AI Production Research Articles

Living in the Digital Age: AI Technology and the Changing Lifestyle of the Elderly

As technology advances, diverse artificial intelligence products are invented and put into use. While dramatically improving the quality of human life, digital technologies are also changing the lifestyles of different groups to varying degrees. Focusing on the elderly population, this study utilizes a combination of questionnaire, interview, and documentary research methods in hopes of collecting information in a broad and comprehensive manner. The study mainly focuses on how AI technologies in the digital age are changing the lifestyles of older people and the potential factors that lead to such changes. The study reveals that the digital age has a dual impact on the lives of older people. Thanks to the intelligence and simplicity of AI technology, older people’s lives have become much more efficient and convenient. However, the expensive price of AI devices and products, which also keeps some older people from enjoying the convenience of technological inventions, prevents them from integrating into the digital society. Among many lifestyles, changes in home-based lifestyles are the areas of greatest change in the lifestyles of the elderly in the digital age. These changes are mainly influenced by social, technological and personal factors. The present study expresses ample social concerns for the elderly population. It also conducive to helping older people fully integrate into a smart and convenient life and equally enjoy the dividends of the digital age.

The Social Identity of the Robot Pet: A multi-dimensional perspective

With the invention and application of artificial intelligence products into modern society, robot pets have become one of the important products. At present, along with the gradual integration of robot pets into human life and becoming people’s intimate companions, more and more people begin to pay attention to the social identity of robot pets. In order to clarify the social identity of robotic pets in modern society, this study used questionnaires and interviews to collect the acceptance attitudes and perspectives of residents from the Yangtze River Delta region toward robotic pets as companions. It is found that there is diversity in public acceptance of robotic pets, and that health, emotional needs, and the function of the robots are important factors influencing people’s choice of robotic pets. Regarding the social status of robotic pets, some people believe that robotic pets should be regarded as an integral part of society. While others believe they lack socio-emotional attributes and should not have a social identity. In the future, pet robots will move toward personalization, multifunctionality, or cross-industry cooperation, where their social identity is expected to be strengthened. In conclusion, this study organizes the social cognition of robotic pets, gives an empirical basis for the study of the social identity of robotic pets, and provides guidance for the future direction of AI products.

AI and work in the creative industries: digital continuity or discontinuity?

There is current uncertainty about the effects of generative AI technologies on labour markets and conditions of work in creative industries. Previous scholarship identifies the potential for both replacement and displacement of human labour due to AI adoption. This article examines potential effects of AI adoption by drawing on case studies of 6 commercial products using AI, investigating conditions of work and mechanisms by which human creative labour might be replaced or displaced by AI technology. The main findings are that AI products were more labour intensive than traditional media products because they combined traditional production skills and new computational expertise. Paradoxically, contributions by human workers tended to be invisibilised in final products that foregrounded AI technology. There was potential for displacement of human labour in the ideation phase, with AI enabling producers to explore different creative possibilities. Some AI products had the potential to compete with earlier production methods (e.g. AI-generated imagery vs. stock photography). Overall, case study findings were consistent with ongoing challenges from digitalisation: conditions of deskilling, re-skilling, flexible employment and uncertainty remain intense for small firms engaged in AI-assisted creative production.

The hidden abode of artificial intelligence production: Stretching the limits of artificial intelligence ethics and critique

The present article aims to discuss the possibility of including the sphere of artificial intelligence production within the domain of artificial intelligence ethics and investigate its moral implications. In the first section, the role of human labour in the artificial intelligence production processes is considered, with particular reference to the distinction between high-skilled and low-skilled jobs, their differential distribution in the production process itself, and the labour conditions of ghost workers, in order to analyse the main ethical issues emerging within the field. In the second section, some aspects of the existing critical literature concerning artificial intelligence and labour are discussed, focusing on Marxist and decolonial scholarship and more precisely on its lack of consideration of the global value chain through which artificial intelligence AI production processes are structured. Finally, the possibility and limits of an ethics of artificial intelligence production are reconsidered by assuming the centrality of workers’ struggles and agency along artificial intelligence's global value chain.

