Artificial Intelligence In Society Research Articles

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.

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.

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

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.

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.

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.

Paving the way for technological innovation through adoption of artificial intelligence in conservative industries

Artificial Intelligence (AI) has emerged as a distinct form of ICT, revolutionizing manufacturing to the fourth industrial revolution. However, inherently complex and time-constrained operations restrict conservative industries from embracing AI transformation, leading to technological innovation. This study attempts to pave the way for AI transformation (leading to technological innovation) in conservative industries by developing and testing a value-based theoretical AI adoption framework. The proposed framework incorporates functional and conditional values as predictors to assess the industrial AI’s fitness to the conservative industry need. Service reliability is taken as a moderator to assess AI acceptance’s intention impact on its consistent use in routine operations in conservative industries. The model was tested in the construction and oil gas industries. A total number of 480 samples were collected from Pakistan. The results have indicated functional value as a significant predictor of the way forward with AI transformation in conservative industries. The other process variables like price value and performance expectancy have shown what drives AI acceptance intention in a conservative industry. The results also found service reliability as a necessity for the sustained use of AI in conservative industries. The findings provide useful insights for industrial AI companies on how such conservative industries envisage AI as a technological innovation and a potential solution to their problem. The framework shall also help conservative industries in evaluating potential AI proposals.

Business Modeling Innovation Using Artificial Intelligence Technology

Startups embracing artificial intelligence (AI) as a component of their business models are quickly emerging right now. The application of AI technology in business has been going on for a while, even though recent research reveals that new or alternative business models are being implemented.It might be claimed that AI technology has been used to business models for a very long time, which puts into question the uniqueness of these business models. In order to better understand how AI companies' business models may differ from traditional IT business models, this study compares them to each other. The first step is to create a taxonomy of business models for AI businesses using a sample of 162 Worldwide Stratup, from which four archetypal business models are derived: Deep technology researcher, data analytics supplier, AI product and service provider, and facilitator of AI development The following are three main elements of startup business models for AI firms that are discussed based on this descriptive analysis: (1) new value propositions made possible by AI, (2) Various uses of data to create value and (3) How AI technology affects general business reasoning. By defining their key purposes, common instantiations, and distinguishing features, this research adds to our fundamental understanding of the AI start-up business model. This study suggests intriguing directions for further investigation in the field of entrepreneurship. À It is structured to foster entrepreneurial activity. Taxonomies and models are actually instrument.

Business Modeling Innovation Using Artificial Intelligence Technology

Startups embracing artificial intelligence (AI) as a component of their business models are quickly emerging right now. The application of AI technology in business has been going on for a while, even though recent research reveals that new or alternative business models are being implemented.It might be claimed that AI technology has been used to business models for a very long time, which puts into question the uniqueness of these business models. In order to better understand how AI companies' business models may differ from traditional IT business models, this study compares them to each other. The first step is to create a taxonomy of business models for AI businesses using a sample of 162 Worldwide Stratup, from which four archetypal business models are derived: Deep technology researcher, data analytics supplier, AI product and service provider, and facilitator of AI development The following are three main elements of startup business models for AI firms that are discussed based on this descriptive analysis: (1) new value propositions made possible by AI, (2) Various uses of data to create value and (3) How AI technology affects general business reasoning. By defining their key purposes, common instantiations, and distinguishing features, this research adds to our fundamental understanding of the AI start-up business model. This study suggests intriguing directions for further investigation in the field of entrepreneurship. À It is structured to foster entrepreneurial activity. Taxonomies and models are actually instrument.

Tracking Online Interest in Artificial Intelligence in Ophthalmology Using Google Trends

