Artificial Intelligence Design Research Articles

The dark side of artificial intelligence in services

ABSTRACT Artificial intelligence (AI) initiatives, including Generative AI, are being increasingly implemented in service industries, and are having a great impact on service operations and on customers’ reactions and behaviors. Previous literature is overoptimistic about AI implementation, and there is still a need to explore the dark side of this technology; that is, its potential negative impacts on consumers, businesses, and society, as well as the moral concerns associated with AI use in services. To establish some fundamental insights related to this research domain, this paper contributes to previous AI based-services literature by proposing a three-part conceptual model inspired by Belanche et al. (2020a), comprised of AI design, customers, and the service encounter. Specifically, we identify key factors and research gaps within each category that need to be addressed. The final research questions provide a research agenda to guide scholars and help practitioners implement AI-based services while avoiding their potential negative outcomes.

Artificial Intelligence Gamers Based on Deep Reinforcement Learning

This study investigates the design and implementation of Artificial Intelligence (AI) game players based on deep reinforcement learning, offering a novel approach to autonomous decision-making and strategy acquisition in intelligent games. Initially, the fundamental principles and algorithms of deep reinforcement learning are introduced, along with the fusion of deep learning and reinforcement learning. Subsequently, existing research is reviewed, and the pros and cons of current methodologies are examined, highlighting the underlying issues and challenges. The utilization of AI players in mainstream games is then introduced, and the influence of AI players on contemporary games is analyzed. Through this analysis of AI players in mainstream games, the strengths and weaknesses of current AI players are identified, and recommendations for optimizing them are provided. This study holds significant implications for guiding the design and development of intelligent game players, while also enriching the application of deep reinforcement learning within the gaming domain.

Exploring Defuturing to Design Artificial-Intelligence Artifacts: A Systemic-Design Approach to Tackle Litigiousness in the Brazilian Judiciary

From the perspective of defuturing design philosophy, this article discusses the close relationship between the growing body of artificial-intelligence (AI) artifacts in the Brazilian Judiciary and the phenomenon of litigiousness therein. Litigiousness has traditionally been tackled through mechanisms that increase productivity and efficiency in case processing, a strategy that has not succeeded in reducing litigiousness, as data make evident. Analyzing data from relevant sources, this article demonstrates that AI artifacts mostly perform tasks related to clustering and mass handling of cases, following the same path dependency. Consequently, they entail risks of judges’ alienation and loss of agency, which can negatively impact citizens’ fundamental rights. Moreover, they defuture; that is, they erase other (preferable) futures. Albeit AI artifacts can play a part in tackling litigiousness, there should be a critical reflection upon futuring and defuturing. Therefore, this article recommends that SoDF—a systemic approach to design that seeks to explore design consequences, futuring and defuturing—be mandatory to any AI design process. Additionally, it proposes continuous judicial monitoring for alienation and loss of agency, as well as investments in judicial education to empower judges to effectively control and supervise AI artifacts. Finally, it suggests a further research agenda.

Stochastic Computing Convolutional Neural Network Architecture Reinvented for Highly Efficient Artificial Intelligence Workload on Field-Programmable Gate Array.

Stochastic computing (SC) has a substantial amount of study on application-specific integrated circuit (ASIC) design for artificial intelligence (AI) edge computing, especially the convolutional neural network (CNN) algorithm. However, SC has little to no optimization on field-programmable gate array (FPGA). Scaling up the ASIC logic without FPGA-oriented designs is inefficient, while aggregating thousands of bitstreams is still challenging in the conventional SC. This research has reinvented several FPGA-efficient 8-bit SC CNN computing architectures, i.e., SC multiplexer multiply-accumulate, multiply-accumulate function generator, and binary rectified linear unit, and successfully scaled and implemented a fully parallel CNN model on Kintex7 FPGA. The proposed SC hardware only compromises 0.14% accuracy compared to binary computing on the handwriting Modified National Institute of Standards and Technology classification task and achieved at least 99.72% energy saving per image feedforward and 31× more data throughput than modern hardware. Unique to SC, early decision termination pushed the performance baseline exponentially with minimum accuracy loss, making SC CNN extremely lucrative for AI edge computing but limited to classification tasks. The SC's inherent noise heavily penalizes CNN regression performance, rendering SC unsuitable for regression tasks.

Machine ex machina: A framework decentering the human in AI design praxis

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.

