Breaking Boundaries through Collaboration: A Human-Centered Framework for Fair AI Design

Purpose One of the major barriers to implementing artificial intelligence (AI) in fashion design is possibly higher consumer reluctance to accept AI-designed (vs. human-designed) products. How can brands alleviate the negative responses to AI-designed products? To answer this question, this research tests the role of product innovativeness in determining the levels of consumer resistance to AI designs. Design/methodology/approach The hypotheses were developed based on the literature on algorithm aversion and appreciation, fashion design evaluation, and mind perception theory. To test the hypotheses, we conducted three online experiments using Amazon Mturk through CloudResearch platform. Findings While a general preference for human designs over AI designs was found, the negative attitudes toward AI designs were stronger for low-innovative products but weaker for high-innovative products. This is because, according to the conditional process analysis, participants perceived AI-designed products as less original compared to human-designed products when innovativeness level was low. However, this pattern was not shown when innovativeness level was high. Practical implications The findings show the potential for overcoming aversion to AI-designed fashion products. Brands utilizing AI in design are recommended to aim for highly innovative designs, characterized by deconstruction fashion and avant-garde approaches and emphasize innovativeness values when promoting AI-designed products. Originality/value This research sheds light on how and why consumers' negative responses to AI designs vary depending on the final product design, contributing to the discourse on fashion creativity in the era of generative AI from consumers' perspectives.

Artificial intelligence dimensions and design features for the knowledge-based work of the future: a socio-technical perspective

Chapter 13 - Application of machine learning and artificial intelligence in design, optimization, and control of power electronics converters for renewable energy-based technologies

E-marketing refers to the use of technology and artificial intelligence to analyze and interpret marketing data. In addition to providing recommendations and guidance to improve marketing campaigns, improve user experience, and increase sales. These tools include data analytics, machine learning, data classification and aggregation, voice and image recognition, and natural language. Therefore, this study aimed to identify the effect of artificial intelligence (AI) in Design on E-Marketing in Companies. This study used a survey design with a quantitative approach, with the target group being workers of E-Marketing companies. A 198 surveys were also gathered from Jordanian E-Marketing companies, however, 187 questionnaires were judged viable for study. Where the results indicate AI positively affects E-Marketing, and natural language processing, analytical models, content marketing, and digital marketing affect E-Marketing.

The Purpose of the Study: This paper addresses how artificial intelligence (AI) plays a role in the world of design and how it affects the concept of originality. The paper examines the use of AI in areas such as graphic design, logo design, painting, original designs and web design, and discusses the innovations that this technology brings to design processes. The paper also considers the positive and negative effects of AI on designers. Positive effects include the acceleration of design processes and the access to a wider creative spectrum. On the other hand, the impact of AI on originality is a controversial issue. It is questioned how original the designs produced with AI are and whether these designs have artistic value. Literature Review/Background: The impact of artificial intelligence (AI) technology in the field of design and the reconsideration of the concept of originality are mentioned. While AI offers speed and efficiency in design processes, creative solutions have been addressed through learning from data and algorithms. While the innovations and efficiency advantages offered by AI expand the creative capacities of designers, the concepts of originality and personal expression are evaluated. Methodology: The research design of the study was qualitative, document scanning method was used as the data collection method, and content analysis method was used to analyse the data. Using the document scanning method, the effects of artificial intelligence in design and the concepts of originality were discussed. Findings: The impact of artificial intelligence (AI) technology in the field of design necessitates a reconsideration of the concept of originality. While AI offers speed and efficiency in design processes, it produces creative solutions through learning from data and algorithms. However, originality in this process can often be derived from existing data or based on style transfers. While the innovations and efficiency advantages offered by AI expand the creative capacities of designers, it may cause you to question the concepts of originality and personal expression. Conclusion: the paper suggests that AI is an important tool in the design world and predicts that this technology will become even more widespread in the future. However, it is emphasised that AI should be used carefully and consciously in creative processes.

