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Highlights • In this position paper, we provide a collection of views on the role of AI in the COVID-19 pandemic, from the clinical needs to the design of AI-based systems, to the translation of the developed tools to the clinic. We highlight key factors in designing system solutions - per specific task; as well as design issues in managing the disease on the national level. We focus on three specific use-cases for which AI systems can be built: from the early disease detection, the management of the disease in a hospital setting, and building patient-specific predictive models that require the combination of imaging with additional clinical features. Infrastructure considerations and population modeling in two European countries will be described. This pandemic has made the practical and scientific challenges of making AI solutions very explicit. A discussion concludes this paper, with a list of challenges facing the community in the AI road ahead., In this position paper, we provide a collection of views on the role of AI in the COVID-19 pandemic, from clinical requirements to the design of AI-based systems, to the translation of the developed tools to the clinic. We highlight key factors in designing system solutions - per specific task; as well as design issues in managing the disease at the national level. We focus on three specific use-cases for which AI systems can be built: early disease detection, management in a hospital setting, and building patient-specific predictive models that require the combination of imaging with additional clinical data. Infrastructure considerations and population modeling in two European countries will be described. This pandemic has made the practical and scientific challenges of making AI solutions very explicit. A discussion concludes this paper, with a list of challenges facing the community in the AI road ahead.

Rock-paper-scissors is a simple game played by two players who use hand gestures that resemble a 'rock' (a fist), a piece of 'paper' (a flat hand) or a pair of 'scissors' (index and middle fingers in the shape of a V). Each player plays a gesture at the same time, with the scoring as follows: 'rock' beats 'scissors'; 'paper' beats 'rock'; and 'scissors' beats 'paper'. If the game is played only once, no dominating strategy exists and the result is down to chance. If the game is played multiple times the chance element reduces as a player's next gesture is influenced by two factors: the previous result (win, loss or draw) and the previous gesture ('rock', 'paper' or 'scissors'). Previous attempts by RoShamBot to create an artificial intelligence (or AI) for rock-paper-scissors have relied on simple frequency analysis (i.e. the user has played 'rock' the most, therefore the AI must play 'paper' next) or history matching (i.e. the AI matching the last four rounds to a large database of previous games and determining what gesture the user will play next and then countering that gesture). In an actuarial spirit, I take a stochastic approach and instead of determining the next gesture with certainty, I flex the probabilities for 'rock', 'paper' and 'scissors' and then let my AI randomly choose one. The probabilities are flexed based on previous results and previous gestures.

The COVID-19 pandemic is rapidly spreading across the globe and infected millions of people that take hundreds of thousands of lives. Over the years, the role of Artificial intelligence (AI) has been on the rise as its algorithms are getting more and more accurate and it is thought that its role in strengthening the existing healthcare system will be the most profound. Moreover, the pandemic brought an opportunity to showcase AI and healthcare integration potentials as the current infrastructure worldwide is overwhelmed and crumbling. Due to AI’s flexibility and adaptability, it can be used as a tool to tackle COVID-19. Motivated by these facts, in this paper, we surveyed how the AI techniques can handle the COVID-19 pandemic situation and present the merits and demerits of these techniques. This paper presents a comprehensive end-to-end review of all the AI-techniques that can be used to tackle all areas of the pandemic. Further, we systematically discuss the issues of the COVID-19, and based on the literature review, we suggest their potential countermeasures using AI techniques. In the end, we analyze various open research issues and challenges associated with integrating the AI techniques in the COVID-19.

What if things had a voice? What if we could talk directly to things instead of using a mediating voice interface such as an Alexa or a Google Assistant? In this paper, we share our insights from talking to a pair of boots, a tampon, a perfume bottle, and toilet paper among other everyday things to explore their conversational capabilities. We conducted Thing Interviews using a more-than-human design approach to discover a thing’s perspectives, worldviews and its relations to other humans and nonhumans. Based on our analysis of the speculative conversations, we identified some themes characterizing the emergent qualities of people’s relationships with everyday things. We believe the themes presented in the paper may inspire future research on designing everyday things with conversational capabilities at home.

Cet article propose une analyse de la ponctuation effectuée par les acteurs institutionnels dans le cadre de communication domotique des crises, dans une problématique qui articule information, confiance et résilience, à partir d’une approche comparative avec le cadrage médiatique. Il examine également la ponctuation par l’image en tant que médiatisation caractéristique du positionnement de nouveaux appareillages médiatiques numériques, notamment la plateforme YouTube. Il montre enfin la pertinence de la ponctuation en tant qu’opérateur communicationnel de confiance et de résilience domotique en temps de crise ainsi que de fiducies numériques d’usage et d’images de l’information. Este artículo propone un análisis de la puntuación que realizan los actores institucionales en la comunicación de crisis interna, en un enfoque comparativo con el encuadre mediático basado en la interconexión entre información, confianza y resiliencia. Este artículo estudia igualmente la puntuación por imágenes como mediatización característica de los nuevos dispositivos digitales de información, en particular de la plataforma YouTube. Muestra finalmente la pertinencia del concepto de puntuación como operador comunicacional de confianza y resiliencia en crisis internas, con fiduciarios digitales de imágenes de información.

