Artificial Intelligence In Society Research Articles

Artificial Intelligence: Communication, Technology, and Society (a Systematic Literature Review)

The development of artificial intelligence (AI) technology has transformed the way various social sectors operate, including healthcare, communications, and decision-making. The integration of AI into these domains not only opens up new opportunities for efficiency and innovation, but also presents significant challenges. Among these challenges, the emergence of algorithmic bias, inadequate data protection, technical limitations, and complex ethical dilemmas are of major concern. These issues affect the reliability and fairness of the use of AI technologies, potentially harming individuals and society as a whole. This study aims to investigate the barriers associated with the implementation of AI in society and provide theoretical and methodological solutions to address these challenges. This study uses a systematic literature review methodology to review 40 journals, with the aim of identifying key issues and suggesting solutions for the ethical and successful integration of AI. The findings show that the potential of AI is often hampered by biased algorithms, inadequate privacy laws, and a lack of transparency in decision-making processes. The study recommends the use of qualitative and quantitative methodologies, including experimental research and case studies, to investigate practical applications of AI. In addition, a multidisciplinary approach is suggested to address the technical and social dimensions of AI applications.

An Early Snapshot of Attitudes Toward Generative Artificial Intelligence in Physical Therapy Education

Introduction. Generative artificial intelligence (AI) is rapidly gaining popularity across health care, education, and society. The purpose of this study was to assess perceptions and use of generative AI in academic physical therapy (PT). Review of Literature. Generative AI became one of the fastest-growing technologies ever after the public release of ChatGPT in November 2022. Early data indicate that attitudes toward generative AI in higher education are mixed and rapidly evolving, with significant ethical concerns around use and potential misuse. There are no published studies investigating perceptions and use of generative AI in PT education. Subjects. A total of 175 surveys were completed and analyzed. Respondents included PT educators, administrators, and students. Methods. An anonymous, online survey on use and perception of AI was distributed through email and social media. Descriptive statistics and cross-tabulations were performed to analyze respondent characteristics and responses to survey questions. Results. Most respondents (61.1%) reported they did not use generative AI during the 2022-2023 academic year, whereas 35.4% were generative AI users. More than 40% of respondents were optimistic or very optimistic toward generative AI. Users of AI were more likely to report an optimistic or very optimistic disposition toward AI compared with nonusers. AI users were more likely to agree or completely agree that generative AI has more benefits than drawbacks compared with nonusers. Discussion and Conclusion. Results of this survey suggest that, despite the rapid uptake of generative AI in society, many PT educators and students harbor reservations and uncertainties toward its use. Artificial intelligence users were less likely to hold negative perceptions toward it and were more likely to find it useful. Understanding use and perceptions of generative AI in PT education may inform strategies to promote innovation, policy-making, and ethical integration of this new and rapidly evolving technology.

Prospects of Computational Intelligence in Society: Human-Centric Solutions, Challenges, and Research Areas

The future of society could be shaped by artificial intelligence (AI), which is a part of soft computing that deals with probabilistic reasoning, neural networks, fuzzy logic, and evolutionary algorithms. This paper focuses on issues that are faced by soft computing as it moves towards human-centric solutions, outlines the challenges for soft computing, and provides possible research directions for development. Currently, there is an increasing emphasis on human-centered solutions in various fields, such as the healthcare industry, transportation sector, financial institutions, and education systems in the world today. Using soft computing techniques in this way comes with its own advantages, especially when managing unclearness or ambiguity or unsureness existing within humans' decision-making processes. Soft computing based on using neural networks may help to develop computer systems capable of faking human behavior and preferences as they evolve over time by using fuzzy logic and evolutionary algorithms. This human-centric approach helps improve the user experience while creating more symbiotic ties between technology and society, leading to examples such as personalized medicine treatment plans, among others. Soft computing’s future in society will be, to a large extent, determined by the provision of human-friendly AI while addressing the ethical, legal, and social challenges of its applications. Rights on AI represent humanity and shall be accountable. Collaborating with interdisciplinary scientists and carrying out frontier studies on developing human-friendly AI systems from ethical, legal, and social perspectives and welfare has the potential to have an influence on community and policy-maker decisions, facilitate a suitable climate for soft computing and AI in society, leverage it for empowering individuals, communities, and societies, and to improve the quality of human life while respecting human values and dignity. It also paves the way for society to be more inclusive and equitable.