Responsible Crowdsourcing for Responsible Generative AI: Engaging Crowds in AI Auditing and Evaluation

With the rise of generative AI (GenAI), there has been an increased need for participation by large and diverse user bases in AI evaluation and auditing. GenAI developers are increasingly adopting crowdsourcing approaches to test and audit their AI products and services. However, it remains an open question how to design and deploy responsible and effective crowdsourcing pipelines for AI auditing and evaluation. This workshop aims to take a step towards bridging this gap. Our interdisciplinary team of organizers will work with workshop participants to explore several key questions, such as how to improve the output quality and workers' productivity for GenAI evaluation crowdsourcing tasks compared to discriminative AI systems, how to guide crowds in auditing problematic AI-generated content while managing their psychological impact, ensuring marginalized voices are heard, and setting up responsible and effective crowdsourcing pipelines for real-world GenAI evaluation. We hope this workshop will produce a research agenda and best practices for designing responsible crowd-based approaches to AI auditing and evaluation.

Large Language Publishing

The AI hype cycle has come for scholarly publishing. This essay argues that the industry’s feverishーif mostly aspirationalーembrace of artificial intelligence should be read as the latest installment of an ongoing campaign. Led by Elsevier, commercial publishers have, for about a decade, layered a second business on top of their legacy publishing operations. That business is to mine and process scholars’ works and behavior into prediction products, sold back to universities and research agencies. This article focuses on an offshoot of the big firms’ surveillance-publishing businesses: the post-ChatGPT imperative to profit from troves of proprietary “training data,” to make new AI products andーthe essay predictsーto license academic papers and scholars’ tracked behavior to big technology companies. The article points to the potential knowledge effects of AI models in academia: Products and models are poised to serve as knowledge arbitrators, by picking winners and losers according to what they makevisible. I also cite potential knock-on effects, including incentives for publishers to roll back open access (OA) and new restrictions on researchers’ access to the open web. The article concludes with a call for a coordinated campaign of advocacy and consciousness-raising, paired with high-quality, in-depth studies of publisher data harvestingーbuilt on the premise that another scholarly-publishing world is possible. There are many good reasons to restore custody to the academy, the essay argues. The latest is to stop our work from fueling the publishers’ AI profits.

Transforming Creative Process: A Systematic Literature Review of Discourse on AI Image Generators

ABSTRACTAI image generator, also known as Text‐to‐image generation (TTIG) technology, allows users to generate images based on natural language input. With advancements in AI, debates related to its application in the creative field have flourished. A systematic literature review was conducted to examine discourse that addresses the perception of people in the creative industry towards AI image generators and the impact of AI image generators on these individuals. The analysis of discourse revealed four major categories: 1) Evaluation of product or outcome of AI; 2) Impact of AI on practitioners/artists; 3) Impact of AI on creative industry and society; 4) Capabilities of AI technology. These findings provide insights into the areas of interest within AI image generator discussions on creative activity.

Empowering geoportals HCI with task-oriented chatbots through NLP and deep transfer learning

ABSTRACT In the past ten years, chatbot development has matured to become one of the most well-distinguished outcomes of artificial intelligence. Despite some criticism, Bing AI, ChatGPT and other natural language processing (NLP) products of similar nature are becoming more popular. The creation of chatbots can close several gaps in geographic information retrieval as well. This research introduces and successfully implements, for the first time, a model for integrating task-oriented chatbots into geoportals, with the goal of easing user requests, improving access to geospatial services, and fostering human-computer interactions (HCI). Additionally, it presents a novel recommendation solution for matching the most appropriate volunteer to the user’s geospatial needs based on expertise similarity, semantic similarity, and community feedback. The three categories of finding map services, discovering geoprocessing services, and volunteer expert recommendations were shown to be the most significant geoportal bot intents. Depending on the requirement, each intent additionally includes various entities such as time, place, description, skill, etc. The notion of deep transfer learning (DTL) was then put into practice by customizing a pre-trained BERT (Bidirectional Encoder Representations from Transformers) model for our particular aim and creating a task-oriented conversational agent. According to the results, effective intent classification and entity recognition in the geospatial domain could arise from this approach. We performed the training process with 200 sample data, 20% of which were utilized in a stratified manner for testing, and we obtained f1-scores of at least 0.75. Finally, a pilot Geoportal Chabot that combines crowdsourcing and conversational agents’ approaches was put into use and tested with success. In keeping with SDI technical purposes, the system might direct users to common geospatial web services, namely WMS and WPS, in addition to including natural language understanding (NLU) and natural language generation (NLG) capabilities. Result of user-centered evaluation indicated that the integration of a chatbot significantly reduces the average time required to access geospatial data and processing services by more than 50%. Notably, this effect is even more pronounced when locating an expert, with a fivefold decrease in the time required. Finally, overall user satisfaction rose from 86% to 94%.