ABSTRACT Purpose To examine trends in internet search queries related to artificial intelligence (AI) in ophthalmology and determine the correlation between online interest in AI, capital investment in AI, and peer-reviewed indexed publications regarding AI and ophthalmology. Methods Online search trends for “AI retina”, “AI eye”, and “AI healthcare” were obtained via Google Trends from 2016 to 2022 on a relative interest scale in 1-week intervals. Global venture financing of AI- and machine learning (ML)-focused companies in healthcare was tracked from 2010 to 2019 from the consulting company, Klynveld Peat Marwick Goerdeler (KPMG), and the technology market intelligence company, CB Insights. Citation count from pubmed.gov was determined using the search query “artificial intelligence retina” from 2012 to 2021. Results An increasingly linear growth in online search trends for “AI retina”, “AI eye”, and “AI healthcare” keyword searches was observed between 2016 and 2022. Global venture financing of AI and ML companies in healthcare also increased exponentially over the same time frame. There was an exponential increase in citations with nearly a 10-fold increase as reported by PubMed from 2015 onwards for the “artificial intelligence retina” search query. There was a significant and positive correlation between online search trends and investment trends (correlation coefficients of 0.98–0.99 and p-values <0.05) and between online search trends and citation count trends (correlation coefficients of 0.98–0.99 and p-values <0.05). Conclusions These results demonstrate that the applications of AI and ML in ophthalmology are increasingly being investigated, financed, and formally researched, suggesting a prominent role for AI-derived tools in ophthalmology clinical practice in the near future.

Ethics of Artificial Intelligence in Society

ABSTRACT: Every day, artificial intelligence (AI) is becoming more prevalent as new technologies are presented to the public with the intent of integrating them into society. However, these systems are not perfect and are known to cause failures that impact a multitude of people. The purpose of this study is to explore how ethical guidelines are followed by AI when it is being designed and implemented in society. Three ethics theories, along with nine ethical principles of AI, and the Agent, Deed, Consequence (ADC) model were investigated to analyze failures involving AI. When a system fails to follow the models listed, a set of refined ethical principles are created. By analyzing the failures, an understanding of how similar incidents may be prevented was gained. Additionally, the importance of ethics being a part of AI programming was demonstrated, followed by recommendations for the future incorporation of ethics into AI. The term “failure” is specifically used throughout the paper because of the nature in which the events involving AI occur. The events are not necessarily “accidents” since the AI was intended to act in certain ways, but the events are also not “malfunctions” because the AI examples were not internally compromised. For these reasons, the much broader term “failure” is used. KEYWORDS: Ethics; Artificial Intelligence; Agent-Deed-Consequence (ADC) Model; Principles of Artificial Intelligence; Virtue Ethics; Deontology; Consequentialism; AI Systems

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.

Social media advertising for clinical studies: Ethical and data protection implications of online targeting

Social media advertising has revolutionised the advertising world by providing data-driven targeting methods. One area where social media advertising is just gaining a foothold is in the recruitment of clinical study participants. Here, as everywhere, social media advertising promises more yield per money spent because the technology can better reach highly specialised groups. In this article, we point out severe societal risks posed by advertising for clinical studies on social media. We show that social media advertising for clinical studies in many cases violates the privacy of individual users (R1), creates collective privacy risks by helping platform companies train predictive models of medical information that can be applied to all their users (R2), exploits the weaknesses of existing guidelines in (biomedical) research ethics (R3) and is detrimental to the quality of (biomedical) research (R4). We argue that the well-intentioned promises, which are often associated with the use of social media advertising for clinical studies, are untenable from a balanced point of view. Consequently, we call for updates of research ethics guidelines and better regulation of Big Data and inferential analytics. We conclude that social media advertising – especially with vulnerable patient populations – is not suitable as a recruitment tool for clinical studies as long as the processing of (even anonymised) social media usage data and the training of predictive models by data analytics and artificial intelligence companies is not sufficiently regulated.

Radiologist’s Role in Artificial Intelligence Era

Artificial intelligence (AI) in radiology is recently a rapidly growing subject. Much literature about AI in radiology has been launched within 5 years, as well as commercial AI companies. This phenomenon makes some old radiologists feel worried about losing their jobs, and junior doctors hesitate to choose radiology as a specialty. Currently, implementations of proprietary AIs in clinical practice are limited, with a default setting for a convenient human overwrite. The AIs in clinical imaging largely remain either investigational as part of clinical/pre-clinical trials or being developed for commercialized purposes. Radiologists have an important role in all AI processes from the beginning to the end and vital in training the machine, as well as to validate its added benefit for outcome prediction/prognostication. This article will discuss the importance for radiologists to develop, implement, and monitor AI in clinical imaging, together with some ethical considerations. We would like to encourage radiologists to use AI as an adjunct tool, to save time and have better performance.

Artificial intelligence in society: Technology in Society Briefing

Artificial intelligence in society: Technology in Society Briefing