Background Early detection of influenza activity followed by timely response is a critical component of preparedness for seasonal influenza epidemic and influenza pandemic. However, most relevant studies were conducted at the regional or national level with regular seasonal influenza trends. There are few feasible strategies to forecast influenza activity at the local level with irregular trends. Methods Multi-source electronic data, including historical percentage of influenza-like illness (ILI%), weather data, Baidu search index and Sina Weibo data of Chongqing, China, were collected and integrated into an innovative Self-adaptive AI Model (SAAIM), which was constructed by integrating Seasonal Autoregressive Integrated Moving Average model and XGBoost model using a self-adaptive weight adjustment mechanism. SAAIM was applied to ILI% forecast in Chongqing from 2017 to 2018, of which the performance was compared with three previously available models on forecasting. Findings ILI% showed an irregular seasonal trend from 2012 to 2018 in Chongqing. Compared with three reference models, SAAIM achieved the best performance on forecasting ILI% of Chongqing with the mean absolute percentage error (MAPE) of 11·9%, 7·5%, and 11·9% during the periods of the year 2014–2016, 2017, and 2018 respectively. Among the three categories of source data, historical influenza activity contributed the most to the forecast accuracy by decreasing the MAPE by 19·6%, 43·1%, and 11·1%, followed by weather information (MAPE reduced by 3·3%, 17·1%, and 2·2%), and Internet-related public sentiment data (MAPE reduced by 1·1%, 0·9%, and 1·3%). Interpretation Accurate influenza forecast in areas with irregular seasonal influenza trends can be made by SAAIM with multi-source electronic data.

Background Although many prognosis-predicting molecular scores for breast cancer have been developed, they are applicable to only limited disease subtypes. We aimed to develop a novel prognostic score that is applicable to a wider range of breast cancer patients. Methods We initially examined The Cancer Genome Atlas breast cancer cohort to identify potential prognosis-related genes. We then performed a meta-analysis of 36 international breast cancer cohorts to validate such genes. We trained artificial intelligence models (random forest and neural network) to predict prognosis precisely, and we finally validated our prediction with the log-rank test. Findings We identified a comprehensive list of 184 prognosis-related genes, most of which have been not extensively studied to date. We then established a universal molecular prognostic score (mPS) that relies on the expression status of only 23 of these genes. The mPS system is almost universally applicable to breast cancer patients (log-rank P

Background Viewing their habitual smoking environments increases smokers’ craving and smoking behaviors in laboratory settings. A deep learning approach can differentiate between habitual smoking versus nonsmoking environments, suggesting that it may be possible to predict environment-associated smoking risk from continuously acquired images of smokers’ daily environments. Objective In this study, we aim to predict environment-associated risk from continuously acquired images of smokers’ daily environments. We also aim to understand how model performance varies by location type, as reported by participants. Methods Smokers from Durham, North Carolina and surrounding areas completed ecological momentary assessments both immediately after smoking and at randomly selected times throughout the day for 2 weeks. At each assessment, participants took a picture of their current environment and completed a questionnaire on smoking, craving, and the environmental setting. A convolutional neural network–based model was trained to predict smoking, craving, whether smoking was permitted in the current environment and whether the participant was outside based on images of participants’ daily environments, the time since their last cigarette, and baseline data on daily smoking habits. Prediction performance, quantified using the area under the receiver operating characteristic curve (AUC) and average precision (AP), was assessed for out-of-sample prediction as well as personalized models trained on images from days 1 to 10. The models were optimized for mobile devices and implemented as a smartphone app. Results A total of 48 participants completed the study, and 8008 images were acquired. The personalized models were highly effective in predicting smoking risk (AUC=0.827; AP=0.882), craving (AUC=0.837; AP=0.798), whether smoking was permitted in the current environment (AUC=0.932; AP=0.981), and whether the participant was outside (AUC=0.977; AP=0.956). The out-of-sample models were also effective in predicting smoking risk (AUC=0.723; AP=0.785), whether smoking was permitted in the current environment (AUC=0.815; AP=0.937), and whether the participant was outside (AUC=0.949; AP=0.922); however, they were not effective in predicting craving (AUC=0.522; AP=0.427). Omitting image features reduced AUC by over 0.1 when predicting all outcomes except craving. Prediction of smoking was more effective for participants whose self-reported location type was more variable (Spearman ρ=0.48; P=.001). Conclusions Images of daily environments can be used to effectively predict smoking risk. Model personalization, achieved by incorporating information about daily smoking habits and training on participant-specific images, further improves prediction performance. Environment-associated smoking risk can be assessed in real time on a mobile device and can be incorporated into device-based smoking cessation interventions.

BackgroundClinical decision support systems are designed to utilize medical data, knowledge, and analysis engines and to generate patient-specific assessments or recommendations to health professionals in order to assist decision making. Artificial intelligence–enabled clinical decision support systems aid the decision-making process through an intelligent component. Well-defined evaluation methods are essential to ensure the seamless integration and contribution of these systems to clinical practice.ObjectiveThe purpose of this study was to develop and validate a measurement instrument and test the interrelationships of evaluation variables for an artificial intelligence–enabled clinical decision support system evaluation framework.MethodsAn artificial intelligence–enabled clinical decision support system evaluation framework consisting of 6 variables was developed. A Delphi process was conducted to develop the measurement instrument items. Cognitive interviews and pretesting were performed to refine the questions. Web-based survey response data were analyzed to remove irrelevant questions from the measurement instrument, to test dimensional structure, and to assess reliability and validity. The interrelationships of relevant variables were tested and verified using path analysis, and a 28-item measurement instrument was developed. Measurement instrument survey responses were collected from 156 respondents.ResultsThe Cronbach α of the measurement instrument was 0.963, and its content validity was 0.943. Values of average variance extracted ranged from 0.582 to 0.756, and values of the heterotrait-monotrait ratio ranged from 0.376 to 0.896. The final model had a good fit (χ262=36.984; P=.08; comparative fit index 0.991; goodness-of-fit index 0.957; root mean square error of approximation 0.052; standardized root mean square residual 0.028). Variables in the final model accounted for 89% of the variance in the user acceptance dimension.ConclusionsUser acceptance is the central dimension of artificial intelligence–enabled clinical decision support system success. Acceptance was directly influenced by perceived ease of use, information quality, service quality, and perceived benefit. Acceptance was also indirectly influenced by system quality and information quality through perceived ease of use. User acceptance and perceived benefit were interrelated.