Developing and evaluating a design method for positive artificial intelligence

Abstract In an era where artificial intelligence (AI) permeates every facet of our lives, the imperative to steer AI development toward enhancing human wellbeing has never been more critical. However, the development of such positive AI poses substantial challenges due to the current lack of mature methods for addressing the complexities that designing AI for wellbeing poses. This article presents and evaluates the positive AI design method aimed at addressing this gap. The method provides a human-centered process for translating wellbeing aspirations into concrete interventions. First, we explain the method’s key steps: (1) contextualizing, (2) operationalizing, (3) designing, and (4) implementing supported by (5) continuous measurement for iterative feedback cycles. We then present a multi-case study where novice designers applied the method, revealing strengths and weaknesses related to efficacy and usability. Next, an expert evaluation study assessed the quality of the case studies’ outcomes, rating them moderately high for feasibility, desirability, and plausibility of achieving intended wellbeing benefits. Together, these studies provide preliminary validation of the method’s ability to improve AI design, while identifying opportunities for enhancement. Building on these insights, we propose adaptations for future iterations of the method, such as the inclusion of wellbeing-related heuristics, suggesting promising avenues for future work. This human-centered approach shows promise for realizing a vision of “AI for wellbeing” that does not just avoid harm, but actively promotes human flourishing.

Enhancing child rights protection in Nigeria through artificial intelligence (AI)

This study explored how Artificial Intelligence (AI) could be used to promote child protection. Critical literature review method was employed in the exploration of the state-of-the-art literature on AI. The study was anchored on the Agenda Setting Theory and the Media and Precaution Adoption Model. It was ascertained that AI could be used positively to enhance child protection. Also, there are some challenges in the use of AI for child protection, some of which are : low awareness in the media, sharenting by parents, non-human-centric AI design by technology industries, and ignorance of some security operatives in the use of AI applications online. It is recommended among other things that parents should not compromise the privacy of their children through sharenting and should monitor the type of information their children are exposed to through AI technologies. States should devise concrete measures to implement child rights acts that are already in the constitution ; ensure that information is available to children themselves on exercising their rights ; the media should create awareness about the use of AI in child protection ; and that the technology industry should design child-friendly AI systems.

Principles Governing Ethical Development and Deployment of AI

The ethical development and deployment of artificial intelligence (AI) is a rapidly evolving field with significant implications for society. This paper delves into the multifaceted ethical considerations surrounding AI, emphasising the importance of transparency, accountability, and privacy. By conducting a comprehensive review of existing literature and case studies, it highlights key ethical issues such as bias in AI algorithms, privacy concerns, and the societal impact of AI technologies. The study underscores the necessity for robust governance frameworks and international collaboration to address these ethical challenges effectively. It explores the need for ongoing ethical evaluation as AI technologies advance, particularly in autonomous systems. The paper emphasises the importance of integrating ethical principles into AI design from the outset, fostering sustainable practices, and raising awareness through education. Furthermore, the paper examines current regulatory frameworks across various regions, comparing their effectiveness in promoting ethical AI practices. The findings suggest a global consensus on key ethical principles, though their implementation varies widely. By proposing strategies to ensure responsible AI innovation and mitigate risks, this research contributes to the ongoing discourse on the future of AI ethics, aiming to guide the development of AI technologies that uphold human dignity and contribute to the common good. Research the ethical considerations and societal impacts of AI, focusing on issues like bias in AI algorithms, privacy concerns, or the effect on employment. This can involve a comprehensive review of existing literature and case studies.

Vulnerability Assemblages: Situating Vulnerability in the Political Economy of Artificial Intelligence

“Vulnerability” is one of the terms recently used to discuss ethical aspects of artificial intelligence (AI). Current discussions on AI vulnerability tend to individualize vulnerability, largely neglecting its political dimensions, which are rooted in systems of inequality and disadvantage. This article draws on data from a multiple-perspective qualitative interview study to explore how notions of vulnerability underpin the development and implementation of AI. Results uncover how AI designers use narratives around missing data on vulnerable populations as justifications for the creation of synthetic data that were artificially manufactured rather than generated by real-world events. Although this was a profitable business model for AI companies, these practices ultimately situated long-term care residents as voiceless in the development of AI. This contribution shows how vulnerability is situated in a political economy of AI, which understands the absence of data on vulnerable groups as a possibility of profit creation rather than a chance of fostering inclusion.

Explicating AI Literacy of Employees at Digital Workplaces

This article aims to understand the definition and dimensions of artificial intelligence (AI) literacy. Digital technologies, including AI, trigger organizational affordances in workplaces, yet few studies have investigated employees’ AI literacy. This article uses a bibliometrics analysis of 270 articles to explore the meaning of AI literacy of employees in the extant literature. Descriptive statistics, keyword co-occurrence analysis, and a hierarchical topic tree are employed to profile the research landscape and identify the core research themes and relevant papers related to AI literacy's definition, dimensions, challenges, and future directions. Findings highlight four sets of capabilities associated with AI literacy, namely technology-related, work-related, human-machine-related, and learning-related capabilities, pointing also to the importance of operationalizing AI literacy for non AI professionals. This result contributes to the literature associated with technology management studies by offering a novel conceptualization of AI literacy and link it to the employee's role in digital workplaces. We conclude by inviting researchers to examine the effect of employee-technology interactions on employees’ AI literacy, which might improve the design and use of AI.