This chapter, which uses the mirror of shape grammar implementation to reflect on why shape grammars, or, more precisely, visual calculating, isn’t more popular among computational designers, proposes that visual calculating might be better understood as a tool for thinking about design, rather than as a method for designers. The chapter compares visual, symbolic and subsymbolic calculating in architectural design, concluding that, while specific shape grammars resemble symbolic artificial intelligence and foreshadowed today’s use of computational design for design automation, visual calculating appears to evade the distinction between symbolic and subsymbolic artificial intelligence methods as a less popular, third alternative. The chapter then briefly summarizes the current state of shape grammar implementation, concluding that visual computing’s lack of popularity can no longer be blamed only on an absence of powerful, usable, and faithful enough shape grammar interpreters. The chapter discusses subshape recognition as a fundamental barrier to an ideal shape grammar interpreter that allows designers to use any rules they want, whenever they want. This barrier is not so much that subshape recognition is NP-hard—which could potentially be overcome by advances in computing—but that the number of subshapes is unbounded in the most general case. As such, shape grammar implementation becomes tractable only through human judgments that limit the exponential increase of possible rule applications. This principled resistance of visual computing to computer implementation —which arises from the requirement that designers should be able to do anything they want to—demonstrates that visual computing more convincingly serves as a striking reminder of the limits of artificial intelligence than as a design method. This conclusion leads to a remarkable convergence with Rittel, who identified “Sollsetzung,” i.e., arbitrary judgment, as the limit of artificial intelligence in design. Finally, the chapter proposes that, over the next fifty years, shape computation could ask what more we can learn from visual calculating about design, and how we might use contemporary artificial intelligence methods to support further advancements in shape grammar implementation.

Evaluation of the efficiency, trueness, and clinical application of novel artificial intelligence design for dental crown prostheses

Artificial Intelligence, Machine Learning and User Interface Design is a forward-thinking compilation of reviews that explores the intersection of Artificial Intelligence (AI), Machine Learning (ML) and User Interface (UI) design. The book showcases recent advancements, emerging trends and the transformative impact of these technologies on digital experiences and technologies. The editors have compiled 14 multidisciplinary topics contributed by over 40 experts, covering foundational concepts of AI and ML, and progressing through intricate discussions on recent algorithms and models. Case studies and practical applications illuminate theoretical concepts, providing readers with actionable insights. From neural network architectures to intuitive interface prototypes, the book covers the entire spectrum, ensuring a holistic understanding of the interplay between these domains. Use cases of AI and ML highlighted in the book include categorization and management of waste, taste perception of tea, bird species identification, content-based image retrieval, natural language processing, code clone detection, knowledge representation, tourism recommendation systems and solid waste management. Advances in Artificial Intelligence, Machine Learning and User Interface Design aims to inform a diverse readership, including computer science students, AI and ML software engineers, UI/UX designers, researchers, and tech enthusiasts.

A growing body of literature has focused on understanding and addressing workplace artificial intelligence (AI) design failures. However, past work has largely overlooked the role of the devaluation of worker expertise in shaping the dynamics of AI development and deployment. In this paper, we examine the case of feminized labor: a class of devalued occupations historically misnomered as “women’s work,” such as social work, K-12 teaching, and home healthcare. Drawing on literature on AI deployments in feminized labor contexts, we conceptualize AI Failure Loops : a set of interwoven, sociotechnical failure modes that help explain how the systemic devaluation of workers’ expertise negatively impacts, and is impacted by, AI design, evaluation, and governance practices. These failures demonstrate how misjudgments on the automatability of workers’ skills can lead to AI deployments that fail to bring value to workers and, instead, further diminish the visibility of workers’ expertise. We discuss research and design implications for workplace AI, especially for devalued occupations.

CORR Synthesis: When Should the Orthopaedic Surgeon Use Artificial Intelligence, Machine Learning, and Deep Learning?

The demanding nature of informal caregiving brings challenges to caregivers' emotional and physical health. The Human-Computer Interaction (HCI) community has increasingly examined ways to support caregivering burden, but the solutions mostly focus on offloading visible caregiving burden, not so much the invisible work of caregivers. Conversely, informal caregivers go through a range of emotion work wherein they suppress, evoke, or perform a mixture of evocation and suppression of emotions in demand to the situation or context. This complexity of emotions and their management has implications that are underexplored in HCI and AI design. In my dissertation work, I aimed to understand the emotion work of informal caregivers and how we can develop a framework to generate design requirements for future AI systems to help with this work. I first conducted user studies and interviews to understand the context-specific needs of informal caregivers, what they perceived as emotion work, and how future design can help with the emotion work. I do this in two caregiving contexts: parenting of young children and Alzheimer's Disease and Related Dementias (ADRD). Then, I iteratively developed a framework that has two aims: (i) understanding the emotion work to gauge the level and scope of AI help needed and (ii) generating design requirements for AI to help with the emotion work. The framework helps HCI researchers and designers to identify, understand, and think about how AI can best help with emotion work and generate design requirements for the same. Finally, I evaluated the framework with HCI/UX students to understand the feasibility of using the framework and the perceived usefulness of the design requirements, and suggest improvements.