BackgroundThe COVID-19 pandemic has disrupted human societies around the world. This public health emergency was followed by a significant loss of human life; the ensuing social restrictions led to loss of employment, lack of interactions, and burgeoning psychological distress. As physical distancing regulations were introduced to manage outbreaks, individuals, groups, and communities engaged extensively on social media to express their thoughts and emotions. This internet-mediated communication of self-reported information encapsulates the emotional health and mental well-being of all individuals impacted by the pandemic.ObjectiveThis research aims to investigate the human emotions related to the COVID-19 pandemic expressed on social media over time, using an artificial intelligence (AI) framework.MethodsOur study explores emotion classifications, intensities, transitions, and profiles, as well as alignment to key themes and topics, across the four stages of the pandemic: declaration of a global health crisis (ie, prepandemic), the first lockdown, easing of restrictions, and the second lockdown. This study employs an AI framework comprised of natural language processing, word embeddings, Markov models, and the growing self-organizing map algorithm, which are collectively used to investigate social media conversations. The investigation was carried out using 73,000 public Twitter conversations posted by users in Australia from January to September 2020.ResultsThe outcomes of this study enabled us to analyze and visualize different emotions and related concerns that were expressed and reflected on social media during the COVID-19 pandemic, which could be used to gain insights into citizens’ mental health. First, the topic analysis showed the diverse as well as common concerns people had expressed during the four stages of the pandemic. It was noted that personal-level concerns expressed on social media had escalated to broader concerns over time. Second, the emotion intensity and emotion state transitions showed that fear and sadness emotions were more prominently expressed at first; however, emotions transitioned into anger and disgust over time. Negative emotions, except for sadness, were significantly higher (PConclusionsThis study showed that the diverse emotions and concerns that were expressed and recorded on social media during the COVID-19 pandemic reflected the mental health of the general public. While this study established the use of social media to discover informed insights during a time when physical communication was impossible, the outcomes could also contribute toward postpandemic recovery and understanding psychological impact via emotion changes, and they could potentially inform health care decision making. This study exploited AI and social media to enhance our understanding of human behaviors in global emergencies, which could lead to improved planning and policy making for future crises.

Background Wearables and artificial intelligence (AI)–powered digital health platforms that utilize machine learning algorithms can autonomously measure a senior’s change in activity and behavior and may be useful tools for proactive interventions that target modifiable risk factors. Objective The goal of this study was to analyze how a wearable device and AI-powered digital health platform could provide improved health outcomes for older adults in assisted living communities. Methods Data from 490 residents from six assisted living communities were analyzed retrospectively over 24 months. The intervention group (+CP) consisted of 3 communities that utilized CarePredict (n=256), and the control group (–CP) consisted of 3 communities (n=234) that did not utilize CarePredict. The following outcomes were measured and compared to baseline: hospitalization rate, fall rate, length of stay (LOS), and staff response time. Results The residents of the +CP and –CP communities exhibit no statistical difference in age (P=.64), sex (P=.63), and staff service hours per resident (P=.94). The data show that the +CP communities exhibited a 39% lower hospitalization rate (P=.02), a 69% lower fall rate (P=.01), and a 67% greater length of stay (P=.03) than the –CP communities. The staff alert acknowledgment and reach resident times also improved in the +CP communities by 37% (P=.02) and 40% (P=.02), respectively. Conclusions The AI-powered digital health platform provides the community staff with actionable information regarding each resident’s activities and behavior, which can be used to identify older adults that are at an increased risk for a health decline. Staff can use this data to intervene much earlier, protecting seniors from conditions that left untreated could result in hospitalization. In summary, the use of wearables and AI-powered digital health platform can contribute to improved health outcomes for seniors in assisted living communities. The accuracy of the system will be further validated in a larger trial.

This paper analyzes the effect of the characteristics of 5G services on users' continuous intention to use, focusing on the technology acceptance model. With the start of the fourth industrial revolution in the 21st century, 5G is the best technology used in the Internet of Things, high-speed information and communication, artificial intelligence, big data, autonomous vehicles, virtual reality, augmented reality, robots, nanotechnology, and blockchain. The technical characteristics of 5G ultra-high-speed information communication are represented by ultra-high speed, ultra-high capacity, ultra-low delay, and ultra-high connectivity. 5G mobile communication technology is essential, and after the technology provided by 5G services is commercialized, it can play all its roles as a practical core new growth engine. 5G mobile communication (hereinafter referred to as 5G) is far superior to LTE, which is a 4G mobile communication, in terms of transmission speed, waiting time, and terminal capacity. 5G service is not just an axis of the process of developing mobile communication technology, but also the creation of innovative corporate value of technology. This is because higher network quality and innovation with 5G service technology will improve perceived usability, perceived ease of use, and perceived entertainment, which will ultimately have a positive impact on users' intention to use 5G services. Therefore, due to the lack of investment in information and communication bases, platforms, and applications, this paper can be used as the basis for establishing government policies.

