Relevant bibliographies by topics / AI adoption

Academic literature on the topic 'AI adoption'

Author: Grafiati
Published: 10 December 2022
Last updated: 28 January 2023

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Journal articles on the topic "AI adoption"

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El-Deeb, Ahmed. "AI Adoption." ACM SIGSOFT Software Engineering Notes 47, no. 4 (September 27, 2022): 16–17. http://dx.doi.org/10.1145/3561846.3561851.

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While Artificial Intelligence (AI) has been an industry buzzword the past 15+ years, AI as a subject is not something new. The term AI has been coined by John McCarthy in 1956 and Neural Networks has been a popular subject well in the 1980s. It's just that AI has undergone a long journey of invention and entrepreneurial phase; and seem to still not fully over it. The question now why the industry is not crossing the chasm to the mass production phase? Why most companies are not relying on AI product to reduce their churn and increase their efficiency? In this paper, I will survey the major factors that play critical role in the slow AI adoption across the software industry.
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Pillai, Rajasshrie, and Brijesh Sivathanu. "Adoption of artificial intelligence (AI) for talent acquisition in IT/ITeS organizations." Benchmarking: An International Journal 27, no. 9 (August 14, 2020): 2599–629. http://dx.doi.org/10.1108/bij-04-2020-0186.

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PurposeHuman resource managers are adopting AI technology for conducting various tasks of human resource management, starting from manpower planning till employee exit. AI technology is prominently used for talent acquisition in organizations. This research investigates the adoption of AI technology for talent acquisition.Design/methodology/approachThis study employs Technology-Organization-Environment (TOE) and Task-Technology-Fit (TTF) framework and proposes a model to explore the adoption of AI technology for talent acquisition. The survey was conducted among the 562 human resource managers and talent acquisition managers with a structured questionnaire. The analysis of data was completed using PLS-SEM.FindingsThis research reveals that cost-effectiveness, relative advantage, top management support, HR readiness, competitive pressure and support from AI vendors positively affect AI technology adoption for talent acquisition. Security and privacy issues negatively influence the adoption of AI technology. It is found that task and technology characteristics influence the task technology fit of AI technology for talent acquisition. Adoption and task technology fit of AI technology influence the actual usage of AI technology for talent acquisition. It is revealed that stickiness to traditional talent acquisition methods negatively moderates the association between adoption and actual usage of AI technology for talent acquisition. The proposed model was empirically validated and revealed the predictors of adoption and actual usage of AI technology for talent acquisition.Practical implicationsThis paper provides the predictors of the adoption of AI technology for talent acquisition, which is emerging extensively in the human resource domain. It provides vital insights to the human resource managers to benchmark AI technology required for talent acquisition. Marketers can develop their marketing plan considering the factors of adoption. It would help designers to understand the factors of adoption and design the AI technology algorithms and applications for talent acquisition. It contributes to advance the literature of technology adoption by interweaving it with the human resource domain literature on talent acquisition.Originality/valueThis research uniquely validates the model for the adoption of AI technology for talent acquisition using the TOE and TTF framework. It reveals the factors influencing the adoption and actual usage of AI technology for talent acquisition.
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Yuen, Simon, and H. Wu. "Smart Logistics and Artificial Intelligence Practices in Industry 4.0 ERA." International Journal of Managing Value and Supply Chains 13, no. 1 (March 31, 2022): 1–7. http://dx.doi.org/10.5121/ijmvsc.2022.13101.

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The purpose of this paper is to analyze the factors that affect success of logistics companies adopting AI by studying the application of AI in the logistics industry. Although the application of technology and artificial intelligence has been widely studied, the factors affecting the adoption of Artificial Intelligence (AI) are still unknown in the existing literature. Therefore, the main research in this paper is to explore the influence of success factors on AI by integrating technology, organization and environment (TOE) framework. The framework is judged by case analysis of logistics companies. This study provides some suggestions on successful adoption of AI technology fortheir logistics operations.
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Kioko, Peter Mwangangi, Nancy Booker, Njoki Chege, and Paul Kimweli. "The Adoption of Artificial Intelligence in Newsrooms in Kenya: a Multi-case Study." European Scientific Journal, ESJ 18, no. 22 (July 31, 2022): 278. http://dx.doi.org/10.19044/esj.2022.v18n22p278.

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The deployment of Artificial Intelligence (AI) in newsrooms is gaining prominence worldwide, with the technology being used to enhance the processes of news gathering, packaging, and distribution. The study was guided by two research questions: what factors drive/hinder(s) the adoption of AI or lack of it in newsrooms in Kenya? Moreover, what opportunities do journalists feel are offered by adopting AI in newsrooms in Kenya? A qualitative research approach and descriptive research design were employed to investigate the adoption of AI in newsrooms in Kenya. British Broadcasting Corporation (BBC-Africa) and Radio Africa Group (RAG) media organizations were the target population. As a research strategy, a multi-case method was employed. The researchers conducted in-depth interviews with newsroom-based participants. A purposive sampling technique was used to select participants for the research. Collected data were analyzed thematically. The paper identified six factors driving the adoption of AI or lack of it: management buy-in, cost, technical skills, clarity of user case, perception, and company structure. Further, the study identified three challenges presented by adopting AI: lack of quality data, ethical concerns, and unpredictability of the technology’s impact. The study concludes that AI offers excellent opportunities for newsrooms in Kenya to explore. Still, some obstacles need to be addressed before they can benefit fully from the technology. The study projects that human and automated journalism will become closely integrated in the future and recommends that newsrooms in Kenya prepare to embrace AI by laying the foundation for its adoption. Media schools should update curricula to prepare journalists to work with emerging technologies such as AI. Further research is needed to identify the specific skill sets required for Kenyan digital journalists to embrace AI fully. Scholars should investigate how AI can shape new business models given shrinking revenues in the media.
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Freeman, Laura, Abdul Rahman, and Feras A. Batarseh. "Enabling Artificial Intelligence Adoption through Assurance." Social Sciences 10, no. 9 (August 25, 2021): 322. http://dx.doi.org/10.3390/socsci10090322.

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The wide scale adoption of Artificial Intelligence (AI) will require that AI engineers and developers can provide assurances to the user base that an algorithm will perform as intended and without failure. Assurance is the safety valve for reliable, dependable, explainable, and fair intelligent systems. AI assurance provides the necessary tools to enable AI adoption into applications, software, hardware, and complex systems. AI assurance involves quantifying capabilities and associating risks across deployments including: data quality to include inherent biases, algorithm performance, statistical errors, and algorithm trustworthiness and security. Data, algorithmic, and context/domain-specific factors may change over time and impact the ability of AI systems in delivering accurate outcomes. In this paper, we discuss the importance and different angles of AI assurance, and present a general framework that addresses its challenges.
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Somjai, Sudawan, Kittisak Jermsittiparsert, and Thitinan Chankoson. "Determining the initial and subsequent impact of artificial intelligence adoption on economy: a macroeconomic survey from ASEAN." Journal of Intelligent & Fuzzy Systems 39, no. 4 (October 21, 2020): 5459–74. http://dx.doi.org/10.3233/jifs-189029.

