three possible social roles of the AI system within it to explore the relationship between the AI system and the user and its implications for designers and practitioners. Our findings emphasise the need to think beyond functionality and highlight the importance of considering the social role of AI systems in shaping meaningful human-AI interactions.
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The increasing use of AI in industry and society not only expects but demands that we build human-centred competencies into our AI education programmes. The computing education community needs to adapt, and while the adoption of standalone ethics modules into AI programmes or the inclusion of ethical content into traditional applied AI modules is progressing, it is not enough. To foster student competencies to create AI innovations that respect and support the protection of individual rights and society, a novel ground-up approach is needed. This panel presents on one such approach, the development of a Human-Centred AI Masters (HCAIM) as well as the insights and lessons learned from the process. In particular, we discuss the design decisions that have led to the multi-institutional master’s programme. Moreover, this panel allows for discussion on pedagogical and methodological approaches, content knowledge areas and the delivery of such a novel programme, along with challenges faced, to inform and learn from other educators that are considering developing such programmes.
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Explainable Artificial Intelligence (XAI) aims to provide insights into the inner workings and the outputs of AI systems. Recently, there’s been growing recognition that explainability is inherently human-centric, tied to how people perceive explanations. Despite this, there is no consensus in the research community on whether user evaluation is crucial in XAI, and if so, what exactly needs to be evaluated and how. This systematic literature review addresses this gap by providing a detailed overview of the current state of affairs in human-centered XAI evaluation. We reviewed 73 papers across various domains where XAI was evaluated with users. These studies assessed what makes an explanation “good” from a user’s perspective, i.e., what makes an explanation meaningful to a user of an AI system. We identified 30 components of meaningful explanations that were evaluated in the reviewed papers and categorized them into a taxonomy of human-centered XAI evaluation, based on: (a) the contextualized quality of the explanation, (b) the contribution of the explanation to human-AI interaction, and (c) the contribution of the explanation to human- AI performance. Our analysis also revealed a lack of standardization in the methodologies applied in XAI user studies, with only 19 of the 73 papers applying an evaluation framework used by at least one other study in the sample. These inconsistencies hinder cross-study comparisons and broader insights. Our findings contribute to understanding what makes explanations meaningful to users and how to measure this, guiding the XAI community toward a more unified approach in human-centered explainability.
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As artificial intelligence (AI) reshapes hiring, organizations increasingly rely on AI-enhanced selection methods such as chatbot-led interviews and algorithmic resume screening. While AI offers efficiency and scalability, concerns persist regarding fairness, transparency, and trust. This qualitative study applies the Artificially Intelligent Device Use Acceptance (AIDUA) model to examine how job applicants perceive and respond to AI-driven hiring. Drawing on semi-structured interviews with 15 professionals, the study explores how social influence, anthropomorphism, and performance expectancy shape applicant acceptance, while concerns about transparency and fairness emerge as key barriers. Participants expressed a strong preference for hybrid AI-human hiring models, emphasizing the importance of explainability and human oversight. The study refines the AIDUA model in the recruitment context and offers practical recommendations for organizations seeking to implement AI ethically and effectively in selection processes.
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With artificial intelligence (AI) systems entering our working and leisure environments with increasing adaptation and learning capabilities, new opportunities arise for developing hybrid (human-AI) intelligence (HI) systems, comprising new ways of collaboration. However, there is not yet a structured way of specifying design solutions of collaboration for hybrid intelligence (HI) systems and there is a lack of best practices shared across application domains. We address this gap by investigating the generalization of specific design solutions into design patterns that can be shared and applied in different contexts. We present a human-centered bottom-up approach for the specification of design solutions and their abstraction into team design patterns. We apply the proposed approach for 4 concrete HI use cases and show the successful extraction of team design patterns that are generalizable, providing re-usable design components across various domains. This work advances previous research on team design patterns and designing applications of HI systems.
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This exploration with ChatGPT underscores two vital lessons for human rights law education. First, the importance of reflective and critical prompting techniques that challenge it to critique its responses. Second, the potential of customizing AI tools like ChatGPT, incorporating diverse scholarly perspectives to foster a more inclusive and comprehensive understanding of human rights. It also shows the promise of using collaborative approaches to build tools that help create pluriversal approaches to the study of human rights law.
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This research investigates the potential and challenges of using artificial intelligence, specifically the ChatGPT-4 model developed by OpenAI, in grading and providing feedback in an educational setting. By comparing the grading of a human lecturer and ChatGPT-4 in an experiment with 105 students, our study found a strong positive correlation between the scores given by both, despite some mismatches. In addition, we observed that ChatGPT-4's feedback was effectively personalized and understandable for students, contributing to their learning experience. While our findings suggest that AI technologies like ChatGPT-4 can significantly speed up the grading process and enhance feedback provision, the implementation of these systems should be thoughtfully considered. With further research and development, AI can potentially become a valuable tool to support teaching and learning in education. https://saiconference.com/FICC
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In child centers, professionals from childcare, education, and occasionally family support services participate in interprofessional collaboration (IPC) to facilitate smooth transitions from childcare to primary education, coordination between school and out-of-school care, provide care for children with special needs, and implement integrated services policies at center level. This study employed a multilevel model for dyadic networks to examine a national sample of professional networks of 788 professionals from 46 Dutch child centers. A newly developed instrument for assessing the degree of integration at center level (Index for Child Center Integration, ICCI) was associated with (inter)professional collaboration at dyadic level for childcare-to-school transitions, out-of-school care to school transition, care for children with special needs, and policy at center level. Higher levels of service integration at center level were associated with increased interprofessional collaboration.
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In the Europe is a growing interest in interprofessional collaboration (IPC) between childcare, school and family support to offer integrated services for children and their families. However, there is a significant variation in the level of early childhood education and care system integration. In a systematic review from six countries with varying levels of system integration (in descending order from integrated systems to split systems; Norway, Finland, Sweden, Germany, Flanders (Dutch speaking part of Belgium) and the Netherlands), we analyzed the literature related to barriers and facilitators in IPC between childcare, school, and family support services. The review showed some differences in IPC characteristics from the literature from countries with different levels of system integration. The literature from countries with an integrated system tended to focus more on the process of collaboration, whereas the literature from countries with a split system concentrated on the content of collaboration. Despite the differences in national context, the results demonstrated that the literature from all countries reported the challenges faced by professionals in connecting different worlds. Contrary to our expectations the literature of ‘somewhat’ integrated countries tends to be more critical about IPC than the Dutch literature (a country with a split system).
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This research reviews the current literature on the impact of Artificial Intelligence (AI) in the operation of autonomous Unmanned Aerial Vehicles (UAVs). This paper examines three key aspects in developing the future of Unmanned Aircraft Systems (UAS) and UAV operations: (i) design, (ii) human factors, and (iii) operation process. The use of widely accepted frameworks such as the "Human Factors Analysis and Classification System (HFACS)" and "Observe– Orient–Decide–Act (OODA)" loops are discussed. The comprehensive review of this research found that as autonomy increases, operator cognitive workload decreases and situation awareness improves, but also found a corresponding decline in operator vigilance and an increase in trust in the AI system. These results provide valuable insights and opportunities for improving the safety and efficiency of autonomous UAVs in the future and suggest the need to include human factors in the development process.
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