This article aims to explore the main areas of research, development trends and provide a systematic overview of publications in the field of artificial intelligence in financial markets. The bibliometric tool VOSViewer is used in this paper. We analysed 353 articles and contributions obtained from the database of Web of Science, and summarized our findings as follows: artificial intelligence is becoming increasingly widespread in the field of finance and interdisciplinary interconnection; artificial intelligence tools such as neural networks and fuzzy logic are most often used to predict the development of financial time series, or to create decision models; the most important cited authors in this field are Markowitz and Lebaron. Expert System with Application is the cradle of a significant part of fundamental research in the field of artificial intelligence. By using effective bibliometric methods, we provide comprehensive analysis and in-depth insight into the subject area of research, which allows individuals and especially new beginners interested in this area to obtain valuable information and possible direction of future research. The study is recommended to focus on hybrid models prediction of individual sectors of the financial markets, which are present in the current research on the rise.

Purpose: This paper aims to classify potato disease using convolutional neural network in different epochs to observe the best performance of the model. The best model will help the farmers to make different decisions to prevent the loss of potato production. Methodology: The paper implements a deep learning approach, the convolutional neural network, to explore potato disease classification. To accomplish the research objective, we collected 10000 images of potato leaves from different sources like google and raw data from potato fields. We collected a dataset of 2152 images from Kaggle and the other 7848 images from the above sources. The dataset belongs to a few classes. The classes are Potato Early Blight, Potato Late Blight, and Potato healthy leaf. The paper includes four main steps: data acquisition, data pre-processing, data augmentation, and image classification to find the output. Findings: This study found that the model performed better when we applied 40 epochs for the 10000 images dataset & we achieved 100% accuracy as we applied a total of 3 different epochs and achieved an accuracy of 99.97% and 99.98% for 30 and 50 epochs, respectively. Research Limitations: The study significantly contributed to the agriculture sector and farmers by providing suggestions to classify the Potato leaf Disease with the best output. Besides, researchers need more raw data to build the model for better output, and they also should be concerned regarding the system when working with large volumes of data as it takes longer to run the code. Originality/Value: This research paper contained high volume of the dataset, which is 10000 images of potato leaves. We collected a dataset of 2152 images from Kaggle and the rest, 7848 images from different sources like google, and raw data from potato filed. We showed different epochs to check the best performance and achieved 100% accuracy when 40 epochs were applied.

This paper examines ways in which the ethics of data-driven technologies might be (re)politicised, particularly where educational institutions are involved. The recent proliferation of principles, guidelines, and frameworks for ethical ‘AI’ (artificial intelligence) have emerged from a plethora of organisations in recent years, and seem poised to impact educational governance. This trend will be firstly shown to align with a narrow form of ethics - deontology - and overlook other potential ways ethical reasoning might contribute to thinking about ‘AI’. Secondly, the attention to ethical principles will be suggested to focus excessively on the technology itself, with the effect of masking political concerns for equity and justice. Thirdly and finally, the paper will propose a more radical form of participation in ethical decision-making that not only challenges the assumption of universal consensus, but also draws more authentically on the capacities for debate, contestation, and exchange inherent in the educational institution.

The study seeks to understand how the AI ecosystem might be implicated in a form of knowledge production which reifies particular kinds of epistemologies over others. Using text mining and thematic analysis, this paper offers a horizon scan of the key themes that have emerged over the past few years during the AIEd debate. We begin with a discussion of the tools we used to experiment with digital methods for data collection and analysis. This paper then examines how AI in education systems are being conceived, hyped, and potentially deployed into global education contexts. Findings are categorised into three themes in the discourse: (1) geopolitical dominance through education and technological innovation; (2) creation and expansion of market niches, and (3) managing narratives, perceptions, and norms.