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The adoption of AI is an ongoing phenomenon in today’s economy in all the industries. The purpose of this paper is to examine the economic impact of AI adoption in the region of ASEAN. To achieve this objective, structural questionnaire was developed for the various industry experts in targeted region. A sample of 240 experts was finally obtained over a time span of 6 weeks through online structural questionnaire approach. For measuring AI adoption, twelve items, initial economic impact (seven items), and subsequent economic impact (six items) were finally added in the questionnaire. For analyses purpose, descriptive statistics, structural equation modelling, and regression analyseswereapplied, examining the both initial and subsequent economic impact of AI adoption. Findings through structural model indicates that overall both initial and subsequent impact are significantly determined by AI adoption in related industries. Additionally, in depth analyses for the individual AI items as their initial and subsequent economic impact indicate that Usage of the data for AI adoption, clear strategy for AI adoption, successful mapping for AI adoption and overall positive attitude towards AI adoption have their significant and positive influence on initial economic indicators. Whereas, as per subsequent economic impact, factors like effective usage of data for AI adoption, assessing the right skills of individuals for AI adoption and positive attitude towards AI adoption are significantly impacting on material investment, capital investment, increasing unemployment, higher economic output, higher return on capital and higher wages for the existing labor. These findings have provided an outstanding evidence in the field of AI and its economic impact in the region of ASEAN and can be considered as initial contribution in related fields. Both industry exports and macroeconomic decision makers can significantly utilize the findings to develop their conceptual framework and understanding for the integration between AI adoption and economy. Additionally, this study can work as reasonable justification for implementing the more adoption of AI in various industries as it has positive economic outcome (both initial and subsequent). However, one of the key limitations of this study is limited sample size and only 240 industry exports were targeted from selected industries in ASEAN. Future study could be reimplemented on similar topic with expanding the sample size for better findings and more generalization.
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الهادی, محمد. "The Top 7 AI Adoption Challenges." مجلة الجمعیة المصریة لنظم المعلومات وتکنولوجیا الحاسبات 24, no. 24 (April 1, 2021): 18–20. http://dx.doi.org/10.21608/jstc.2021.165200.

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Mudgal, Keshav Shree, and Neelanjan Das. "The ethical adoption of artificial intelligence in radiology." BJR|Open 2, no. 1 (November 1, 2020): 20190020. http://dx.doi.org/10.1259/bjro.20190020.

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Artificial intelligence (AI) is rapidly transforming healthcare—with radiology at the pioneering forefront. To be trustfully adopted, AI needs to be lawful, ethical and robust. This article covers the different aspects of a safe and sustainable deployment of AI in radiology during: training, integration and regulation. For training, data must be appropriately valued, and deals with AI companies must be centralized. Companies must clearly define anonymization and consent, and patients must be well-informed about their data usage. Data fed into algorithms must be made AI-ready by refining, purification, digitization and centralization. Finally, data must represent various demographics. AI needs to be safely integrated with radiologists-in-the-loop: guiding forming concepts of AI solutions and supervising training and feedback. To be well-regulated, AI systems must be approved by a health authority and agreements must be made upon liability for errors, roles of supervised and unsupervised AI and fair workforce distribution (between AI and radiologists), with a renewal of policy at regular intervals. Any errors made must have a root-cause analysis, with outcomes fedback to companies to close the loop—thus enabling a dynamic best prediction system. In the distant future, AI may act autonomously with little human supervision. Ethical training and integration can ensure a "transparent" technology that will allow insight: helping us reflect on our current understanding of imaging interpretation and fill knowledge gaps, eventually moulding radiological practice. This article proposes recommendations for ethical practise that can guide a nationalized framework to build a sustainable and transparent system.
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Nazri, Shahrizal, Mohamed Azlan Ashaari, and Hazrieffendy Bakri. "EXPLORING THE ADOPTION OF ARTIFICIAL INTELLIGENCE IN INSTITUTIONS OF HIGHER LEARNING." Journal of Information System and Technology Management 7, no. 27 (September 1, 2022): 54–62. http://dx.doi.org/10.35631/jistm.727004.

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In every sector of the economy, artificial intelligence (AI) is becoming more feasible, and higher education service is no exception. At an unparalleled pace, both within and outside the classroom, AI opens the possibility for institutions of higher learning (IHLs) to become scalable. This paper proposed a research framework for AI adoption of in IHLs. The research aims to examine the determinants that significantly affected AI adoption among IHLs. This study presents an interpretation of the Technology-Organisation-Environment (TOE) theory for the adoption of AI. The research framework derived from the TOE theory, where the context of technological, organisational, and environmental are vital for IT adoption. It discussed the development of hypotheses that consisted the determinants for the adoption of AI from the context of technological (relative advantage and compatibility), organisational (resources availability, top management support and organisation size) and environmental (government regulation and competitive pressure).
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Tahiru, Fati. "AI in Education." Journal of Cases on Information Technology 23, no. 1 (January 2021): 1–20. http://dx.doi.org/10.4018/jcit.2021010101.

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Artificial intelligence (AI) is developing and its application is spreading at an alarming rate, and AI has become part of our daily lives. As a matter of fact, AI has changed the way people learn. However, its adoption in the educational sector has been saddled with challenges and ethical issues. The purpose of this study is to analyze the opportunities, benefits, and challenges of AI in education. A review of available and relevant literature was done using the systematic review method to identify the current research focus and provide an in-depth understanding of AI technology in education for educators and future research directions. Findings showed that AI's adoption in education has advanced in the developed countries and most research became popular within the Industry 4.0 era. Other challenges, as well as recommendations, are discussed in the study.
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Dissertations / Theses on the topic "AI adoption"

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Roa, Baez Julian, and Remi Leon Igbekele. "Challenges of AI Adoption in SMEs." Thesis, KTH, Skolan för industriell teknik och management (ITM), 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-301269.