Ersatz Intelligence

While the uses of generative artificial intelligence (AI) are vast, one capability it lacks is true creativity. This requires originality and surprise, neither of which is possible without the human process. Yet students and others increasingly rely on AI, so it seems likely to shape public discourse as well as everyday life. Social-media groupthink and insular academic communities have already narrowed the range of intellectual discourse. AI stands to exacerbate the trend, filtering out unconventional prose and content in languages and dialects that are not widely spoken. Yet AI companies have limited motivation to enlighten consumers about its shortcomings. As with other sweeping technological innovations, complacency and overreliance on AI appears inevitable absent intervention. Without proper guidelines, AI could distil writing and idea formulation into a few predicted outcomes and extinguish true creative thought.

Language Model Method for Collocation Rules of Parts of Speech in Machine Translation System

With the development of the times, modern society has now entered the Internet of Things (IoT) information age and Machine Translation (MT) plays an important role in increasingly frequent cross-language communication. In recent years, China's artificial intelligence industry has been in a stage of rapid construction, and the scale of its core industries has grown explosively, and a large number of artificial intelligence companies, including issuers, have emerged. Part of speech has always been a major problem in MT. One of the reasons is that there are a large number of multi-category words in Chinese and a large number of polysemy words in English, so part of speech collocation problems account for a large proportion of MT errors, which to some extent affects the credibility and accuracy of the translation. To reduce the error problem in MT of part of speech collocation, this paper used Machine Learning (ML) methods to study the Language Model (LM) of part of speech collocation based on recurrent neural network (NN) and compared it with the traditional statistical LM. In terms of the accuracy rate of the two LMS in the automatic evaluation index of machine translation, the experimental results show that the recursive NN LM established by the ML method had an accuracy rate of 80.42% and 83.57%, respectively, for the part-of-speech matching rules of the IoT machine translation system in the dialogue between Chinese and English and the translation of articles. The accuracy of traditional statistical LM evaluation was 71.29% and 69.52%, respectively. Compared to traditional statistical LM, the accuracy of translation was higher. This showed that the recurrent NN LM reduced the number of errors in the collocation of parts of speech in MT and improved the accuracy and credibility of MT.

Greening Automation: Policy Recommendations for Sustainable Development in AI-Driven Industries

This study delves into the dynamic relationship between artificial intelligence (AI) and environmental performance, with a specific focus on greenhouse gas (GHG) emissions across European countries from 2012 to 2022. Utilizing data on industrial robots, AI companies, and AI investments, we examine how AI adoption influences GHG emissions. Preliminary analyses, including ordinary least squares (OLS) regression and diagnostic assessments, were conducted to ensure data adequacy and model readiness. Subsequently, the Elastic Net (ENET) regression model was employed to mitigate overfitting issues and enhance model robustness. Our findings reveal intriguing trends, such as a downward trajectory in GHG emissions correlating with increased AI investment levels and industrial robot deployment. Graphical representations further elucidate the evolution of coefficients and cross-validation errors, providing valuable insights into the relationship between AI and environmental sustainability. These findings offer policymakers actionable insights for leveraging AI technologies to foster sustainable development strategies.

Attitudes towards and expectations on the role of artificial intelligence in the classroom among digitally skilled Finnish K-12 mathematics teachers

The growing impact and importance of artificial intelligence in society has led to an increasing interest for the potential of artificial intelligence as an educational tool in schools to aid both students and teachers. In this study we investigate digitally skilled K-12 mathematics teachers’ (N=85) attitudes towards and expectations on the role of artificial intelligence in the classroom. The study was done by conducting and analyzing the results of a web-based survey among Swedish and Finnish speaking mathematics teachers using a mixed methods strategy. The Will, Skill and Tool framework was used for the analysis. The survey was done before the introduction of ChatGPT-3. The results indicate that the teachers’ attitudes toward AI tools in school are characterized by interest, openness, and awareness. Teachers have a balanced view on the possibilities and risks of AI use in school. However, the teachers also stress that there is a risk that AI tools will shift the focus from learning key mathematical skills towards learning and interaction with the AI tools themselves. The research concluded that the K-12 mathematics teachers surveyed have broad experience with digital tools and will likely become early adopters of AI tools in the classroom.