My technical paper and STS research paper are most related to my University of Virginia (UVA) major and minor degrees. I am majoring in computer science and minoring in data science. My capstone paper is based on my internship work and the research paper focuses on the artificial intelligence (AI) model, ChatGPT. The summer before my fourth year at UVA, I interned at General Atomics Commonwealth Computer Reasearch, Inc (GA-CCRi) as a backend software engineer intern, and I worked on a project called the MR Scraper. The scraper was a tool used to retrieve data from our GitLab Application Programming Interface (API) and format it in such a way that it is consise and understandable. My internship was directly correlated to my computer science degree wheras my STS research paper had a more data science premise. The topic of my research paper came to fruition because of the recent publicity spike of AI models like ChatGPT. I was interested in the many new AIs releasing, but ChatGPT especially caught my eye when I was instructed not to use it in one of the syllabus lessons for my computer science class during my last semester. My capstone is about the MR Scraper and how I helped to cement it into the GA-CCRi infrastructure. Before my time there they started working on the scraper, but had stopped developing it so they could work on other projects. The implementation that they were working with had issues and known flaws. I followed the software development life cycle to implement my solutions to these flaws. The MR Scraper successfully extracted crucial information from the API, including author details, timestamps, comments or titles, and approvers for all GA-CCRi’s projects and merge requests. This data was then processed and formatted to align with the requirements of Grafana, a graphing model used for data visualization. The integration of the MR Scraper allows the company to view the evolution of code changes through visually appealing graphs and tables, significantly enhancing productivity and aiding effective resource allocation. The STS research paper is about the large language model called ChatGPT. It works by predicting the next word or phrase when given a prompt. After the user sends a statement to ChatGPT, like they would talking to a chatbot, the program will use its trained model to predict the next word or phrase in its response. It will continue to make these predictions until a full answer is given. ChatGPT's response capabilities makes it a powerful tool for various applications, including research assistance and programming support. Instead of helpful tools, it can also be used to completely write essays and complete programming assignments. These reasons worry professors about students violating the honor code and using it to complete assignments. My paper helps to understand ChatGPT through the Social Construction of Technology framework and looks at the relevant social groups and how they have collaborated and will continue to do so until the technology has stabilized. My capstone provided a healthy reflection on my work at GA-CCRi. I did not fully comprehend the value behind visualizing the productivity of a team. I knew I was helping out the company with my work, but I now know how viewing the graphs and tables could optimize the company’s productivity as a whole. I am also glad I could have a deeper dive into a topic related to my data science minor with the research paper. After writing it, I am even more interested in the upcoming machine learning models and I’m both excited and scared for what the future brings with AI.

Artificial Intelligence (AI) has become a rapidly growing and transformative technology that has the potential to revolutionize many aspects of our lives from driving to writing. As such, it is crucial to ensure that AI is developed and used in an ethical and accountable manner. Thus, this synthesis includes two research projects connected by the principles and processes of building responsible and ethical AI. The first paper will focus on the development of a framework for explainable AI (XAI) using SafeBench, a software designed and used to seamlessly generate car simulation scenarios in a vehicle simulation software known as CARLA. The later aim of this research is to utilize this framework to detect out-of-distribution features from video of AI crashes that can be analyzed and used in legal proceedings to explain the decision-making process of the AI system. The second paper demonstrating the STS research will highlight the legal implications of AI systems that lack accountability, specifically under tort litigation, biased sentencing, and intellectual property, as well as the unintended consequences of relying on such systems. Together, both of these papers shed light on the importance of building ethical and accountable AI systems that can be trusted by providing means to protect individuals and prevent harm from AI’s consequences. The technical component of this project aims to investigate how SafeBench can be used to autonomously generate car simulation scenarios and later be used to detect out-of-distribution factors in AI images saved from car cameras. Towards this, the CARLA self-driving simulator helps to accurately simulate and generate crash data while SafeBench uses reinforcement learning and generative scenarios to train the CARLA agent. Throughout the paper, the agents’ performance is visualized across several scenarios and figures and limitations and potential areas for future work are discussed afterwards. As AI becomes more ubiquitous, understanding their decisions becomes increasingly crucial to hold actors legally accountable when unintended consequences inevitably arise. By identifying factors relevant to these decisions, such as out-of-distribution features, we can provide more accurate and persuasive explanations for AI decisions in court cases. Thus, this research provides a framework that stands as a crucial step towards making AI systems more transparent and explainable, making it possible to build safer and more trustworthy AI systems that can be used throughout a variety of contexts. The STS component of this project investigates the many legal complications that arise as a result of AI systems that lack accountability. As dominating rise in society, especially in transportation, criminal justice, and art, indicates an increasing importance to understand AI’s legal implications. This STS paper will explore how legal systems currently fail to determine accountable actors when AI make erroneous or unethical decisions that cause harm. By applying Susan Leigh Star’s framework on infrastructure, the paper examines how traditional legal concepts such as fault, liability, and intellectual property, erode in the face of complex socio-technical systems involving AI decision-making. Finally, both components of this project are designed to simultaneously address the overall socio-technical challenges of building ethnically accountable AI. By developing a framework for explainable AI while investigating the legal implications of AI decision-making, this project aims to contribute a more comprehensive understanding of AI’s impact on societal functions. Therefore, integrating these technical and social perspectives offers a holistic approach to building AI systems that justifies a need for them to be transparent, accountable, and trustworthy so that we develop responsible AI designed to benefit society while minimizing the risks of unintended consequences associated with this powerful technology.

My capstone research project as a computer science student was a reflection on my internship experience at AWS this past summer. I worked on AWS Genomics CLI to make the product, which does batch processing for DNA sequencing workloads, easier to work with a smaller learning curve. In order to address this problem I developed a full stack web application which transformed the AWS Genomics CLI into a web app using Next.js and Flask alongside Docker and a number of AWS services. It is important to consider the human and social dimensions of this web app because DNA sequencing workloads can be major privacy concerns, incorrect results can be bad for scientific results, and there could be racial and/or sex based concerns on the differences found by the results. For my capstone paper, I believe that the most important STS theories would be reflective practice, tacit knowledge (particularly documenting my tacit knowledge), and black boxes. I chose these three because the first two encapsulate the meaning for me writing the paper, reflection and self improvement. The black boxes is a good theory for my paper because the Genomics CLI could for the most part remain a black box while I developed the website. On the other hand, my STS research paper is on the differences between the actor networks involved in the creation of machine learning models for environmental sustainability between academia and Amazon. It will use Actor Network theory as a framework for understanding the planning of the research and the evidence will be collected through document analysis. I believe the research will show that businesses are much more impacted by customers and business goals rather than pure environmental sustainability. On the other hand I believe academia will be much more driven by true sustainability, personal researcher goals, and the location of funding. In concert, my STS research paper and capstone project are a good way for me to understand the company I will be working for after college and how I can improve my own work whilst also impacting social issues at the company.