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This thesis paper discusses the adoption of Artificial Intelligence (AI) technologies in the context of small and medium-sized companies. AI is a disruptive innovation currently leading a technological transition in many industries. Academic literature on its adoption in the context of SMEs is limited. This thesis aims to answer the research question "W hat are the main challenges of AI adoption in SMEs?". The authors use a qualitative research approach, interviewing 18 representatives of different Swedish organizations to answer the question. The authors identify six main challenge categories regarding AI adoption in SMEs via a coding process, which are Change Management, Education, Data, Hiring, Project Structuring, and External Help. The authors show similarities to challenges described in the existing literature on AI adoption. However, Hiring and External Help impact AI adoption in SMEs more significantly than in larger corporations, while Change Management can be easier for smaller organizations. Furthermore, a framework that helps to prioritize the above challenges is introduced. In comparison with literature on the adoption of other (non-AI) IT innovations in SMEs, some challenges are more salient to AI: the unclear definition of the term, false expectations as a result of the AI Hype, the uncertainty of project outcome, and the prerequisite of a previous digitalization process. The authors recommend further research in other contexts.
I denna avhandling diskuteras införandet av artificiell intelligens (AI) teknik i små och medelstora företag. AI är en disruptiv innovation som för närvarande leder en teknisk övergång i många branscher. Den akademiska litteraturen kring dess användning i små och medelstora företag är begränsad. Denna avhandling syftar till att besvara forskningsfrågan "Vilka är de största utmaningarna med att införa AI i små och medelstora företag?". Författarna använder en kvalitativ forskningsansats och intervjuar 18 representanter från olika svenska organisationer för att besvara frågan. Författarna identifierar sex huvudsakliga utmaningar områden avseende AI-adoption i små och medelstora företag via en kodningsprocess: förändringshantering, utbildning, data, anställning, projekt strukturering och extern hjälp. Författarna visar på likheter med de utmaningar som beskrivs i den befintliga litteraturen om AI-användning. Anställning och extern hjälp påverkar dock AI-användningen i små och medelstora företag mer påtagligt än i större företag, medan förändringshantering kan vara lättare för mindre organisationer. Vidare introduceras ett ramverk som hjälper till att prioritera ovanstående utmaningar. I jämförelse med litteratur om antagandet av andra (icke-AI) IT-innovationer i små och medelstora företag är vissa utmaningar mer framträdande när det gäller AI: den oklara definitionen av begreppet, falska förväntningar som ett resultat av AI-hypen, osäkerheten från projektresultatet och förutsättningen av tidigare digitaliseringsprocess. Författarna rekommenderar ytterligare forskning i andra sammanhang.
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Kim, Jee Won. "How speciesism affects artificial intelligence (AI) adoption intent." Thesis, Queensland University of Technology, 2022. https://eprints.qut.edu.au/228673/1/Jee%20Won_Kim_Thesis.pdf.

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As there have been concerns about the excessive advancement of artificial intelligence (AI) surpassing humans, exploring reactions to AI as challenging human superiority is meaningful. By examining how the hierarchical and discriminative views on animals (speciesism) affects the views on non-living AI, this thesis has significant and novel contributions to AI adoption literature and AI product marketing.
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Savola, Tommi, Tyko Tuohimaa, and Sebastian Berg. "AI-Enhanced Marketing Management – Factors Influencing Adoption in SMEs." Thesis, Högskolan i Jönköping, Internationella Handelshögskolan, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:hj:diva-39908.

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Recent developments and hype around artificial intelligence (AI) have arisen as result of two main factors: increase in computational power and data. Although marketing is considered as one of the main business applications within AI today, there is a lack of literature combining the disciplines. Marketing management tools, which utilise AI in supporting decision making are referred to as knowledge-driven marketing management support systems (MMSS). These systems provide besides quantitative analysis, further qualitative facets into marketing management. Despite the willingness of many SMEs to engage with the technology that may foster competitive advantage, many adoption processes fail. The purpose of this thesis is to explore the factors influencing adoption of knowledge-driven MMSS in SMEs in Finland and Sweden. Qualitative primary data was collected from nine company representatives at top management level in Finnish and Swedish firms. Companies were classified in three categories, providers, adopters and non-adopters of knowledge-driven MMSS.   The findings show that there are several factors influencing adoption of knowledge-driven MMSS. The factors were grouped into technological, organizational and environmental factors, based on the TOE framework. Even though SMEs suffer from a lack of resources compared to large companies, this research suggests that they are at the forefront of adopting AI for marketing purposes. Additionally, it was found that the factors affecting adoption are dependent on whether the knowledge-driven MMSS is built in-house or outsourced.   This study has contributed to the identified gaps in literature by combining the disciplines of AI, marketing and SMEs, and by exploring the factors behind adoption of knowledge-driven MMSS. The authors of this thesis have the aspiration that the developed post-empirical framework will serve as a guiding tool for top management and marketing managers in SMEs looking to adopt knowledge-driven MMSS into their organizations.
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Stenberg, Louise, and Svante Nilsson. "Factors influencing readiness of adopting AI : A qualitative study of how the TOE framework applies to AI adoption in governmental authorities." Thesis, KTH, Skolan för industriell teknik och management (ITM), 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-279583.

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Artificial intelligence is increasing in interest and it is creating value to many organizations world-wide. Due to the potential, governmental authorities in Sweden who work with large volumes of text documents are interested in natural language processing models, which is a sub field of AI and have started to incorporate it to their organizations. This study explores and discusses factors that are influential for governmental authorities when adopting AI and highlights ethical aspects which are of importance for the adoption process. This is explored through a literature review which lead to a frame of reference built on the Technology Organization Environment framework (TOE), which then was tested through interviews with project leaders and AI architects at governmental authorities who are working with language models. The results show that the TOE framework is suitable for analysing AI adoption for governmental authorities. The factors that are found influential are Relative Advantage, Compatibility and Complexity, Management support, Staff capacity, Regulatory environment and Cooperation. Furthermore, the findings suggest that AI Ethics and Data access are influential in all three contexts of technology, organization and environment. The findings of this study confirm results from previous research regarding adoption of new technology, and also provides the literature with exploring the adoption process of AI in governmental authorities, which was not widely explored in literature on beforehand.
Allt fler intresserar sig för artificiell intelligens då det skapar värde för många organisationer. Svenska myndigheter som arbetar med stora mängder textdokument ser potentialen i AI och har börjat implementera språkmodeller, ett sorts AI, i sina organisationer. Den här studien utforskar och diskuterar faktorer som är inflytelserika inför implementering av AI och belyser etiska aspekter som är viktiga för implementationsprocessen. Detta har utforskats först genom en litteraturstudie, ur vilken ett ramverk som bygger på Teknologi Organisation Miljö-ramverket (TOE) har tagits fram. Detta har sedan testats genom intervjuer med projektledare och AI arkitekter på svenska myndigheter som arbetar med språkmodeller. Resultaten visar att TOE-ramverket lämpar sig väl för att analysera adoptering av AI i myndigheter. Faktorerna som har identifierats som inflytelserika är relativ fördel, kompatibilitet, komplexitet, ledningsstöd, anställdas kapacitet, regleringskontext och samarbete. Dessutom föreslås det att etik för AI och datatillgång ska spänna över alla tre kontexter inom TOE. Resultaten av studien bekräftar tidigare forskning gällande adoptering av nya teknologier, och den bidrar även till litteraturen genom att utforska adopteringsprocessen av AI i myndigheter, vilket inte har utforskats i större utsträckning tidigare.
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Tersander, Jacob. "AI – Can You Afford To Wait?" Thesis, KTH, Skolan för industriell teknik och management (ITM), 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-241051.