Substitutor or Assistant: The Double-Edged Sword Effect of Artificial Intelligence Images on OTPs

Recently, the green concept has become integral to education, leading to the rise of paperless online teaching. With the rapid development of online teaching platforms (OTPs) due to the pandemic, studies on user behavior have gained momentum. However, most studies have focused on students’ online learning attitudes and behaviors, neglecting in-depth analysis of teachers’ behaviors on OTPs. OTPs can either assist or substitute teachers, enhancing efficiency but also causing anxiety. This paper proposes reframing OTPs as assistants to reduce teachers’ resistance. We investigate if the OTP image (assistant vs. substitutor) impacts teachers’ satisfaction in a specific online teaching context, exploring its explanatory mechanisms. A study of 2*2 group experiments revealed that teachers were less threatened by the assistant OTP image and thus more satisfied. Experiment 1 confirmed that the OTP image influenced teachers’ willingness to recommend and satisfaction. Experiment 2 again tested the effect of different images of OTP (facilitator vs. substitute vs. control group) on teacher satisfaction, and the pie verified the mediating role of identity threat in this effect. Experiment 3 verified that self-affirmation as a moderating variable mitigates identity threats due to the alternative image of the OTP. Therefore, in the future promotion of AI products, more emphasis should be placed on assisting users rather than completely replacing traditional human hands, thus weakening the identity threat posed by AI products to users. The findings of this study enrich the study on teachers’ attitudes towards OTPs, dissect the sources of users’ (teachers’) satisfaction with OTPs from the perspective of product/brand (OTP) image, and provide guidance on how OTPs can choose the appropriate image positioning and promotional language.

When being smart trumps AI: An exploration into consumer preferences for smart vs. AI-powered products

Prior to the rapid growth of Artificial Intelligence (“AI”) in the consumer market, smart products received great attention from marketers and consumers. Given the recent increase in attention to AI technologies, this research explores consumers' preferences and intentions when products are framed as “smart” versus “AI-powered.” While previous literature has explored AI products and smart products individually, little is known about consumers’ preferences between the two products simultaneously. Three empirical experiments demonstrate that consumers show preference for products labeled as “smart” over those labeled as “AI-powered.” This preference is mediated by enhanced learning anxiety related to AI. The findings provide insights for marketers applying message framing, suggesting that labeling products as “smart” may evoke more favorable consumer behavior compared to the “AI-powered” label. Moreover, this research significantly contributes to the existing literature on perceptions and intentions related to AI and smart products by concurrently exploring consumer preferences regarding both smart products and AI-powered products.

Foot and Mouth Disease Virus Strain-specific Antibody Titres in Naturally Infected or Vaccinated Bulls in Kenya

Foot-and-mouth disease (FMD) is a global viral infection that causes vesicular lesions in and around the mouth and feet, causing reluctance of animals to eat or move. In Kenya, bulls raised for AI receive vaccinations against FMD, but it is unclear if these animals experience vaccine-induced immunity. No research has been conducted to determine if animals in endemic areas develop natural immunity or whether animals in disease-free regions might be seropositive. This study aimed to determine the prevalence and levels of foot and mouth disease virus infection-triggered and vaccine-induced antibodies. A cross-sectional study was conducted on bulls farmed for AI production and vaccinated against FMD. Antibodies were quantified using a virus-neutralization test. One-way analysis of variance (ANOVA) and the Kruskal-Wallis test with Tukey and Dunn post-tests, respectively, were used to examine the data using the GraphPad InStat program. Additionally, the Spearman test was employed for correlation analysis and the t-test for intergroup differences analysis. A statistically significant P value was defined as less than 0.05. Findings showed protective antibody levels were present in 23%, 10.3%, 2.6%, and 7.7% of the animals in the FMD non-endemic region against the FMD virus strains O, A, SAT 1, and SAT 2, respectively. The protection provided by the O strain virus was significantly greater than that of SAT 1 (P = 0.01). In the FMD endemic area, all sampled animals showed protection levels at 100%, 100%, 100% and 29% for virus strains O, A, SAT 1, and SAT 2 respectively with the antibody titres showing significant differences (P < 0.05) for all the intergroup analysis except between strains O vs SAT 1 and A vs SAT 1 (P > 0.05). To conclude, the current research suggests that FMD may be making a comeback in the areas where the illness is not established. Furthermore, it seems that sperm recovery upon freezing is somewhat mitigated by FMDV-specific antibodies. The study advises monitoring FMD in areas where the illness is not endemic and confirms the current findings with larger sample sizes to enable more informed decision-making.

