Готові списки джерел за темами / User-Centric AI

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Добірка наукової літератури з теми "User-Centric AI"

Автор: Grafiati

Опубліковано: 13 квітня 2024

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Статті в журналах з теми "User-Centric AI"

Pazzani, Michael, Severine Soltani, Robert Kaufman, Samson Qian, and Albert Hsiao. "Expert-Informed, User-Centric Explanations for Machine Learning." Proceedings of the AAAI Conference on Artificial Intelligence 36, no. 11 (June 28, 2022): 12280–86. http://dx.doi.org/10.1609/aaai.v36i11.21491.

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We argue that the dominant approach to explainable AI for explaining image classification, annotating images with heatmaps, provides little value for users unfamiliar with deep learning. We argue that explainable AI for images should produce output like experts produce when communicating with one another, with apprentices, and with novices. We provide an expanded set of goals of explainable AI systems and propose a Turing Test for explainable AI.

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Saber Ismail, Dr Walaa. "Human-Centric AI : Enhancing User Experience through Natural Language Interfaces." Journal of Wireless Mobile Networks, Ubiquitous Computing, and Dependable Applications 15, no. 1 (March 29, 2024): 172–83. http://dx.doi.org/10.58346/jowua.2024.i1.012.

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AI has significantly altered the way humans interact with technology. It is important to observe the impact of Natural Language Interfaces (NLIs) on user experiences in Human-Centric AI across various industries. Therefore, we specifically focus on the influence of Human-Centric AI and user interactions within AI chatbots in the United Arab Emirates (UAE). The aim of this study is to assess the factors that influence the acceptance of AI, examine its practical implications across different industries, and offer valuable insights for the responsible development of AI. A quantitative survey methodology was employed, involving 230 participants in the UAE. The research design, data collection, and analysis followed the Unified Theory of Acceptance and Use of Technology (UTAUT) model, which emphasizes performance expectancy, effort expectancy, social influence, and facilitating conditions. The survey encompassed a variety of participants from various organizations, with a majority expressing positive attitudes towards AI chatbots. The survey found that 80% of users agreed that AI systems improve task efficiency, 84% believe they help achieve goals, and 84% view them as practical. According to 75% of participants, the social impact is strongly influenced by AI chatbot system adoption. However, 80% understood the relevance of organizational infrastructure and favorable conditions. In particular, 72% of users stated that Natural Language Interfaces transform, indicating satisfactory user experiences. These features demonstrate the influence of Human-Centric AI adoption and its use in different organizations. Natural language interfaces play a critical role in improving human-centered AI user experiences, investigating theoretical issues and real-world applications, and providing guidance for the ethical use of AI.

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Ugochukwu Okwudili Matthew, Kafayat Motomori Bakare, Godwin Nse Ebong, Charles Chukwuebuka Ndukwu, and Andrew Chinonso Nwanakwaugwu. "Generative Artificial Intelligence (AI) Educational Pedagogy Development: Conversational AI with User-Centric ChatGPT4." December 2023 5, no. 4 (December 2023): 401–18. http://dx.doi.org/10.36548/jtcsst.2023.4.003.

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In terms of language models, generative artificial intelligence (GenAI), and more specifically ChatGPT, offer a significant technological achievement as a revolutionary tool for natural language processing (NLP) and a transformative educational business tool. ChatGPT users' suggestions have the ability to optimize teaching and learning, thereby having a substantial impact on the educational environment of the twenty-first century. Educational robots are getting easier to access for a number of reasons. The human-robot cooperation that has advanced scientifically in industry 5.0 extreme digital automation, will also probably become a regular aspect of life in the days to come. This study examines the prospective uses of GenAI for NLP synthesis as well as its potential role as a conversational agent in the classroom business. GenAI's capacity to understand and produce language that is human-like by employing NLP to generate semantics was essential to its ability to replicate the most advanced human technology through comprehensive assumptions of patterns and structures it learns from its training data. With the rise of artificial intelligence (AI) driven conversational agents, prompt engineering has become an important aspect of digital learning. It is essential to get ready for an AI-dominated future when general and educational technologies combine. The study demonstrated how society may impact and contribute to the development of AI pedagogic learning using an instructional robotics application driven by AI, emphasizing the responsibility of humans as producers to reduce any potential misfortunes. The study highlights that since generative AI technologies have the potential to drastically change teaching and learning approaches and necessitate new ways of thinking, more research on organizational robotics, with a focus on human collaboration and education, will emerge from the technological concerns raised in this study.