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The paradigm of diffusion research can be traced back all the way to the 1940s when Ryan and Gross investigated the diffusion of hybrid seed among farmers in Iowa. Since the 1960s diffusion research has been applied in a wide variety of disciplines, for instance, to study the diffusion of the Internet and the non-diffusion of the Dvorak keyboard. Currently, the technologies that are on top of the Gartner Hype Cycle are all associated with Artificial Intelligence (AI), which shortly can be defined as learning devices that perceive their environment and take actions to maximize their success at some goal. Consequently, some people suggest that the current hype surrounding AI can be the end of the human kind, while others believe it will give way for millions of fresh jobs and cleverer decision-making. In recent years both media and political organizations have shown great interest in AI. In addition, the industry is captivated by the potential uses of AI. In the last years, AI-related companies in the US have raised billions of dollars in the stock market together with a large number of acquisitions. The large flow of capital into AI technology underpins the fast development of AI solutions. The purpose of this study is to investigate how groups approach AI. What can be concluded after reviewing different sectors is that organizations seem to share a common interest of AI. Furthermore, organizations share the opinion that eventually AI will be a more natural part of their processes. Organizations investing a larger share of their budget in R&D have a longer experience of using AI and are currently doing projects utilizing more advanced technologies within AI. In organizations from other sectors, the investments in AI depend on the people with the authority to invest money in projects and their view on AI. Organizations generally seem to approach AI in a similar way. Firstly, they evaluate what AI is. Secondly, they find areas to make small iterative PoC-projects utilizing AI, usually with machine learning. Finally, more money is invested if the PoC-projects were successful and the organization starts looking at how to acquire more competence within the area to fully exploit the value of AI.
Paradigmet för innovationsspridning kan spåras ända tillbaka till 1940-talet när Ryan och Gross undersökte spridningen av hybridfrön bland bönder i Iowa. Sedan 1960-talet har forskningen tillämpats inom en mängd olika discipliner, till exempel för att studera spridningen av Internet och icke-spridningen av Dvorak-tangentbordet. För närvarande är teknologierna som ligger på toppen av Gartner Hype-cykeln alla förknippade med artificiell intelligens (AI), som kan definieras som lärande enheter som uppfattar sin miljö och vidtar åtgärder för att maximera sin framgång gällande något mål. Hypen som nu finns kring AI har lett till att vissa människor tror att det kan innebära slutet för mänskligheten medan andra tror att det kommer att ge plats för miljoner nya jobb och smartare beslutsfattande. Under de senaste åren har både medier och politiska organisationer visat stort intresse för AI samt visat intresse för potentiella användningsområden av AI. AI-relaterade företag i USA har under de senaste åren har tagit in miljarder dollar i riskkapital. Ett stort antal förvärv och kapitalflödet till AI-teknik ökar den snabba utvecklingen av AI-lösningar. Syftet med denna studie är att beskriva spridningen av AI i organisationer från ett antal olika sektorer. Vad som kan sägas efter att ha studerat olika sektorer är att organisationer delar en gemensam nyfikenhet för AI och att de tror att AI kommer bli en allt mer naturlig del av sina processer. De företag som spenderar mycket pengar på FoU har längre erfarenhet av att använda AI och gör för närvarande projekt som använder mer avancerade tekniker. I andra organisationer är investeringarna inom AI beroende av de anställda som har rätt att investera pengar i projekt och deras syn på AI. Organisationer verkar allmänt närma sig AI på ett liknande sätt där de först utvärderar vad AI är. Därefter väljer de ett antal områden där de gör små iterativa projekt där de utnyttjar AI, vanligtvis via ML. Därefter investerades mer pengar om de små projekten lyckas och företaget börjar titta på hur man kan förvärva mer kompetens inom området.
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Pajany, Peroumal. "AI Transformative Influence: Extending the TRAM to Management Student's AI’s Machine Learning Adoption." Franklin University / OhioLINK, 2021. http://rave.ohiolink.edu/etdc/view?acc_num=frank1623093426530669.

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Rabe, Erik, and Zacharias Sundlöf. "Bidragande faktorer till attityder gentemot implementering av AI-styrda fordon." Thesis, Uppsala universitet, Institutionen för informatik och media, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-417547.

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Artificiell intelligens är en form av teknik som blir vanligare inom samhället. I takt med att tekniken utvecklas blir även diskussionen inom området mer utvecklad vilket resulterat i att eventuella problem och möjligheter blivit mer tillgänglig information. Det finns en avsaknad av tankar och förväntningar från privatpersoners synvinkel inom ämnet vilket kan ses som negativt då de förväntas vara en majoritet av användarbasen för tekniken. Eftersom denna typ av teknik förutspås ta över ett större ansvar av mänskliga uppgifter är det viktigt att klarlägga olika typer av tillvägagångssätt samt utvecklingsperspektiv i syfte att skapa ett hälsosamt och välfungerande AI-system till respektive områden. Studien syftar till att belysa bidragande faktorer till attityder och åsikter relaterade specifikt till AI-styrda fordon ur privatpersoners perspektiv samt hur dessa kan påverka en eventuell implementering och använder sig av en kvalitativ metod. Den data som används inom arbetet har samlats in via semistrukturerade intervjuer med personer som anmält att de vill delta i studien. Analysen genomförs baserat på innovationsspridningsteorin (IDT) och relevant tidigare forskning för att undersöka vad som påverkar användare att adoptera tekniken eller inte. Faktorer som identifierades vara påverkande för adoptionsprocessen var oro över att tekniken inte skulle fungera på ett kompatibelt sätt med mänskliga värderingar, ett krav på utförlig testning samt möjligheten till att reducera olyckor eller klimatpåverkan relaterat till trafik. Utifrån dessa faktorer härleddes förslag till implementeringsprocesser vilket bestod av expanderande statligt kontrollerad testning inom kollektivtrafiken, tydligt klarlagda strukturella regler och avgränsningar samt ett främjande av de positiva faktorer som möjliggörs av AI-styrda fordon. Detta främjande kan genomföras med en effektiv kommunikation som drar nytta av vår bristfällliga rationella beslutsprocess och använder starka känslomässiga intryck.
Artificial intelligence is a form of technology that is becoming increasingly more common within society. As the technology evolves, the discussion within the subject is also increasing which has made information about eventual problems and possibilities more public. There is a shortage of thoughts and expectations from the private individual’s point of view regarding this topic which can be a negative thing due to this group being expected to make up the majority of the technology’s user base. Because this type of technology is predicted to take on a larger responsibility of human tasks it is important to clarify different approaches and development perspectives in order to create a healthy and well-functioning AI-system within respective areas. The study intends to highlight contributing factors to attitudes and opinions specifically related to AI-controlled vehicles from the public's view as well as how these can affect an eventual implementation and is carried out with a qualitative method. The data that is used is gathered through semi-structured interviews with people that expressed interest in participating in the study. The analysis is based on the diffusion of innovations theory (IDT) and relevant earlier research in order to examine what influences users to adopt the technology or not. The factors that were identified to be affecting this process were worry that the technology would not work in a compatible way with human values, a demand for extensive testing as well as the possibility to reduce accidents or the affect on climate related to traffic. Several suggestions for implementation were derived from these factors which consisted of continuous expanded testing within public transport regulated by the state, clear structural rules and limitations as well as a promotion of the positive factors made possible by AI-controlled vehicles. This promotion can be done through effective communication which takes advantage of our flawed rational decision making and uses strong emotional impressions.
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Edberg, Emelie, and Lea Beck. "Adoption of AI in Digital Design : A qualitative study about the effects on the profession." Thesis, Jönköping University, Tekniska Högskolan, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:hj:diva-48126.