Creation Is Not Like a Box of Chocolates: Why Is the First Judgment Recognizing Copyrightability of AI-Generated Content Wrong?

Abstract The judgment of the Beijing Internet Court recognizing copyrightability of AI-generated images is flawed for three reasons. First, the judgment treats generative AI as a tool of creation akin to a brush, camera or Photoshop. But generative AI is not a passive means for the author to implement the act of creation that directly produce works; instead, it is actively involved in the decision-making process of the substance of the resulting content. Second, the judgment attaches much importance to the creative nature of the text prompts and other inputs of the user of generative AI, while it fails to make the analysis within the framework of the idea/expression dichotomy. Different generative AI systems, and even the same generative AI, may generate completely different images based on exactly the same ‘user’s inputs’. This fact shows that ‘user’s inputs’ are an unprotectable idea in relation to the outcome of the AI production, because a single creative and original idea may lead to a large number of expressions. Third, while acknowledging that the relationship between generative AI and its users is akin to the relationship between the commissioned party and the commissioner during the creation of a painting, the judgment wrongly attributes user’s authorship of AI-generated content to AI’s lack of free will and legal personality.

Research on Copyright Protection and Attribution of Artificial Intelligence Products - Focusing on Reviewing Cases of Major Foreign AI Products -

Research on Copyright Protection and Attribution of Artificial Intelligence Products - Focusing on Reviewing Cases of Major Foreign AI Products -

Exploring the paradox: Perceptions of AI in higher education – a study of hype and scandal

In the rapidly evolving field of generative artificial intelligence (GenAI), the introduction of OpenAI’s ChatGPT-4 signifies a noteworthy juncture economically, in workplaces, and in education. This article explores the dual public perception of GenAI as both promising and controversial, especially in higher education. The study is rooted in ‘information ecologies’ to understand the integration and implications of new technologies in educational settings. Employing a ‘multiplicity’ method, the article emphasizes a self-reflexive, context-sensitive approach, utilizing digital ethnography and auto-ethnographic narratives to analyse the nuanced public perception and its effects on the use of AI in academia. The research underlines the importance of a balanced integration of AI, aiming for students to become proficient in managing AI products, which is essential for aligning educational practices with modern workplace demands.

AI governance in India – law, policy and political economy

Artificial Intelligence technologies have elicited a range of policy responses in India, particularly as the Government of India attempts to position and project the country as a global leader in the production of AI technologies. Policy responses have ranged from providing public infrastructure to enable market-led AI production, to nationalising datasets in an effort to enable Big Data analysis through AI. This paper examines the recent history of AI policy in India from a critical political economy perspective, and argues that AI policy and governance in India constructs and legitimises a globally-dominant paradigm of informational capitalism, based on the construction of data as a productive resource for an information-based economic production, and encouraging self-regulation of harmful impacts by firms, even as it attempts to secure a strong hand for the state to determine, both through law and infrastructure, how such a market is structured and to what ends.

On trust in humans and trust in artificial intelligence: A study with samples from Singapore and Germany extending recent research

The AI revolution is shaping societies around the world. People interact daily with a growing number of products and services that feature AI integration. Without doubt rapid developments in AI will bring positive outcomes, but also challenges. In this realm it is important to understand if people trust this omni-use technology, because trust represents an essential prerequisite (to be willing) to use AI products and this in turn likely has an impact on how much AI will be embraced by national economies with consequences for the local work forces. To shed more light on trusting AI, the present work aims to understand how much the variables trust in AI and trust in humans overlap. This is important to understand, because much is already known about trust in humans, and if the concepts overlap, much of our understanding of trust in humans might be transferable to trusting AI. In samples from Singapore (n = 535) and Germany (n = 954) we could observe varying degrees of positive relations between the trust in AI/humans variables. Whereas trust in AI/humans showed a small positive association in Germany, there was a moderate positive association in Singapore. Further, this paper revisits associations between individual differences in the Big Five of Personality and general attitudes towards AI including trust.The present work shows that trust in humans and trust in AI share only small amounts of variance, but this depends on culture (varying here from about 4 to 11 percent of shared variance). Future research should further investigate such associations but by also considering assessments of trust in specific AI-empowered-products and AI-empowered-services, where things might be different.