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Zhang, Pengyi, Kathleen Gregory, Ayoung Yoon, and Carole Palmer. "Conceptualizing Data Behavior: Bridging Data‐centric and User‐centric Approaches." Proceedings of the Association for Information Science and Technology 60, no. 1 (October 2023): 856–60. http://dx.doi.org/10.1002/pra2.878.

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ABSTRACTWith the development of technologies in big data and AI, data has become more and more central to users for various tasks in different contexts. Yet the concept of data behavior, an emerging concept that captures the actions and interactions of individuals with data in various contexts and situations is not explicitly defined and framed. Data behavior focuses on the observable actions and reactions of users when they encounter, discover, seek, use, or create data for individual or collaborative tasks, while data practice encompasses the entire spectrum of how people work with data, from creating and managing to sharing and reusing data, as well as the intentional and strategic decisions and actions involved in these processes. This panel proposes a conversation and discussion about the concepts of data practice and data behavior by drawing on literature in data practice, data curation, and information behavior. This panel aims to discuss, compare, and bridge data‐centric and user‐centric approaches to conceptualizing data behavior. It will also present some examples of data behavior research in different domains and scenarios. The panel will highlight the challenges and opportunities of data behavior research for information science and practice.

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Hassan, Ali, Riza Sulaiman, Mansoor Abdullateef Abdulgabber, and Hasan Kahtan. "TOWARDS USER-CENTRIC EXPLANATIONS FOR EXPLAINABLE MODELS: A REVIEW." Journal of Information System and Technology Management 6, no. 22 (September 1, 2021): 36–50. http://dx.doi.org/10.35631/jistm.622004.

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Recent advances in artificial intelligence, particularly in the field of machine learning (ML), have shown that these models can be incredibly successful, producing encouraging results and leading to diverse applications. Despite the promise of artificial intelligence, without transparency of machine learning models, it is difficult for stakeholders to trust the results of such models, which can hinder successful adoption. This concern has sparked scientific interest and led to the development of transparency-supporting algorithms. Although studies have raised awareness of the need for explainable AI, the question of how to meet real users' needs for understanding AI remains unresolved. This study provides a review of the literature on human-centric Machine Learning and new approaches to user-centric explanations for deep learning models. We highlight the challenges and opportunities facing this area of research. The goal is for this review to serve as a resource for both researchers and practitioners. The study found that one of the most difficult aspects of implementing machine learning models is gaining the trust of end-users.

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Bernardo, Ezekiel, and Rosemary Seva. "Affective Design Analysis of Explainable Artificial Intelligence (XAI): A User-Centric Perspective." Informatics 10, no. 1 (March 16, 2023): 32. http://dx.doi.org/10.3390/informatics10010032.

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Explainable Artificial Intelligence (XAI) has successfully solved the black box paradox of Artificial Intelligence (AI). By providing human-level insights on AI, it allowed users to understand its inner workings even with limited knowledge of the machine learning algorithms it uses. As a result, the field grew, and development flourished. However, concerns have been expressed that the techniques are limited in terms of to whom they are applicable and how their effect can be leveraged. Currently, most XAI techniques have been designed by developers. Though needed and valuable, XAI is more critical for an end-user, considering transparency cleaves on trust and adoption. This study aims to understand and conceptualize an end-user-centric XAI to fill in the lack of end-user understanding. Considering recent findings of related studies, this study focuses on design conceptualization and affective analysis. Data from 202 participants were collected from an online survey to identify the vital XAI design components and testbed experimentation to explore the affective and trust change per design configuration. The results show that affective is a viable trust calibration route for XAI. In terms of design, explanation form, communication style, and presence of supplementary information are the components users look for in an effective XAI. Lastly, anxiety about AI, incidental emotion, perceived AI reliability, and experience using the system are significant moderators of the trust calibration process for an end-user.