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The development of new technology plays a major role in today's society and several different industries. While some technologies have more or less an impact upon the whole working sector, one of the more recent and controversial technologies is Artificial Intelligence (AI). In recent years, this technology has evolved continuously and is spreading across several different industries. As it is clear that AI is reshaping the workplace, it is relevant to examine how and to what extent it is affecting the digital design profession. Purpose The purpose of this study is to gain insight into the current state of adoption of AI within digital design, including graphic design and web design. Furthermore, to explore the effects of AI on the nature of the profession, from the perspectives of professionals in the industry. While focusing on the creative process and the development of digital products, it investigates how the industry is experiencing the effects of AI in daily tasks and workflows. Furthermore, it examines if the implementation of AI has lead to the development of new work processes, or if traditional tasks remain but are carried out with AI tools as assistance. Method The research method is qualitative. Through literature reviews and by carrying out interviews with relevant designers currently working in the industry, the appropriate data is collected and analyzed. The interviews focus on understanding the participant’s perspective on the topic, their experiences of AI and what effect it has on their work. Through the interviews, the study identifies to what extent AI is used in creative processes, and sheds light on the general feelings towards AI, including expectations and concerns. Conclusions The findings show that the overall awareness surrounding AI is rather divided. AI is already implemented in various design processes and software, whether the designer is aware of it or not. It can thus be concluded that AI has affected the nature of the digital design profession. However, the effects vary depending on the specific role and the related tasks. Most are interested in learning more about it but natural skepticism and lack of knowledge about the technology remain an obstacle for implementing more AI in companies.
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Kerey, Ayşegül Begüm, and Enrico D`Alessandro. "AI in recruitment: an exploratory study into the factors that impact its pace of adoption. : A case study to reveal the strategic implications of these factors on AI solution providers from a contingency perspective." Thesis, Uppsala universitet, Industriell teknik, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-446406.

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Over the past few years, the adoption of AI in recruitment has accellerated. However, there has been a noticeable resistance from HR managers to invest in AI tools for their deparment. With the aim of understanding the causes prompting this resistance, this thesis investigates the factors that impact the pace of adoption of AI in HR, with a focus on recruitment solutions. While designing an analytical framework inspired by the contingency perspective, the factors have been searched through a literature review and their effects have been tested in terms of magnitude and direction through a qualitative study. To do this, the authors performed a case study involving an external partner, an AI solution provider start-up company. A total of 16 semi-structured interviews have been conducted with different levels of stakeholders, including external partner`s employees, investors, competitors, and end- users. Finally, a strategic analysis of the AI recruitment market has been deployed. Our ambition is that the combination of the information over the factors together with the strategic analysis will empower the companies within the industry in taking better informed strategic decisions.
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Regona, Massimo. "Opportunities and adoption constraints of artificial intelligence in the construction industry: A scoping study." Thesis, Queensland University of Technology, 2022. https://eprints.qut.edu.au/232514/1/Massimo_Regona_Thesis.pdf.

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This MPhil thesis provides an overview of AI technologies and the key opportunities and challenges these technologies bring to the construction industry. The first study was a literature review that identified and explored the theoretical foundations underpinning AI technologies that are currently being used in construction, specifically in the design, planning and construction stages. Secondly, the study used social media analytics to explore AI technology adoption, prospects and constraints in the Australian construction industry. Finally, a sentiment analysis established the opportunities, challenges and public perception towards AI technologies in construction.
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Books on the topic "AI adoption"

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Songqiao, Li, ed. Sheng ming zhi ai: A love for life. Beijing Shi: Wai yu jiao xue yu yan jiu chu ban she, 2002.

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Pellegrino, Piero. Gli impedimenti relativi ai vincoli etico-giuridici tra le persone nel matrimonio canonico. Torino: G. Giappichelli, 2002.

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Singh, Surabhi. Adoption and Implementation of AI in Customer Relationship Management. IGI Global, 2021.

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Singh, Surabhi, ed. Adoption and Implementation of AI in Customer Relationship Management. IGI Global, 2022. http://dx.doi.org/10.4018/978-1-7998-7959-6.

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Singh, Surabhi. Adoption and Implementation of AI in Customer Relationship Management. IGI Global, 2021.

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Singh, Surabhi. Adoption and Implementation of AI in Customer Relationship Management. IGI Global, 2021.

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Adoption and Implementation of AI in Customer Relationship Management. IGI Global, 2021.

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Singh, Surabhi. Adoption and Implementation of AI in Customer Relationship Management. IGI Global, 2021.

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Mitra, Rudradeb. Creating Value with Artificial Intelligence: Lessons Learned from 10 Yrs of Building AI Products and Overcoming Data, Adoption, and Engineering Challenges. Independently Published, 2018.

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Reber, Arthur S. The First Minds. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780190854157.001.0001.