Measuring User Experience Inclusivity in Human-AI Interaction via Five User Problem-Solving Styles

Motivations: Recent research has emerged on generally how to improve AI products’ Human-AI Interaction (HAI) User Experience (UX), but relatively little is known about HAI-UX inclusivity. For example, what kinds of users are supported, and who are left out? What product changes would make it more inclusive? Objectives: To help fill this gap, we present an approach to measuring what kinds of diverse users an AI product leaves out and how to act upon that knowledge. To bring actionability to the results, the approach focuses on users’ problem-solving diversity. Thus, our specific objectives were: (1) to show how the measure can reveal which participants with diverse problem-solving styles were left behind in a set of AI products; and (2) to relate participants’ problem-solving diversity to their demographic diversity, specifically gender and age. Methods: We performed 18 experiments, discarding two that failed manipulation checks. Each experiment was a 2x2 factorial experiment with online participants, comparing two AI products: one deliberately violating one of 18 HAI guideline and the other applying the same guideline. For our first objective, we used our measure to analyze how much each AI product gained/lost HAI-UX inclusivity compared to its counterpart, where inclusivity meant supportiveness to participants with particular problem-solving styles. For our second objective, we analyzed how participants’ problem-solving styles aligned with their gender identities and ages. Results & Implications: Participants’ diverse problem-solving styles revealed six types of inclusivity results: (1) the AI products that followed an HAI guideline were almost always more inclusive across diversity of problem-solving styles than the products that did not follow that guideline—but “who” got most of the inclusivity varied widely by guideline and by problem-solving style; (2) when an AI product had risk implications, four variables’ values varied in tandem: participants’ feelings of control, their (lack of) suspicion, their trust in the product, and their certainty while using the product; (3) the more control an AI product offered users, the more inclusive it was; (4) whether an AI product was learning from “my” data or other people’s affected how inclusive that product was; (5) participants’ problem-solving styles skewed differently by gender and age group; and (6) almost all of the results suggested actions that HAI practitioners could take to improve their products’ inclusivity further. Together, these results suggest that a key to improving the demographic inclusivity of an AI product (e.g., across a wide range of genders, ages, etc.) can often be obtained by improving the product’s support of diverse problem-solving styles.

Case Study of Skilful Use of LLM-Based Generative AI in Management of a Hypothetical Business

Now that generative AI technology products are already spreading worldwide and are improving their capabilities very fast, it is high time we examine what it can do to decision making, which is the essence of managing businesses. The present paper has used an innovative methodology where one of the currently available Large Language Model based product, namely ChatGPT, is given a series of questions/prompts to use its own capabilities to give its self-assessment of supporting the different dimensions of managing a business. Multiple conversations illustrated the need for well-structured prompts/questions to get the most out of ChatGPT, otherwise it can get confused and provide illogical response. The quality of mutual learning improves by constructive feedback between the human interlocutor and generative AI. Probing it further with multiple scenarios for a hypothetical mid-sized business has verified and confirmed those self-assessments. The insights and conclusions drawn from those conversations are that skillful use of the LLM-based generative AI products can assist human managers in making better quality decisions at all levels of management in a timely manner. Senior managers of businesses need to begin an effort to test-drive an available LLM-based generative AI product and plan to leverage it in the ongoing management to become a learning organization in the interest of the business.

Emerging technologies adoption and market positioning of AI products in China

Emerging technologies adoption and market positioning of AI products in China

Understanding user switch of information seeking: From search engines to generative AI

Generative AI products such as ChatGPT have changed the way people seek and obtain information. Users may gradually switch from search engines to generative AI for information seeking. This may lead to their defection from search services. Based on the push-pull-mooring (PPM) model, this research examined user switch intention from search engines to generative AI. The results indicated that low information-task fit and information overload lead to user dissatisfaction, whereas AI generated content (AIGC) information quality and perceived interactivity affect perceived value. Dissatisfaction, perceived value, and social influence determine user switch intention. The fsQCA identified three configurations leading to switch intention. The results suggest that search service providers need to enhance information-task fit and reduce information overload in order to retain users and achieve a sustainable development.