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Mardania, Agnes Dini. "The Rise of AI in Business: Uncharted Avenues for Digital Transformation." Equator Journal of Management and Entrepreneurship (EJME) 12, no. 1 (January 29, 2024): 25. http://dx.doi.org/10.26418/ejme.v12i1.75794.

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This study investigates the transformative impact of Artificial Intelligence (AI) on PT. Perhutani Anugerah Kimia, with a focus on user experience, AI investments, digital transformation, and their collective influence on increased business efficiency. Through path analysis, the study reveals statistically significant direct effects, highlighting the pivotal role of user experience and strategic AI investments in propelling digital transformation and, consequently, improving business efficiency. The indirect effects analysis underscores the mediating role of digital transformation, elucidating how enhancements in user experience and AI investments positively cascade to boost business efficiency. These findings advocate for a strategic emphasis on user-centric approaches and substantial AI investments, positioning organizations to navigate the evolving business landscape by fostering digital transformation and realizing tangible operational efficiency gains. This research contributes valuable insights to organizations seeking to harness the full potential of AI for holistic and impactful digital evolution.

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Wang, Yu. "Impact of Social Emotional Intelligence on Students' Interpersonal Relationships and Academic Development." Journal of Education and Educational Research 5, no. 2 (September 1, 2023): 122–26. http://dx.doi.org/10.54097/jeer.v5i2.12552.

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The paper explores the intersection of Artificial Intelligence (AI) and mobile homestay design, elucidating AI’s multifaceted role in optimizing space, fostering sustainability, and enhancing user experience within this specific context. Through a comprehensive literature review, theoretical framework development, and detailed case studies, the paper unveils the significant potential and challenges of implementing AI in mobile homestay design. The case studies spotlight AI’s ability to dynamically optimize small spaces, promote sustainable practices, and tailor user experiences, providing invaluable insights for designers and researchers alike. However, alongside its potential, the ethical considerations, including privacy, security, and bias, are scrutinized, emphasizing the necessity for responsible and ethical AI deployment. Furthermore, the review of various AI tools and techniques provides practical insights for practitioners in the field. The paper concludes by highlighting areas for future research, particularly in developing ethical frameworks and exploring diverse AI applications in various mobile homestay contexts, to further understand and leverage the potent synergy between AI and design in crafting innovative, sustainable, and user-centric spaces.

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Pawar, Dr Suvarna. "Advancing Interview Preparation: An AI-driven Approach." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 02 (February 16, 2024): 1–13. http://dx.doi.org/10.55041/ijsrem28705.

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This research paper presents a transformative approach to interview preparation through the integration of cutting-edge artificial intelligence and personalized learning techniques. In today's competitive job market, traditional methods of interview readiness often lack the immediacy and tailored support needed for success. Addressing this gap, our platform utilizes advanced AI algorithms to analyze user responses, offer real-time feedback, and dynamically adjust question difficulty based on performance. By prioritizing user-centric design and ethical considerations, our research explores the development, implementation, and evaluation of this innovative solution. Insights from user feedback and performance metrics underscore the platform's effectiveness in enhancing interview skills and fostering confidence among job seekers. This paper contributes to the broader discourse on AI-driven learning technologies and their potential to empower individuals in navigating the complexities of professional advancement. Key Words: Interview Preparation, Artificial Intelligence, Natural Language Processing, Personalized Learning, Skill Development.