Demystifying Explainable AI: Understanding, Transparency, and Trust

Artificial intelligence (AI) has emerged as a transformative technology with vast potential to revolutionize industries and societies. However, the responsible development, deployment, and governance of AI technologies require addressing complex ethical, regulatory, and societal challenges. This research paper aims to demystify Explainable AI (XAI) and explore its implications for understanding, transparency, and trust in AI systems. Through a comprehensive review of the literature, we examine key concepts, methodologies, and applications of XAI, as well as ethical considerations, regulatory frameworks, international cooperation, and societal impacts of AI. The paper highlights the importance of transparency, fairness, and accountability in AI governance and emphasizes the need for interdisciplinary collaboration and stakeholder engagement to ensure the responsible and ethical development of AI technologies. By fostering a deeper understanding of XAI and its implications, this paper contributes to the ongoing dialogue on the ethical and responsible use of AI in society.

How to design an AI ethics board

AbstractThe development and deployment of artificial intelligence (AI) systems poses significant risks to society. To reduce these risks to an acceptable level, AI companies need an effective risk management process and sound risk governance. In this paper, we explore a particular way in which AI companies can improve their risk governance: by setting up an AI ethics board. We identify five key design choices: (1) What responsibilities should the board have? (2) What should its legal structure be? (3) Who should sit on the board? (4) How should it make decisions? (5) And what resources does it need? We break each of these questions down into more specific sub-questions, list options, and discuss how different design choices affect the board’s ability to reduce societal risks from AI. Several failures have shown that designing an AI ethics board can be challenging. This paper provides a toolbox that can help AI companies to overcome these challenges.

'Humans think outside the pixels' - Radiologists' perceptions of using artificial intelligence for breast cancer detection in mammography screening in a clinical setting.

This study aimed to explore radiologists' views on using an artificial intelligence (AI) tool named ScreenTrustCAD with Philips equipment) as a diagnostic decision support tool in mammography screening during a clinical trial at Capio Sankt Göran Hospital, Sweden. We conducted semi-structured interviews with seven breast imaging radiologists, evaluated using inductive thematic content analysis. We identified three main thematic categories: AI in society, reflecting views on AI's contribution to the healthcare system; AI-human interactions, addressing the radiologists' self-perceptions when using the AI and its potential challenges to their profession; and AI as a tool among others. The radiologists were generally positive towards AI, and they felt comfortable handling its sometimes-ambiguous outputs and erroneous evaluations. While they did not feel that it would undermine their profession, they preferred using it as a complementary reader rather than an independent one. The results suggested that breast radiology could become a launch pad for AI in healthcare. We recommend that this exploratory work on subjective perceptions be complemented by quantitative assessments to generalize the findings.

AI in management control: Emergent forms, practices, and infrastructures

This paper discusses the significance of Artificial Intelligence (AI), particularly Machine Learning (ML) and Large Language Processing (LLP), in the context of management control. The key concern is the epistemological shift from traditional deductive approaches to an inductive approach brought about by AI technologies. The paper elaborates on shifts related to the forms, practices, and infrastructures of management control, discussing new avenues for research in social studies of accounting. The discussion outlines how the integration of AI in accounting not only changes accounting practice but also fuels the relevance of some of the prior insights about social aspects of calculative practices. On a final note, the paper also suggests that, given the speed and scope at which new calculative technology is being introduced in virtually all parts of society, accounting scholars may draw upon prior insights and contribute to wider debates about the social impact of AI in society.

UPOTREBA VEŠTAČKE INTELIGENCIJE U RAČUNOVODSTVU – ETIČKI ASPEKTI

Artificial intelligence (AI) represents a broad term for intelligent technologies capable of performing tasks that typically require human intelligence. In the accounting sector, it is already being applied in various applications, such as process automation, data analysis, and decision-making. It is anticipated that the use of artificial intelligence in this sector will continue to expand in the future, as technology progresses and becomes increasingly accessible. With the increased adoption of artificial intelligence in society, including the accounting sector, various ethical issues and challenges arise. These issues may relate to data privacy, fairness of algorithms, transparency in decision-making, responsibility for the consequences of artificial intelligence implementation, and similar concerns. These dilemmas are particularly relevant in the context of accounting, where accuracy, reliability, and adherence to rules play a crucial role. Two key ethical questions have been identified in relation to the application of artificial intelligence in accounting: integrity and responsibility. Considering these aspects is crucial for the development of ethical guidelines and practices that will ensure responsible and transparent use of artificial intelligence in accounting.