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The book presents a novel theory of the origins of mind and consciousness dubbed the Cellular Basis of Consciousness (CBC). It argues that sentience emerged with life itself. The most primitive unicellular species of bacteria are conscious, though it is a sentience of a primitive kind. They have minds, though they are tiny and limited in scope. There is nothing even close to this thesis in the current literature on consciousness. Hints that cells might be conscious can be found in the writings of a few cell biologists, but a fully developed theory has never been put forward before. Other approaches to the origins of consciousness are examined and shown to be seriously or fatally flawed, specifically ones based on: (a) the assumption that minds are computational and can be captured by an artificial intelligence (AI), (b) efforts to discover the neurocorrelates of mental experiences, the so-called Hard Problem, and (c) looking for consciousness in less complex species by identifying those that possess precursors of those neurocorrelates. Each of these approaches is shown to be either essentially impossible (the AI models) or so burdened by philosophical and empirical difficulties that they are effectively unworkable. The CBC approach is developed using standard models of evolutionary biology. The remarkable repertoire of single-celled species that micro- and cell-biologists have discovered is reviewed. Bacteria, for example, have sophisticated sensory and perceptual systems, learn, form memories, make decisions based on information about their environment relative to internal metabolic states, communicate with one another, and even show a primitive form of altruism. All such functions are indicators of sentience. Conversations with a caterpillar function as a literary vehicle Finally, the implications of the CBC model are discussed along with a number of related issues in evolutionary biology, philosophy of mind, the possibility of sentient plants, the ethical repercussions of universal animal sentience, and the long-range impact of adopting the CBC stance.
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Book chapters on the topic "AI adoption"

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Sciberras, Marvic, and Alexiei Dingli. "AI Adoption." In Investigating AI Readiness in the Maltese Public Administration, 11–16. Cham: Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-19900-4_4.

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Kabalisa, Rene, and Jörn Altmann. "AI Technologies and Motives for AI Adoption by Countries and Firms: A Systematic Literature Review." In Economics of Grids, Clouds, Systems, and Services, 39–51. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-92916-9_4.

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AbstractIn this era of digital revolution, artificial intelligence stands to be one of the emerging technologies to revolutionize the way we live, work, or communicate. While everyone is fighting to lead in this technology, their readiness differs and adoption challenges arise in many sectors. These competitions also result in various economic impacts on countries, firms, and individuals. This paper uses a systematic literature review to analyze the existing economic impact of AI adoption and the technology used. Overall, this paper presents clear evidence that AI adoption has a large effect on an economy. Findings of this research help researchers and practitioners to identify important economic impacts of adopting AI, identify directions for future research, and set policies that need to be put in place.
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Akiyama, Nobumasa. "AI Nuclear Winter or AI That Saves Humanity? AI and Nuclear Deterrence." In Robotics, AI, and Humanity, 161–70. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-54173-6_13.

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AbstractNuclear deterrence is an integral aspect of the current security architecture and the question has arisen whether adoption of AI will enhance the stability of this architecture or weaken it. The stakes are very high. Stable deterrence depends on a complex web of risk perceptions. All sorts of distortions and errors are possible, especially in moments of crisis. AI might contribute toward reinforcing the rationality of decision-making under these conditions (easily affected by the emotional disturbances and fallacious inferences to which human beings are prone), thereby preventing an accidental launch or unintended escalation. Conversely, judgments about what does or does not suit the “national interest” are not well suited to AI (at least in its current state of development). A purely logical reasoning process based on the wrong values could have disastrous consequences, which would clearly be the case if an AI-based machine were allowed to make the launch decision (this virtually all experts would emphatically exclude), but grave problems could similarly arise if a human actor relied too heavily on AI input.
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Okolo, Chinasa T., Kehinde Aruleba, and George Obaido. "Responsible AI in Africa—Challenges and Opportunities." In Social and Cultural Studies of Robots and AI, 35–64. Cham: Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-08215-3_3.

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AbstractThis chapter provides an analysis into the factors that impact the effective adoption and successful implementation of artificial intelligence (AI) technologies in Africa. The study begins by defining the concept of “responsible AI” and what this means specifically for technologies developed and used within Africa. Further sections within the chapter present challenges including digital literacy, lack of local AI talent and governmental barriers that impede. The chapter also goes into an in-depth analysis of the AI startup and research landscape within the African continent, highlighting organisations of interest and concerning trends. ‘Challenges to effective AI adoption and Implementation in Africa’ concludes by envisioning what responsible AI could look like in the African context and provides actionable recommendations for making strides towards this goal.
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Ade-Ibijola, Abejide, and Chinedu Okonkwo. "Artificial Intelligence in Africa: Emerging Challenges." In Social and Cultural Studies of Robots and AI, 101–17. Cham: Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-08215-3_5.

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AbstractIn the current African society, Artificial Intelligence (AI) is becoming more popular and seeking to cover all facets of human activity. The adoption and use of these modern technologies in the African context are currently low due to some emerging challenges. Consequently, these difficulties may have a direct influence on African economic development. In this paper, we highlight the challenges facing the adoption of AI technologies in Africa which include skills acquisition, lack of structured data ecosystem, ethics, government policies, insufficient infrastructure and network connectivity, uncertainty, and user attitude. Finally, various solutions to enhance AI adoption in Africa were then proposed.
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Ramesh, Sattu, and Simanchala Das. "Adoption of AI in Talent Acquisition: A Conceptual Framework." In Digital Technologies and Applications, 12–20. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-01942-5_2.

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Ruttkamp-Bloem, Emma. "Epistemic Just and Dynamic AI Ethics in Africa." In Social and Cultural Studies of Robots and AI, 13–34. Cham: Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-08215-3_2.

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AbstractThis chapter considers the potential for actualising the ideal for responsible AI on the African continent, focusing on the AI ethics policy environment in Africa. I consider the impact of context and culture on successful adoption of AI technologies in general and on trust in AI technology and openness to AI regulation in particular. It concludes that actionable AI ethics in Africa should be driven by dynamic and epistemic just ethical systems.
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Zhu, Yiwei, and Shiwei Sun. "Exploring Patients’ AI Adoption Intention in the Context of Healthcare." In Communications in Computer and Information Science, 27–39. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-3631-8_4.

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Smit, Danie, Sunet Eybers, and Jarod Smith. "A Data Analytics Organisation’s Perspective on Trust and AI Adoption." In Artificial Intelligence Research, 47–60. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-95070-5_4.

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Falcone, Rino, and Cristiano Castelfranchi. "Levels of Delegation and Levels of Adoption as the basis for Adjustable Autonomy." In AI*IA 99: Advances in Artificial Intelligence, 273–84. Berlin, Heidelberg: Springer Berlin Heidelberg, 2000. http://dx.doi.org/10.1007/3-540-46238-4_24.

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Conference papers on the topic "AI adoption"

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Ben David, Daniel, Yehezkel S. Resheff, and Talia Tron. "Explainable AI and Adoption of Financial Algorithmic Advisors." In AIES '21: AAAI/ACM Conference on AI, Ethics, and Society. New York, NY, USA: ACM, 2021. http://dx.doi.org/10.1145/3461702.3462565.

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Leroux, Sam, Pieter Simoens, Meelis Lootus, Kartik Thakore, and Akshay Sharma. "TinyMLOps: Operational Challenges for Widespread Edge AI Adoption." In 2022 IEEE International Parallel and Distributed Processing Symposium Workshops (IPDPSW). IEEE, 2022. http://dx.doi.org/10.1109/ipdpsw55747.2022.00160.