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Tan, Seng-Keong, Siew-Chin Chong, Kuok-Kwee Wee, and Lee-Ying Chong. "Personalized Healthcare: A Comprehensive Approach for Symptom Diagnosis and Hospital Recommendations Using AI and Location Services." Journal of Informatics and Web Engineering 3, no. 1 (February 14, 2024): 117–35. http://dx.doi.org/10.33093/jiwe.2024.3.1.8.

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Utilizing digital advancements, an integrated Flask-based platform has been engineered to centralize personal health records and facilitate informed healthcare decisions. The platform utilizes a Random Forest model-based symptom checker and an OpenAI API-powered chatbot for preliminary disease diagnosis and integrates Google Maps API to recommend proximal hospitals based on user location. Additionally, it contains a comprehensive user profile encompassing general information, medical history, and allergies. The system includes a medicine reminder feature for medication adherence. This innovative amalgamation of technology and healthcare fosters a user-centric approach to personal health management.

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Більше джерел

Частини книг з теми "User-Centric AI"

Eckhardt, Regina, and Sikha Bagui. "A User-centric Focus for Detecting Phishing Emails." In AI, Machine Learning and Deep Learning, 313–33. Boca Raton: CRC Press, 2023. http://dx.doi.org/10.1201/9781003187158-23.

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Cirqueira, Douglas, Dietmar Nedbal, Markus Helfert, and Marija Bezbradica. "Scenario-Based Requirements Elicitation for User-Centric Explainable AI." In Lecture Notes in Computer Science, 321–41. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-57321-8_18.

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Hu, Qihan, Zhenghui Xu, Peng Du, Hao Zeng, Tongqing Ma, Youbing Zhao, Hao Xie, et al. "CanFuUI: A Canvas-Centric Web User Interface for Iterative Image Generation with Diffusion Models and ControlNet." In AI-generated Content, 128–38. Singapore: Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-7587-7_11.

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Cirqueira, Douglas, Markus Helfert, and Marija Bezbradica. "Towards Design Principles for User-Centric Explainable AI in Fraud Detection." In Artificial Intelligence in HCI, 21–40. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-77772-2_2.

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Covelo de Abreu, Joana. "The “Artificial Intelligence Act” Proposal on European e-Justice Domains Through the Lens of User-Focused, User-Friendly and Effective Judicial Protection Principles." In Multidisciplinary Perspectives on Artificial Intelligence and the Law, 397–414. Cham: Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-41264-6_21.

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AbstractEuropean e-Justice aims at developing electronic tools to allow national jurisdictions and ECJ to contact through reliable and secure digital channels. The 2019–2023 e-Justice Strategy underlined some new EU general principles directly developed under e-Justice paradigm, deserving particular attention the ones concerning user-focused and user-friendly dimensions. As 2021 is the year where justice digitalization will be under discussion, there is a need to understand how AI will impact on justice fields, not only in MS judicial systems (EU functional jurisdictions, when applying EU law), but also in ECJ, as this disruptive technology is being discussed. The Proposal for an AI Act stresses AI systems intended for the administration of justice should be classified as high-risk, considering their potentially significant impact on effective judicial protection domains. Therefore, this paper intends to understand the need to fully stress AI human-centric approach on justice fields, so effective judicial protection can be deepened through user-focused and user-friendly principles; and to scrutinize, from the e-Justice standpoint, how the Proposal for an AI Act must further address judicial instrumental usage of AI systems, so judicial independence, procedural rights and access to justice are observed in the EU jurisdictional setting.

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Rai, Rahul K., Reshu Bansal, Shashi Shekhar Jha, and Rahul Narava. "Assessing the Utility of GAN-Generated 3D Virtual Desert Terrain: A User-Centric Evaluation of Immersion and Realism." In AI Technologies and Virtual Reality, 179–91. Singapore: Springer Nature Singapore, 2024. http://dx.doi.org/10.1007/978-981-99-9018-4_13.

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Theis, Sabine, Sophie Jentzsch, Fotini Deligiannaki, Charles Berro, Arne Peter Raulf, and Carmen Bruder. "Requirements for Explainability and Acceptance of Artificial Intelligence in Collaborative Work." In Artificial Intelligence in HCI, 355–80. Cham: Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-35891-3_22.