In this paper, we contribute to research on enterprise artificial intelligence (AI), specifically to organizations improving the customer experiences and their internal processes through using the type of AI called machine learning (ML). Many organizations are struggling to get enough value from their AI efforts, and part of this is related to the area of explainability. The need for explainability is especially high in what is called black-box ML models, where decisions are made without anyone understanding how an AI reached a particular decision. This opaqueness creates a user need for explanations. Therefore, researchers and designers create different versions of so-called eXplainable AI (XAI). However, the demands for XAI can reduce the accuracy of the predictions the AI makes, which can reduce the perceived usefulness of the AI solution, which, in turn, reduces the interest in designing the organizational task structure to benefit from the AI solution. Therefore, it is important to ensure that the need for XAI is as low as possible. In this paper, we demonstrate how to achieve this by optimizing the task structure according to sociotechnical systems design principles. Our theoretical contribution is to the underexplored field of the intersection of AI design and organizational design. We find that explainability goals can be divided into two groups, pattern goals and experience goals, and that this division is helpful when defining the design process and the task structure that the AI solution will be used in. Our practical contribution is for AI designers who include organizational designers in their teams, and for organizational designers who answer that challenge.

ObjectiveThe integration of artificial intelligence (AI) into CAD/CAM workflows has revolutionized dental prosthetics manufacturing, yet its morphological trueness compared to manual design remains underexplored.Materials and methodsThis study evaluated 30 single-tooth restoration cases from 30 patients. For each case, the original clinically-approved designs were used as reference. AI designs (3Shape Automate) were compared to manual designs created by a technician (3Shape Dental System™). Morphological trueness was evaluated through 3D deviation analysis. Global surface deviations (RMSE) were compared using the Wilcoxon signed-rank test, and maximum discrepancies were compared with a paired Student’s t-test, with significance set at p < 0.05.ResultsWhile AI demonstrated batch-processing efficiency, 6.7% of cases (2/30) with suboptimal preparation geometries required manual intervention. No significant difference was found in global surface deviation between AI (median = 79.8 μm) and manual designs (median = 68.6 μm; p = 0.1056). However, AI designs produced significantly greater maximum discrepancies (mean = 225.0 μm) compared to manual designs (mean = 184.4 μm; p = 0.0243).ConclusionThese findings validate AI’s viability for routine restoration design but emphasize the necessity of case selection protocols and algorithm improvements for dynamic occlusion modeling to ensure comprehensive clinical adoption.Supplementary InformationThe online version contains supplementary material available at 10.1186/s12903-025-07004-z.

Artificial intelligence (AI) design typically incorporates intelligence in a manner that is affirmatory of the superiority of human forms of intelligence. In this paper, we draw from relevant research and theory to propose a social-ecological design praxis of machine inclusivity that rejects the presumption of primacy afforded to human-centered AI. We provide new perspectives for how human-machine communication (HMC) scholarship can be synergistically combined with modern neuroscience’s integrated information theory (IIT) of consciousness. We propose an integrated theoretical framework with five design practice recommendations to guide how we might think about responsible and conscious AI environments of the future: symbiotic design through mutuality; connectomapping; morethan- human user storytelling, designing for AI conscious awakenings; and the revising of vernaculars to advance HMC and AI design. By adopting the boundaries HMC scholarship extends, we advocate for replacing ex machina mentalities with richer understandings of the more-than-human world formed by interconnected and integrated human, humanmade, and nonhuman conscious machines, not superior or inferior but each unique.

This paper explores the intersection of artificial intelligence (AI) design, Sadharanikaran communication principles, and various theoretical frameworks to enhance mutual understanding in human computer interaction. Drawing upon insights from Focus Group Discussions (FGDs) and evaluating the effectiveness of custom instructions and prompt engineering on ChatGPT, this study analyzes the implications of diffusion of innovation theory, human-computer interaction theory, cognitive load theory, emotional design theory, and social learning theory within the context of Sadharanikaran and AI design. By synthesizing these theoretical perspectives and empirical findings, this paper explores scopes for designing AI systems that promote Sahridayata, or mutual understanding among users.