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

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

A Posthumanist Study of Stigmatization of AI in Sawyer's Mindscan and Dick's Do Androids Dream of Electric Sheep?

This study examines the ethical and sociological implications of artificial intelligence (AI) and its Stigmatization in Contemporary American Science Fiction (CASF), with a focus on AI dominance and societal marginalization. It employs a methodological approach that combines Goffman's (1986) theory of Stigmatization with Link and Phelan's (2001) framework of Stigmatization, utilizing textual analysis to explore CASF's portrayal of AI. The findings reveal that CASF largely casts AI in a negative light, depicting it as a threat to humanity rather than a potential ally. This portrayal reinforces societal biases against AI, which could lead to its Stigmatization and marginalization. CASF also often depicts the victims of AI stigmatization as marginalized groups, such as the poor, the disabled, and the elderly. The study argues that CASF plays an important role in shaping public perceptions of AI. Its largely negative portrayal of AI could have harmful consequences, such as Stigmatization and marginalization, which highlights the need for a more nuanced and critical understanding of AI in society.

Is generative artificial intelligence the next step toward a personalized hemodialysis?

Artificial intelligence (AI) generative models driven by the integration of AI and natural language processing technologies, such as OpenAI's chatbot generative pre-trained transformer large language model (LLM), are receiving much public attention and have the potential to transform personalized medicine. Dialysis patients are highly dependent on technology and their treatment generates a challenging large volume of data that has to be analyzed for knowledge extraction. We argue that, by integrating the data acquired from hemodialysis treatments with the powerful conversational capabilities of LLMs, nephrologists could personalize treatments adapted to patients' lifestyles and preferences. We also argue that this new conversational AI integrated with a personalized patient-computer interface will enhance patients' engagement and self-care by providing them with a more personalized experience. However, generative AI models require continuous and accurate updates of data, and expert supervision and must address potential biases and limitations. Dialysis patients can also benefit from other new emerging technologies such as Digital Twins with which patients' care can also be addressed from a personalized medicine perspective. In this paper, we will revise LLMs potential strengths in terms of their contribution to personalized medicine, and, in particular, their potential impact, and limitations in nephrology. Nephrologists' collaboration with AI academia and companies, to develop algorithms and models that are more transparent, understandable, and trustworthy, will be crucial for the next generation of dialysis patients. The combination of technology, patient-specific data, and AI should contribute to create a more personalized and interactive dialysis process, improving patients' quality of life.

Artificial Intelligence in Society

Artificial Intelligence in Society

ChatGPT Isn't Magic

ChatGPT Isn't Magic

Augmenting a Transitional Care Model With Artificial Intelligence Decreased Readmissions.

Evaluate if augmenting a transitions of care delivery model with insights from artificial intelligence (AI) that applied clinical and exogenous social determinants of health data would reduce rehospitalization in older adults. Retrospective case-control study. Adult patients discharged from integrated health system between November 1, 2019, and February 31, 2020, and enrolled in a rehospitalization reduction transitional care management program. An AI algorithm utilizing multiple data sources including clinical, socioeconomic, and behavioral data was developed to predict patients at highest risk for readmitting within 30days and provide care navigators five care recommendations to prevent rehospitalization. Adjusted incidence of rehospitalization was estimated with Poisson regression and compared between transitional care management enrollees that used AI insights and matched enrollees for whom AI insights were not used. Analyses included 6371 hospital encounters between November 2019 and February 2020 across 12 hospitals. Of the encounters 29.3% were identified by AI as being medium-high risk for re-hospitalizing within 30days, for which AI provided transitional care recommendations to the transitional care management team. The navigation team completed 40.2% of AI recommendations for these high-risk older adults. These patients had overall 21.0% less adjusted incidence of 30-day rehospitalization compared with matched control encounters, or 69 fewer rehospitalizations per 1000 encounters (95% CI 0.65‒0.95). Coordinating a patient's care continuum is critical for safe and effective transition of care. This study found that augmenting an existing transition of care navigation program with patient insights from AI reduced rehospitalization more than without AI insights. Augmenting transitional care with insights from AI could be a cost-effective intervention to improve transitional care outcomes and reduce unnecessary rehospitalization. Future studies should examine cost-effectiveness of augmenting transitional care models of care with AI when hospitals and post-acute providers partner with AI companies.

Performance of emergency triage prediction of an open access natural language processing based chatbot application (ChatGPT): A preliminary, scenario-based cross-sectional study.