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AbstractThe increasing prevalence of Artificial Intelligence (AI) in safety-critical contexts such as air-traffic control leads to systems that are practical and efficient, and to some extent explainable to humans to be trusted and accepted. The present structured literature analysis examines $$n = 236$$ articles on the requirements for the explainability and acceptance of AI. Results include a comprehensive review of $$n = 48$$ articles on information people need to perceive an AI as explainable, the information needed to accept an AI, and representation and interaction methods promoting trust in an AI. Results indicate that the two main groups of users are developers who require information about the internal operations of the model and end users who require information about AI results or behavior. Users’ information needs vary in specificity, complexity, and urgency and must consider context, domain knowledge, and the user’s cognitive resources. The acceptance of AI systems depends on information about the system’s functions and performance, privacy and ethical considerations, as well as goal-supporting information tailored to individual preferences and information to establish trust in the system. Information about the system’s limitations and potential failures can increase acceptance and trust. Trusted interaction methods are human-like, including natural language, speech, text, and visual representations such as graphs, charts, and animations. Our results have significant implications for future human-centric AI systems being developed. Thus, they are suitable as input for further application-specific investigations of user needs.

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Saxena, Aakansha C., Adhishree Ojha, Daksh Sobti, and Alex Khang. "Artificial Intelligence (AI)-Centric Model in the Metaverse Ecosystem." In Advances in Computational Intelligence and Robotics, 1–24. IGI Global, 2023. http://dx.doi.org/10.4018/978-1-6684-8851-5.ch001.

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Metaverse is a prodigy that combines the real and digital worlds, enabling avatars to participate in a variety of activities. AI will have an influential impact on the future of Metaverse, as it will enable Metaverse to be user-centric by introducing features like augmented reality and virtual reality. This chapter will provide insights about how AI-centric modeling and AI concepts can boost the emerging world of Metaverse. AI will be an indispensable component of Metaverse, from the foundational layer to the experiential layer. AI will enable Metaverse to be user-centric by introducing features like augmented reality and virtual reality, creating an immersive experience for the user.

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Sundaramoorthy, K., Ajeet Singh, G. Sumathy, A. Maheshwari, A. R. Arunarani, and Sampath Boopathi. "A Study on AI and Blockchain-Powered Smart Parking Models for Urban Mobility." In Handbook of Research on AI and ML for Intelligent Machines and Systems, 223–50. IGI Global, 2023. http://dx.doi.org/10.4018/978-1-6684-9999-3.ch010.

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Urban problems like traffic jams and a lack of parking spaces can be solved in an innovative way with the help of smart parking models powered by AI and blockchain technology. These models enhance user experience, optimise space allocation, and shorten search times. Predictive analytics and real-time data from IoT sensors direct drivers to available parking spaces, minimising traffic and environmental impact. By protecting user privacy, controlling access, and securing transactions, blockchain technology improves AI. Users are empowered by blockchain-based decentralised digital identities, which also guarantee data privacy and transparent business dealings. With less traffic, more user happiness, and significant cost savings, this combination produces user-centric, environmentally friendly, and cost-effective smart parking solutions. The cost-benefit analysis for AI and blockchain-powered smart parking demonstrates a favourable return on investment, paving the way for smarter, greener cities and more interconnected urban settings.

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Manolova, Monika. "Ethical Risks in the Cross Section of Extended Reality (XR), Geographic Information Systems (GIS), and Artificial Intelligence (AI)." In Applied Ethics in a Digital World, 199–215. IGI Global, 2022. http://dx.doi.org/10.4018/978-1-7998-8467-5.ch014.