Executive Summary The present Deliverable, ‘D9.2 – In-depth analysis of legal and ethical requirements’, part of Work Package 9 ‘Ethical and Legal Framework’ (WP9) of the In Silico World project, assesses the core pieces of legislation and ethical principles identified in Deliverable ‘D9.1 – Legal and Ethical Inventory’. The report analyses the following areas of legislation – and identifies some key issues – relevant to the In Silico World project and, thus, for in silico trials: Privacy and Data Protection: Data protection is a long-established legislation in the European Union (EU), which has evolved throughout the last decades, and the GDPR is the fundamental EU law to consider for in silico trials. Hence, the report introduces the principles of data processing, the notion of personal data and health data and the legal bases for data processing. In silico trials may highlight some of the common challenges for the healthcare sector in data protection. These include the legal bases and the further processing of health data. In silico trials may also raise interpretative questions. These include doctrinal discussions about the nature of synthetic data in their relationship with anonymization and pseudonymisation, and the concept of data ownership. Data Governance: The legal landscape concerning health data sharing is changing due to a series of new EU legislative initiatives. These initiatives encompass the Data Governance Act, the European Health Data Space (EHDS) proposal, and the Data Act proposal. The Data Governance Act sets rules for re-using certain categories of personal data and introduces the concept of data altruism in healthcare. The EHDS proposal regulates, inter alia, the primary and secondary use of health data. The Data Act proposal proposes business-to-consumer, business-to-business and business-to-government data sharing rules. These three pieces of legislation are expected to apply simultaneously once all are approved. Nevertheless, some challenges may arise in the future, which may concern the appropriate legal basis for data sharing and data altruism, and the interaction of these with the GDPR and national legislation. Clinical Trials, Medicinal Products and Medical Devices: Clinical trials, medicinal products and medical device legislation are relevant to the very essence of in silico trials. For clinical trials and medicinal products, Regulation 726/2004 provides the legal basis for the EMA to deal with novel methodologies for drug development. The Medical Device Regulation (MDR) and In Vitro Medical Device Regulation (IVDR) are the applicable laws for medical device legislation and explicitly mention modelling and simulation. However, while these laws do not prohibit in silico trials, they do not extensively address them either. There exist challenges and barriers that need to be addressed both on a regulatory and legislative perspective. On the regulatory side, guidance and standardisation efforts are needed in general (to tackle, for example, Artificial Intelligence (AI)) and in particular (on the verification and validation of in silico models). On the legislative side, there is common agreement that the current pharmaceutical legal framework lacks behind digital innovation processes. The EU Pharmaceutical Strategy promises the reform of the existing pharmaceutical framework. It is desirable that the reform will address more comprehensively the innovative aspects of the medicinal product’s lifecycle, including in silico trials. Artificial Intelligence: The legal framework of AI is in the process of being established in the EU. The AI Act proposal, expected to be approved in 2023, will introduce new requirements for providers, users and all the actors involved in AI systems. These include, inter alia, adopting a risk management system, a quality management system, documentation duties, ensuring transparency, human oversight, accuracy, robustness, and cybersecurity. The AI Act will be relevant for medical devices and in vitro diagnostic medical devices, as – according to the latest available version of the proposal – they are explicitly included in the scope of the regulation. The regulation sparked several discussions that this report cannot summarise comprehensively. Therefore, the report chooses a new item currently negotiated in the latest proposal’s version may be of crucial relevance for medicine and healthcare: the potential non-application of AI rules in the context of scientific research. The preliminary conclusion is that these rules, as currently formulated, may generate legal uncertainties in the future. The report concludes with a section on the ‘Ethics Principles’. The section is based on the Biomedical Ethics principles that were illustrated in D9.1, i.e. autonomy, justice, beneficence, and non-maleficence. The report offers some examples (patients’ self-determination for autonomy; incidental findings for beneficence; safety and security risks for non-maleficence; patients’ representativeness for justice) to show how these principles could guide stakeholders to ensure the protection and advancement of human values in the context of in silico trials., {"references":["Anton Vedder and Daniela Spajić, 'Moral Autonomy of Patients and Legal Barriers to a Possible Duty of Health Related Data Sharing' (2023) 25 Ethics and Information Technology 23","Aljaaf AJ and others, 'Partially Synthesised Dataset to Improve Prediction Accuracy' in De-Shuang Huang, Vitoantonio Bevilacqua and Prashan Premaratne (eds), Intelligent Computing Theories and Application (Springer International Publishing 2016)","Arnaudo L and Pitruzzella G, La Cura Della Concorrenza: L'industria Farmaceutica Tra Diritti e Profitti (1. ed, Luiss University Press 2019)","Article 29 Data Protection Working Party, 'Opinion 4/2007 on the Concept of Personal Data'","Article 29 Working Party, 'Opinion 05/2014 on Anonymisation Techniques' ","Assessing the Credibility of Computational Modeling through Verification and Validation: Application to Medical Devices (American Society of Mechanical Engineers 2018)","Avicenna Alliance, 'Modelling and Simulation as a Transformative Tool for Medical Devices: The Transatlantic Regulatory Perspective' (2018)","Avicenna CSA, 'In Silico Clinical Trials: How Computer Simulation Will Transform the Biomedical Industry' (2016)","Azizi Z and others, 'Can Synthetic Data Be a Proxy for Real Clinical Trial Data? 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Purpose This paper aims to provide an overview of key definitions related to ChatGPT, a public tool developed by OpenAI, and its underlying technology, Generative Pretrained Transformer (GPT). Design/methodology/approach This paper includes an interview with ChatGPT on its potential impact on academia and libraries. The interview discusses the benefits of ChatGPT such as improving search and discovery, reference and information services; cataloging and metadata generation; and content creation, as well as the ethical considerations that need to be taken into account, such as privacy and bias. Findings ChatGPT has considerable power to advance academia and librarianship in both anxiety-provoking and exciting new ways. However, it is important to consider how to use this technology responsibly and ethically, and to uncover how we, as professionals, can work alongside this technology to improve our work, rather than to abuse it or allow it to abuse us in the race to create new scholarly knowledge and educate future professionals. Originality/value This paper discusses the history and technology of GPT, including its generative pretrained transformer model, its ability to perform a wide range of language-based tasks and how ChatGPT uses this technology to function as a sophisticated chatbot.