Artificial intelligence companies have been increasing their initiatives recently to improve the results of chatbots, which are software programs that can converse with a human in natural language. The role of chatbots in health care is deemed worthy of research. OpenAI's ChatGPT is a supervised and empowered machine learning-based chatbot. The aim of this study was to determine the performance of ChatGPT in emergency medicine (EM) triage prediction. This was a preliminary, cross-sectional study conducted with case scenarios generated by the researchers based on the emergency severity index (ESI) handbook v4 cases. Two independent EM specialists who were experts in the ESI triage scale determined the triage categories for each case. A third independent EM specialist was consulted as arbiter, if necessary. Consensus results for each case scenario were assumed as the reference triage category. Subsequently, each case scenario was queried with ChatGPT and the answer was recorded as the index triage category. Inconsistent classifications between the ChatGPT and reference category were defined as over-triage (false positive) or under-triage (false negative). Fifty case scenarios were assessed in the study. Reliability analysis showed a fair agreement between EM specialists and ChatGPT (Cohen's Kappa: 0.341). Eleven cases (22%) were over triaged and 9 (18%) cases were under triaged by ChatGPT. In 9 cases (18%), ChatGPT reported two consecutive triage categories, one of which matched the expert consensus. It had an overall sensitivity of 57.1% (95% confidence interval [CI]: 34-78.2), specificity of 34.5% (95% CI: 17.9-54.3), positive predictive value (PPV) of 38.7% (95% CI: 21.8-57.8), negative predictive value (NPV) of 52.6 (95% CI: 28.9-75.6), and an F1 score of 0.461. In high acuity cases (ESI-1 and ESI-2), ChatGPT showed a sensitivity of 76.2% (95% CI: 52.8-91.8), specificity of 93.1% (95% CI: 77.2-99.2), PPV of 88.9% (95% CI: 65.3-98.6), NPV of 84.4 (95% CI: 67.2-94.7), and an F1 score of 0.821. The receiver operating characteristic curve showed an area under the curve of 0.846 (95% CI: 0.724-0.969, P < 0.001) for high acuity cases. The performance of ChatGPT was best when predicting high acuity cases (ESI-1 and ESI-2). It may be useful when determining the cases requiring critical care. When trained with more medical knowledge, ChatGPT may be more accurate for other triage category predictions.

495-P: Comparing Detection of Diabetic Retinopathy by Three Artificial Intelligence Platforms in Subjects Not Known to Have Diabetes

Background: The glycemic threshold for the diagnosis of type 2 diabetes (T2D) was determined based upon the prevalence of moderate retinopathy; however, diabetes related complications may occur below that threshold. This study utilized three companies to analyse retinal images using their commercial software and returned data on absence or presence of features of any diabetic retinopathy (DR) on a per-eye basis. Methods: Ophthalmic retinal images from 11,449 subjects (right and left eyes for each, totalling 22,898 images) not known to have diabetes from the Qatar Biobank were subjected to assessment for detection of features of DR using three Conformitè Europëenne (CE)-marked independent commercial Artificial Intelligence (AI) software systems. Results: From 11,449 patients: •DR was recorded as absent in both eyes in 7,094 (62.0%) patients by all 3 methods (Test Negative at patient level).•DR was detected by 2 or 3 of the AI systems in at least one eye in a total of 2,408 (21.0%) patients (Test Positive at patient level). Of these, in 1,532 (13.4%) cases DR was detected in just one eye of any given patient and in 876 (7.6%) cases DR was detected in both eyes of a patient. •Overall, 1,947 (17.0%) patients had at least one eye reported as unassessable by 2 or 3 of the AI systems; hence, No Overall Result for DR features could be obtained for these patients. Results from the three AI companies differed markedly: 17.7%, 32.9% and 46.6% ranked Test-Positive results at patient level. Only 304 right eyes and 280 left eyes were positive for all 3 companies. Conclusion: This is the first direct comparison of three independent AI companies when simultaneously assessing retinal images from more than 10,000 individuals. It showed markedly differing prevalences of diabetic retinopathy between existing AI systems, indicating inconsistencies if wishing to apply these tools to detect retinopathy in subjects not already known to have diabetes, and may differ in patients with known diabetes. Disclosure A.E.Butler: None. E.S.Kilpatrick: None. S.C.Hunt: None. S.Atkin: None.