Energy Conservation: Utilization of Artificial Intelligence (AI) in Entertainment Industrial Design, Case Study on Jalan Sutomo, Medan

Energy conservation is a major concern in today’s entertainment industry due to the increasing awareness of the need for sustainable resource management. This research proposes an approach that integrates artificial intelligence (AI) and green design to improve energy efficiency in various aspects of entertainment industry buildings. First, we explore the role of AI in optimizing energy consumption and improving operational efficiency in entertainment venues such as cinemas, theaters, and shopping malls. Next, we discuss eco-design which includes the use of eco-friendly materials, waste management, and the use of renewable energy sources in the construction and operation of entertainment facilities. Through the incorporation of AI technology and sustainable design, this research highlights the potential to create a more energy-efficient and environmentally friendly entertainment industry, while making a positive contribution to global conservation efforts.

Research on the Future Trends of the Integration of Artificial Intelligence and Fashion Design of Clothing

Abstract Along with the flow of clothing designer personnel, clothing style design technology will be lost, so the intelligent design of the clothing industry, with the development of artificial intelligence technology, has received great attention. In this paper, based on the real-time style migration algorithm to study and design the local pattern of clothing, based on the principle of real-time style migration of images with perceptual loss function, it is proposed to optimize the real-time style migration network by using the group normalization method to obtain the style migration image that is more in line with the visual habit. The fusion of the style migration image with the garment base map through Poisson fusion achieves the end-to-end intelligent design of garment local patterns. The AdaIN-based style migration network model designed in this paper has an average accuracy of 75.758% when trained. Then, the model is applied to the clothing fashion design to evaluate the effect of the garment. The average scores of the overall, front, side, and back evaluation of the dressing effect of the garment are 4.04, 4.15, 4.01, and 3.92, respectively. The garments show a better effect. AI technology will bring more innovative possibilities to the clothing fashion industry and inject new vitality into the fashion industry.

Artificial Intelligence and Drug Design: Future Prospects and Ethical Considerations

Artificial Intelligence and Drug Design: Future Prospects and Ethical Considerations

Artificial Intelligence and Digital Museum VR Environment Design Based on Embedded Image Processing

Abstract As a carrier of human cultural heritage, it is of great practical significance to apply VR technology to the construction of modern digital museums. This paper combines artificial intelligence and SNN algorithms for image segmentation and proposes the overall framework of ‘embedded image acquisition and SNN segmentation processing’. This system is utilized to improve the picture clarity and delay efficiency of VR technology and reduce the vertigo feeling of the user experience. The VR environment of the digital museum is designed according to the user’s intentions. Finally, the effectiveness of the designed museum VR environment is verified by evaluating the comfort, interaction, and emotional experience of the VR museum. The experimental results show that in terms of comfort experience, the SSQ scale score of the VR museum experience based on the embedded image processing is 7.1, which is nearly double the score of the original VR museum experience. In terms of interaction and emotional experience, the overall satisfaction score of the VR environment design of the M museum was 9.4. The two aspects of emotional experience and cultural communication effects were evaluated as 4.27 and 4.05, respectively. It indicates that the user satisfaction of the VR environment design of the AI and digital museum based on the embedded image processing is high, and that this VR environment design can effectively improve the communication of the museum.

Plot Dynamic Adaptation and Character Interaction Design of Artificial Intelligence in Musical Theater Performing Arts

Abstract In recent years, artificial intelligence technology has received widespread attention in the field of artistic creation. This paper proposes the use of intelligent software to create a musical work that matches the emotions of the audience when they watch a musical performance so as to mobilize the development of the musical plot and to involve the virtual digital man in the performance of the musical to increase the interactivity of the characters. Both the music automatic generation software and the application of the virtual digital person need to match the rhythm of the performance. Accordingly, this paper proposes a multimodal emotion recognition model based on deep learning, which is applied to recognize the audience’s emotions in real time to create music that matches the plot development and reasonable role interaction. Through simulation experiments, the average recognition rate of multimodal based on decision layer fusion is 84.8%, which is slightly higher than the average recognition rate based on feature fusion and much higher than the average recognition rate of single-modal speech emotion (67.5%) and face expression (78.5%). 76% and 73% of the audience members liked the “music” and “character” elements of the musical performance with AI technology, and 62% of the audience members expressed their desire to watch it again.