Questions: Why are we interested in the question of MSMEs and cyber readiness? Linking it to the SME Programme at ITU (Rahul) and WTO MSME Informal Working Group (Kathryn) Evolution of the digital economy and how digital trade has evolved over the past 10-20 years (literature review, see UNCTAD and OECD) What is “digital” (review of the many definitions from UN Agencies/OECD etc. and what our papers' focus will be)? What is cyber readiness for MSMEs in this context? (Academic Researcher + Digital SMEs perspectives) Theoretical conceptualisation of Cyber readiness as a formative or reflective latent variable. (to be decided upon literature review) Is cyber readiness the same as business digitalization; how do these concepts differ from digital transformation, digitisation etc.? How do others define it? What definition will we use (it might be a function of what data are available) What cyber readiness is required for international trade participation? What do MNCs require, and what is needed, in terms of cyber readiness in order for MSMEs to participate in GVCs? MSME participation in digital trade and the need for cyber readiness (e.g., cyber readiness can be a determinant of MSME participation in trade (various presentations received on cyber readiness to WTO MSME Group WTO | Informal Working Group on Micro, Small and Medium-sized Enterprises (MSMEs)), either as part of domestic value chains, to participate in e-commerce where digital platforms can facilitate international transactions by firms of any size, or to participate in digital trade including digital services that is an area where MSMEs can excel (for example with born global firms)). Case studies Finalise list of questions to be asked directly to the MSMEs for Questions can be floated via 1-1 interview sessions (recorded) or Microsoft Forms. Data What is a MSME for this paper (most likely number of employees, use OECD definition micro are less than 10 employees, small are 10-49, medium are 50-249, and large are greater than 250 employees) Most likely goods trade from World Bank data with firm size characteristics (answering the question as to whether "cyber ready" MSMEs, or MSMEs in economies that are more cyber ready; trade more. Modelling ITU stats for a digitalization indicator (country and firm levels) World Bank (other?) stats for trade Policy Implications Conclusion