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The ethical risks which emerge from the cross section of artificial intelligence, extended reality, and geographic information systems could be examined in two broad categories of environmental and user-centric interactions of human beings with AI-curated mixed realities. These categories resonate with the capacity of AI to significantly impact the efficient application of extended reality technologies, while utilizing geodata and behavioral modelling to alter and transform experiences. While regulatory frameworks are catching up with the rights of users in the digital economy, the recently accelerated growth of immersive technologies provides further scenarios and use cases, which ought to be considered for their capacity to amplify biases, produce alternative realities, and affect human emotions.

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Тези доповідей конференцій з теми "User-Centric AI"

Wang, Danding, Qian Yang, Ashraf Abdul, and Brian Y. Lim. "Designing Theory-Driven User-Centric Explainable AI." In CHI '19: CHI Conference on Human Factors in Computing Systems. New York, NY, USA: ACM, 2019. http://dx.doi.org/10.1145/3290605.3300831.

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Li, Chen, Wang Hua, Ai Ming, and Sun Shaohui. "AI-native User-Centric Network for 6G." In 2022 IEEE/CIC International Conference on Communications in China (ICCC Workshops). IEEE, 2022. http://dx.doi.org/10.1109/icccworkshops55477.2022.9896699.

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Kumar, Rajesh, Csaki Gabor, and Michael Schmidts. "User centric AI-based similar defect recommendation system." In 2023 Second International Conference On Smart Technologies For Smart Nation (SmartTechCon). IEEE, 2023. http://dx.doi.org/10.1109/smarttechcon57526.2023.10391348.

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Zhang, Zhaonian, and Richard Jiang. "User-Centric Democratization towards Social Value Aligned Medical AI Services." In Thirty-Second International Joint Conference on Artificial Intelligence {IJCAI-23}. California: International Joint Conferences on Artificial Intelligence Organization, 2023. http://dx.doi.org/10.24963/ijcai.2023/702.

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Democratic AI, aiming at developing AI systems aligned with human values, holds promise for making AI services accessible to people. However, concerns have been raised regarding the participation of non-technical individuals, potentially undermining the carefully designed values of AI systems by experts. In this paper, we investigate Democratic AI, define it mathematically, and propose a user-centric evolutionary democratic AI (u-DemAI) framework. This framework maximizes the social values of cloud-based AI services by incorporating user feedback and emulating human behavior in a community via a user-in-the-loop iteration. We apply our framework to a medical AI service for brain age estimation and demonstrate that non-expert users can consistently contribute to improving AI systems through a natural democratic process. The u-DemAI framework presents a mathematical interpretation of Democracy for AI, conceptualizing it as a natural computing process. Our experiments successfully show that involving non-tech individuals can help improve performance and simultaneously mitigate bias in AI models developed by AI experts, showcasing the potential for Democratic AI to benefit end users and regain control over AI services that shape various aspects of our lives, including our health.

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Bader, Elias, Dominik Vereno, and Christian Neureiter. "Facilitating User-Centric Model-Based Systems Engineering Using Generative AI." In Workshop on Model-based System Engineering and Artificial Intelligence. SCITEPRESS - Science and Technology Publications, 2024. http://dx.doi.org/10.5220/0012623200003645.

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Parikh, Rahil, Himanshu Nimonkar, Riddhi Gandhi, Tarash Budhrani, Ashwini Dalvi, and Irfan Siddavatam. "Streamlining Educational Assessment: A User-Centric Analysis of an AI-Powered Examination App." In 2023 6th International Conference on Advances in Science and Technology (ICAST). IEEE, 2023. http://dx.doi.org/10.1109/icast59062.2023.10454906.

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Ahmad, Atif, and Preetii Raman. "AUTOMATED ANALYSIS AND GRADING OF PRIVACY POLICIES: AI-DRIVEN APPROACH FOR USER-CENTRIC DASHBOARD." In 18th International Technology, Education and Development Conference. IATED, 2024. http://dx.doi.org/10.21125/inted.2024.0715.

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Zhang, Yueze, Zheng Zeng, Tianrui Wang, Zekai Chen, and Ping Chen. "Reimagining Public Utilities through AI-Driven User-Centric Multimodal Interaction: A Case Study on the Lighthouse System." In CHCHI 2023: Chinese CHI 2023. New York, NY, USA: ACM, 2023. http://dx.doi.org/10.1145/3629606.3629616.