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Yazdani, Ghulam, Farheen Qazi, and Sadiq Ali Khan. "Adoption of VR influencing AI on 3D objects." In 2019 International Conference on Information Science and Communication Technology (ICISCT). IEEE, 2019. http://dx.doi.org/10.1109/cisct.2019.8777408.

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"AI ADOPTION IN HEALTHCARE: TRUST AND PRIVACY CONCERNS." In Applied Computing 2022 and WWW/Internet 2022. IADIS Press, 2022. http://dx.doi.org/10.33965/ac_icwi2022_202208r038.

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Alzaabi, Maryam, and Ahmed Shuhaiber. "The Role of The AI Availability and Perceived Risks on AI Adoption and Organizational Values." In Intelligent Human Systems Integration (IHSI 2022) Integrating People and Intelligent Systems. AHFE International, 2022. http://dx.doi.org/10.54941/ahfe1001043.

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Artificial intelligence (AI) is the simulation of human intelligence in various machines which are programmed to think like humans. AI could have many forms, such as Natural language Processing (NLP), expert systems, unsupervised learning, content extraction, question answering, image recognition, machine vision, speech to text and machine translation, and Robotics. Despite the potential of AI technologies, little is known about their adoption by organizations worldwide, and especially in the Arab World and the Middle East. Thus, this research intends to investigate the relationships and the impact of the availability of AI technologies and the associated perceived risks on AI adoption in organizations and its perceived organizational values, and also considering two mediating variables: AI replacing humans and AI creating jobs, in one Arab and Gulf country, the UAE. By reviewing the literature and describing the AI applications and services available in UAE, a research model was proposed and tested quantitatively on a sample of 150 employees who are familiar with organizational AI technologies. The results revealed that the availability of AI technologies significantly impacts organizations’ decisions to adopt those technologies in the UAE. In addition, the more the AI applications are available, the more the AI will be replacing humans in job positions and opportunities. On the other hand, perceived security, privacy, and technical risks can significantly influence organizations’ intentions to adopt AI technologies, and can, surprisingly, contribute to creating new job opportunities which serve the human side positively. The research concludes with some implications, limitations, and future directions.
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Jahic, Jasmin, Robin Roitsch, and Lukasz Grzymkowski. "Knowledge-based Adequacy assessment Approach to support AI adoption." In 2021 IEEE 18th International Conference on Software Architecture Companion (ICSA-C). IEEE, 2021. http://dx.doi.org/10.1109/icsa-c52384.2021.00008.

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WANG, Shih-Shuan, Hong-fu CHOU, and Mircea BOȘCOIANU. "AI SMEs IN INDUSTRIAL MANAGEMENT." In SCIENTIFIC RESEARCH AND EDUCATION IN THE AIR FORCE. Publishing House of "Henri Coanda" Air Force Academy, 2022. http://dx.doi.org/10.19062/2247-3173.2022.23.16.

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SMEs form the pile in the Romanian overall economy, creating a huge sum of the job and added benefit within the nation, which makes them important in this context. IoT and cloud are processing Romanian but the difficulties encountered during the adoption of those systems by Romanian SMEs. Nevertheless, current literature will not heavily concentrate on SMEs and their particular challenges nor will it include a lot of situation studies focusing upon maturity amounts of impaired computing and IoT technologies. The outcome of this research seeks to contribute to the field of IoT and maturation models by adding more research that is specific to SMEs in Romania. The particular insights created by the conclusions of this thesis goal to help SMEs and researchers in assessing maturity levels and dealing with the challenges connected to the adoption of either IoT or cloud computing technologies.
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Eluwole, Opeoluwa Tosin, Segun Akande, and Oluwole Abiodun Adegbola. "Major threats to the continued adoption of Artificial Intelligence in today's hyperconnected world." In 2022 IEEE World AI IoT Congress (AIIoT). IEEE, 2022. http://dx.doi.org/10.1109/aiiot54504.2022.9817247.

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Kwon, Ohbyung, Sujin Bae, and Bongsik Shin. "Understanding the Adoption Intention of AI through the Ethics Lens." In Hawaii International Conference on System Sciences. Hawaii International Conference on System Sciences, 2020. http://dx.doi.org/10.24251/hicss.2020.611.

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Ulrich, Patrick, Vanessa Frank, and Mona Kratt. "Adoption of artificial intelligence technologies in German SMEs — Results from an empirical study." In Corporate governance: A search for emerging trends in the pandemic times. Virtus Interpress, 2021. http://dx.doi.org/10.22495/cgsetpt13.

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Artificial intelligence (AI) is globally regarded as one of the most important technologies of the future. Germany is not considered a pioneer in the field of AI in the international context, and the implementation of AI technologies is rather sluggish. As the German economy is mainly driven by small and medium-sized enterprises (SMEs), the implementation of AI in SMEs is the main success factor. This study discusses the implementation perspectives of AI in German SMEs based on an empirical study from the year 2020 among 283 companies
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Reports on the topic "AI adoption"

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Agrawal, Ajay, Joshua Gans, and Avi Goldfarb. AI Adoption and System-Wide Change. Cambridge, MA: National Bureau of Economic Research, May 2021. http://dx.doi.org/10.3386/w28811.

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Gans, Joshua. AI Adoption in a Monopoly Market. Cambridge, MA: National Bureau of Economic Research, April 2022. http://dx.doi.org/10.3386/w29995.

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Gans, Joshua. AI Adoption in a Competitive Market. Cambridge, MA: National Bureau of Economic Research, April 2022. http://dx.doi.org/10.3386/w29996.

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Stopford, Nikki, and Jacqueline O’Reilly. Innovation Work Chains in US Retail: Automation, Tracking and AI Adoption during the COVID-19 pandemic. Digital Futures at Work Research Centre, March 2022. http://dx.doi.org/10.20919/ivrp6984.

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The 2020 global pandemic led to record grocery sales and significantly accelerated the adoption of online retail services. This trend is expected to grow as mainstream retailers aim to keep up with the speed of delivery from ‘digitally native’ competitors and changing consumer expectations. Technological innovation is being introduced to different parts of the retail supply chain leading to a changing landscape for jobs. Here we develop the concept of Innovation Work Chains (IWC). We use this framing to discuss how the introduction of different types of innovative technology are likely to impact on employment practices across the supply chain in large-scale grocery retail. This research draws on sector reports and extensive interviews with Walmart US and one of their technology partner organisations in the USA. The focus is on how automation technologies like robots, tracking technologies and AI have become pivotal to the efficient management of retail supply chains. The evidence suggests that an iterative process of adoption and adaption is required to develop company specific solutions. However, legacy systems can pose a challenge to the speed at which automation technologies can be efficiently integrated. The concept of Innovation Work Changes highlights the differential impact on the employment landscape across the retail eco-system
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Mehmood, Hamid, Surya Karthik Mukkavilli, Ingmar Weber, Atsushi Koshio, Chinaporn Meechaiya, Thanapon Piman, Kenneth Mubea, Cecilia Tortajada, Kimberly Mahadeo, and Danielle Liao. Strategic Foresight to Applications of Artificial Intelligence to Achieve Water-related Sustainable Development Goals. United Nations University Institute for Water, Environment and Health, April 2020. http://dx.doi.org/10.53328/lotc2968.