Design of Artificial Intelligence Driven Crowd Density Analysis for Sustainable Smart Cities

Design of Artificial Intelligence Driven Crowd Density Analysis for Sustainable Smart Cities

Bridging artificial intelligence and cognitive ergonomics: a call for synergy

There is considerable potential between the field of artificial intelligence (AI) and cognitive ergonomics. The purpose of this thesis is to support increasing attention to the synergy between artificial intelligence and cognitive ergonomics, as well as the urgent need to explore it in research and practice.Artificial intelligence (AI) has become an integral part of the modern workplace, transforming industries, increasing productivity, and streamlining processes. However, as AI technologies continue to evolve, it is essential to consider the human side of this digital transformation. Ergonomics, the science of designing workspaces and tools that fit the people who use them, is a critical element in ensuring that the integration of artificial intelligence into the workplace increases employee well-being, efficiency, and overall success.
 With its rapid advancements, artificial intelligence is revolutionizing various fields and industries, from healthcare to self-driving vehicles and financial services. While the potential benefits are obvious, the ethical, cognitive, and ergonomic implications are just as important. Cognitive ergonomics is a sub-branch of ergonomics that deals with the design and evaluation of systems and technologies that support human cognitive processes such as perception, attention, memory, and decision-making. The field of cognitive ergonomics, which primarily focuses on Optimizing human-system interaction is the key to ensuring that artificial intelligence systems are designed and implemented in a way that aligns with human capacities, abilities, and cognitive limitations . Current AI implementations typically adopt a technology-driven focus, and employees are expected to adapt to the technology. In this technology-driven focus, the performance, performance, and accuracy of AI are optimized, but these aspects are considered separately. This point of view raises various critical considerations that are often neglected in the design and implementation of advanced technologies and sometimes have disastrous consequences. From the point of view of ergonomics, the design of artificial intelligence should be transferred from a technology-oriented focus to a systemic and human perspective. By applying a focus on personnel, artificial intelligence should be meaningfully and safely designed and integrated into work processes with the aim of optimizing overall system performance and people's well-being.

Design and development of artificial intelligence assisted railway gate controlling system using internet of things

A improvised solution for the Indian Railways' human level crossing controls and the attendant reliability issues they cause. In our nation, level crossings have been the scene of several train accidents. So far, there have been zero productive actions. Using an infrared (IR) detector and an audible alert, the proposed system automatically opens and closes level crossing gates based on when trains approach or depart from the crossing. However, it's possible that a car may become stuck between the pedestrian crossing barriers if they're automated. Here, an ultrasonic sensor may identify the obstruction between the crossing gates, and an Internet of Things (IoT) module would inform the train of the situation. When a train approaches or departs from a railroad crossing, infrared sensors detect its presence and alert an Arduino-UNO, which then commands the gates to open or close using a little servomotor stationed nearby. Using the Node MCU and GPS navigational technology, this system also aims to reduce latency in detecting combustion attacks within the compartment. The resulting section of this paper proves the proposed work efficiency by means of graphically illustrates the following metrics such as: human detection accuracy, gate closing efficiency, alerting system efficiency and data storage efficiency.

Bridging the engineering skill gap in Nigeria: Preliminary findings and recommendations of the E4I survey

The paper presents the key findings and recommendations of an evidence-based survey report on the engineering skill gap among young engineering graduates in Nigeria. The outcome of the study highlighted the lack of technical, problem-solving, research and development, and management skills in the country’s young engineering graduates. Integrating emerging technologies like artificial intelligence, additive manufacturing, virtual reality, computer programming, computer-aided manufacturing, and computer-aided design into the curriculum was strongly recommended for the training of young engineering students in Nigerian higher education institutions. Other recommendations for skill augmentation include; enabling young graduates to develop their optimisation, modelling, simulation, design, creativity, technical, practical, machining, operation, and management skills to boost their hands-on engineering proficiencies. Strong emphasis was also on the importance of a one-year post-graduation internship program to upskill all young engineering graduates and provide a head-start in industrial settings. Lastly, adopting an outcome-based education (OBE) engineering training curriculum was recognised as crucial for addressing the skills gap.

Innovative Applications of Image Media and Intelligent Service Systems

With the continuous development of artificial intelligence, image media, and intelligent service systems are being constantly innovated and improved. Personalized customization, multimodal interaction, wearable devices and smart homes, artificial intelligence, and sustainable design will be the trend of future development. The intelligence and personalized customization of human-computer interaction will become the focus of future development, and sustainable design will also receive increasing attention. This study has significant reference value for the innovative application of image media and intelligent service systems.