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Brillowski, Florian, Luisa Vervier, Thomas Schemmer, Philipp Brauner, Martina Ziefle, and Thomas Gries. "User Centered Design and Evaluation of an Artificial Intelligence based Process Recommender System in Textile Engineering." In 13th International Conference on Applied Human Factors and Ergonomics (AHFE 2022). AHFE International, 2022. http://dx.doi.org/10.54941/ahfe1001709.

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Despite digitization and automation in everyday life and at the workplace, traditional craftsmanship continues to be primarily analogue and manual. This specifically applies to decision-making processes that are predominantly influenced by experience and intuition. As a result, established best-practice solutions are commonly used and promising alternatives are overlooked. AI-based decision support tools are a viable option to automate and objectify the decision-making process. Additionally, these tools can help stimulate decision makers to break with common best-practice solutions and consider novel, promising alternatives. However, using AI may lead to lower social acceptance among users, due to scepticism about effectiveness, workers’ fear of being eventually substituted, and missing comprehensibility of the suggestions due to the black box-models of many AI systems. Currently, there is a lack of grounded guideline for designing and implementing user-oriented AI-based decision support systems in traditional craftsmanship.This contribution investigates how a user-centred design of AI-based decision support influences user acceptance and usage intention. For this purpose, two AI-based process recommender systems for planning textile reinforced composite processes are designed with varying focus (user-centred and purely functional). Both applications are then benchmarked in a mixed-method user study with qualitative (think aloud) and quantitative (survey) parts and 17 domain experts. We used an Excel-based decision support system as a reference, since it realistically represents the currently prevailing planning support in manufacturing companies. In the user study we evaluate the planning efficiency, objectivity, and user orientation by measuring the duration of the planning process, the result quality, consistency, and reproducibility of the designed process chains and the usability of the system. Additionally, trust in automation, the performance expectation, as well as the intention to use are measured based on the acceptance models of Körber and Venkatesh et al. and are supplemented by additional items (e.g., comprehensibility). The results of the study suggest that an AI-based support system can increase the speed and objectivity of the decision-making process. However, it is also important to design the system in a user-centric way to ensure usability, trust, and acceptance. Further, we found that it is reasonable to leave the final decision-making authority with the decision maker, since our participants tended to less frequently question a completely automated result. Based on the results of our study, we derive actionable guidelines for the design of AI-based support systems in manufacturing.

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Wei, Yishen, and Kian Azadi. "Fostering Inclusivity and Body Positivity: An AI-Driven Fashion Recommendation System for Mitigating Body Dysmorphic Disorder Effects." In 12th International Conference on Software Engineering and Applications. Academy & Industry Research Collaboration, 2023. http://dx.doi.org/10.5121/csit.2023.131704.

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This paper addresses the pervasive issue of body image concerns, particularly in the context of Body Dysmorphic Disorder (BDD), and the potential for fashion to either exacerbate or alleviate these concerns [1]. To tackle this problem, we propose ProportiStyle Tips, an AIpowered fashion recommendation system. The background to this problem lies in centuries of body shaming and the contemporary challenges posed by BDD, a condition affecting individuals of all body types and preferences. ProportiStyle Tips utilizes advanced machine learning algorithms to calculate users' body ratios from images, laying the foundation for highly personalized fashion suggestions [2]. Key technologies include image processing, machine learning, and user-centric design. Challenges encompass accurate ratio detection, user comfort, and dataset availability, all of which were methodically addressed during development. Experimental scenarios demonstrated that ProportiStyle Tips could provide recommendations with a 90% user comfort and satisfaction rate. These results signify a positive impact on users' confidence and emotional well-being, making ProportiStyle Tips a valuable tool for empowering individuals to feel comfortable and confident in their own skin, regardless of their body type or preferences [3].

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