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The report recommends that: 1) Policymakers should conduct holistic assessments of social, economic, and cultural factors before AI adoption in the water sector, as prospective applications of AI are case- specific. It is also important to conduct baseline studies to measure the implementation capacity, return on investment, and impact of intervention. 2) To ensure positive development outcomes, policies regarding the use of AI for water-related challenges should be coupled with capacity and infrastructure development policies. Capacity development policies need to address the AI and Information and Communications Technology (ICT) needs for the AI-related skill development of all water-related stakeholders. Infrastructure development policies should address the underlying requirements of computation, energy, data generation, and storage. The sequencing of these policies is critical. 3) To mitigate the predicted job displacement that will accompany AI-led innovation in the water sector, policies should direct investments towards enabling a skilled workforce by developing water sector-related education at all levels. This skilled workforce should be strategically placed to offset dependency on the private sector. 4) Water-related challenges are cross-cutting running from grassroots to the global level and require an understanding of the water ecosystem. It is important for countries connected by major rivers and watersheds to collaborate in developing policies that advance the use of AI to address common water-related challenges. 5) A council or agency with representation from all stakeholders should be constituted at the national level, to allow for the successful adoption of AI by water agencies. This council or agency should be tasked with the development of policies, guidelines, and codes of conduct for the adoption of AI in the water-sector. These key policy recommendations can be used as primary guidelines for the development of strategies and plans to use AI to help achieve water-related SDGs.
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Chen, Maggie, and Christian Volpe Martincus. Digital Technologies and Globalization: A Survey of Research and Policy Applications. Inter-American Development Bank, March 2022. http://dx.doi.org/10.18235/0004117.

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In recent years, the world has witnessed the rise of multiple specific digital technologies, including online trade platforms, robotics, artificial intelligence (AI), 3D printing, cloud computing, blockchain, and financial technology (fintech). These digital technologies are fundamentally transforming the ways that firms and individualsas both workers and consumerscommunicate, search, trade, and invest. They are also substantially changing how governments design and implement trade and investment policies and programs and, in so doing, how they interact with firms, individuals, and each other. This paper reviews the growing empirical literature on the trade, investment, and broader development effects of the adoption of specific digital technologies. It also describes the policy applications of these technologies and discusses the incipient empirical literature on the impacts thereof. Based on this review, it identifies several open questions and avenues of future research that may be useful for deepening our understanding of digital technologies and their policy implications.
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Stanley-Lockman, Zoe. Responsible and Ethical Military AI. Center for Security and Emerging Technology, August 2021. http://dx.doi.org/10.51593/20200091.

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Allies of the United States have begun to develop their own policy approaches to responsible military use of artificial intelligence. This issue brief looks at key allies with articulated, emerging, and nascent views on how to manage ethical risk in adopting military AI. The report compares their convergences and divergences, offering pathways for the United States, its allies, and multilateral institutions to develop common approaches to responsible AI implementation. Download Full Report
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Fedasiuk, Ryan, Jennifer Melot, and Ben Murphy. Harnessed Lightning: How the Chinese Military is Adopting Artificial Intelligence. Center for Security and Emerging Technology, October 2021. http://dx.doi.org/10.51593/20200089.

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This report examines nearly 350 artificial intelligence-related equipment contracts awarded by the People’s Liberation Army and state-owned defense enterprises in 2020 to assess how the Chinese military is adopting AI. The report identifies China’s key AI defense industry suppliers, highlights gaps in U.S. export control policies, and contextualizes the PLA’s AI investments within China’s broader strategy to compete militarily with the United States.
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Borrett, Veronica, Melissa Hanham, Gunnar Jeremias, Jonathan Forman, James Revill, John Borrie, Crister Åstot, et al. Science and Technology for WMD Compliance Monitoring and Investigations. The United Nations Institute for Disarmament Research, December 2020. http://dx.doi.org/10.37559/wmd/20/wmdce11.

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Abstract:
The integration of novel technologies for monitoring and investigating compliance can enhance the effectiveness of regimes related to weapons of mass destruction (WMD). This report looks at the potential role of four novel approaches based on recent technological advances – remote sensing tools; open-source satellite data; open-source trade data; and artificial intelligence (AI) – in monitoring and investigating compliance with WMD treaties. The report consists of short essays from leading experts that introduce particular technologies, discuss their applications in WMD regimes, and consider some of the wider economic and political requirements for their adoption. The growing number of space-based sensors is raising confidence in what open-source satellite systems can observe and record. These systems are being combined with local knowledge and technical expertise through social media platforms, resulting in dramatically improved coverage of the Earth’s surface. These open-source tools can complement and augment existing treaty verification and monitoring capabilities in the nuclear regime. Remote sensing tools, such as uncrewed vehicles, can assist investigators by enabling the remote collection of data and chemical samples. In turn, this data can provide valuable indicators, which, in combination with other data, can inform assessments of compliance with the chemical weapons regime. In addition, remote sensing tools can provide inspectors with real time two- or three-dimensional images of a site prior to entry or at the point of inspection. This can facilitate on-site investigations. In the past, trade data has proven valuable in informing assessments of non-compliance with the biological weapons regime. Today, it is possible to analyse trade data through online, public databases. In combination with other methods, open-source trade data could be used to detect anomalies in the biological weapons regime. AI and the digitization of data create new ways to enhance confidence in compliance with WMD regimes. In the context of the chemical weapons regime, the digitization of the chemical industry as part of a wider shift to Industry 4.0 presents possibilities for streamlining declarations under the Chemical Weapons Convention (CWC) and for facilitating CWC regulatory requirements.
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10

Probasco, Emelia. A Common Language for Responsible AI: Evolving and Defining DOD Terms for Implementation. Center for Security and Emerging Technology, October 2022. http://dx.doi.org/10.51593/20220028.

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Policymakers, engineers, program managers and operators need the bedrock of a common set of terms to instantiate responsible AI for the Department of Defense. Rather than create a DOD-specific set of terms, this paper argues that the DOD could benefit by adopting the key characteristics defined by the National Institute of Standards and Technology in its draft AI Risk Management Framework with only two exceptions.
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