Artificial intelligence (AI) solutions are increasingly being used by organizations to select candidates when hiring. The recruitment of highly qualified personnel is an essential task for companies. Selecting the right personnel provides a competitive advantage that determines whether a company has the necessary knowledge, skills, and abilities to succeed in the highly competitive business environment. Especially in times of demographic change and the “War for Talent” (Chambers et al., 1998), companies see the potential of utilizing AI (Acikgoz et al., 2020). On the one hand, this has economic reasons, as AI allows for the fast processing of a large number of applications, which makes applicant selection processes faster and more efficient (Alsever, 2017). On the other hand, AIs ideally contribute to more objectivity and consistency as they process each application in the same way, potentially reducing human-bias in terms of unconscious biases and stereotypes that influence human behavior and decisions (Lacroux & Martin-Lacroux, 2022). At the same time, the use of algorithms also carries the risk of potential discrimination on a large scale, as they can also be vulnerable to biases in terms of gender and ethnicity, for example, if they rely on inaccurate or biased input and training data (Kim, 2017). This can be detrimental to applicants as well as companies. However, in light of the advancing digitization and the shortage of skilled labor, the adoption of AI in recruiting will continue to gain relevance in the coming years (Ahmed, 2018). Employer reputation and branding play an important role in attracting applicants, as the impression gained during the recruitment and selection process influences whether those who initially applied will stay in the applicant pool and ultimately accept a job offer (Acikgoz, 2019). Therefore, considering the applicant's perspective and ensuring that they have a positive opinion of the recruitment process is important for the success of recruitment efforts. Not taking into consideration how potential applicants perceive the use of AI in the applicant selection process can negatively impact the employer’s image, as applicants might, for instance, perceive the use of AI to evaluate humans as demeaning and dehumanizing (Lee, 2018). This attitude of general distrust towards algorithms refers to the phenomenon of algorithm aversion. Research has shown that trust in algorithms remains low, even though many algorithms are superior to humans in certain tasks, because they are more reliable and accurate in most cases (Burton et al., 2020; Jussupow et al., 2020). In addition, several studies have shown that individuals give more weight to human as compared to AI input (Önkal et al., 2009; Promberger & Baron, 2006). A study by Lee (2018) showed that many lay people believe that algorithms are unable to identify good candidates, because they lack the human judgment and intuition that are necessary, in their eyes, for subjective decisions such as hiring employees. This suggests that, overall, trust in AI systems is lower when it comes to assessing people, as it is often the case in Human Resources (HR) (Lacroux & Martin-Lacroux, 2022). The general distrust in algorithms can inhibit the adoption and efficacy of technologies, as trust is a crucial factor for technology acceptance, making it important to establish the right level of trust (Lee, 2018; Lee & See, 2004). Although the phenomenon of algorithm aversion is well established, there are relatively few studies that examine which variables contribute to increasing applicants' trust in algorithmic selection systems and thus decreasing algorithm aversion. Therefore, further research is required for the use of AI and the reduction of algorithm aversion in the HR field. This study examines two factors in terms of their potential influence on trust in AI systems: Personal information inquiries and feedback opportunities. In a paper by Lee (2018), it was suggested for future research to investigate the impact of interaction on the perception of AI decisions. A similar implication for further research was given in an empirical study by Dahm and Dregger (2019), who suggested communicating the score determined by an AI to applicants to increase trust in the AI system. The type of interaction we therefore consider as especially relevant is giving feedback. In this regard, feedback on the applicant’s suitability for a job in the selection process conducted by an AI might impact the applicant’s trust in the AI-based selection system. Yarger et al. (2020) stated in their paper that a growing number of companies are using AI to remove personally identifying information such as name, gender, age, ethnic and racial background as known sources of human and unconscious biases from the hiring process to provide equal opportunity. Blind hiring is a practice increasingly used by organizations to consider applicants primarily on the basis of their skills. This reduces bias and can increase the trustworthiness of AI (Yarger et al., 2020). Thus, we will additionally assess how inquiring versus not inquiring about personal information during the AI-based selection process affects applicants’ trust in such AI-based selection processes. The aim of this study is therefore to investigate how the selected variables, personal information inquiries and feedback opportunities, affect applicants’ trust in AI systems in the context of hiring processes.

The challenges introduced by AI for the EU anti-discrimination legal framework have been a widely discussed topic among the doctrine. In the light of the 20th anniversary of the EU Charter of Fundamental Rights, the Commission released a regulatory proposal to tackle AI. This paper seeks to determine whether the proposal successfully addresses the existent pitfalls of the EU framework. First, this paper explores the functioning of AI systems that employ machine learning techniques and determines how discrimination takes place. Second, the article examines intellectual property rights as one of the main barriers for accountability and redressal of violations committed by an AI system. Third, the state of the discussion concerning the pitfalls of the existent EU approach towards non-discrimination is addressed. The available academic literature suggests that discriminatory outputs produced by an AI will amount to indirect discrimination in most scenarios. In this sense, cases of indirect proxy discrimination will likely pass the proportionality test, therefore justifying the discriminatory output. The last section of this article studies the Commission’s regulatory proposal. Although the document seems to effectively tackle discrimination caused by biased training data sets, this paper concludes that intellectual property rights and proxy discrimination still constitute significant barriers for the enforcement of anti-discrimination law.

This white paper is the result of a research project by Hogeschool Utrecht, Floryn, Researchable, and De Volksbank in the period November 2021-November 2022. The research project was a KIEM project1 granted by the Taskforce for Applied Research SIA. The goal of the research project was to identify the aspects that play a role in the implementation of the explainability of artificial intelligence (AI) systems in the Dutch financial sector. In this white paper, we present a checklist of the aspects that we derived from this research. The checklist contains checkpoints and related questions that need consideration to make explainability-related choices in different stages of the AI lifecycle. The goal of the checklist is to give designers and developers of AI systems a tool to ensure the AI system will give proper and meaningful explanations to each stakeholder.

This white paper is the result of a research project by Hogeschool Utrecht, Floryn, Researchable, and De Volksbank in the period November 2021-November 2022. The research project was a KIEM project1 granted by the Taskforce for Applied Research SIA. The goal of the research project was to identify the aspects that play a role in the implementation of the explainability of artificial intelligence (AI) systems in the Dutch financial sector. In this white paper, we present a checklist of the aspects that we derived from this research. The checklist contains checkpoints and related questions that need consideration to make explainability-related choices in different stages of the AI lifecycle. The goal of the checklist is to give designers and developers of AI systems a tool to ensure the AI system will give proper and meaningful explanations to each stakeholder.

In recent decades, epidemic and pandemic illnesses have grown prevalent and are a regular source of concern throughout the world. The extent to which the globe has been affected by the COVID-19 epidemic is well documented. Smart technology is now widely used in medical applications, with the automated detection of status and feelings becoming a significant study area. As a result, a variety of studies have begun to focus on the automated detection of symptoms in individuals infected with a pandemic or epidemic disease by studying their body language. The recognition and interpretation of arm and leg motions, facial recognition, and body postures is still a developing field, and there is a dearth of comprehensive studies that might aid in illness diagnosis utilizing artificial intelligence techniques and technologies. This literature review is a meta review of past papers that utilized AI for body language classification through full-body tracking or facial expressions detection for various tasks such as fall detection and COVID-19 detection, it looks at different methods proposed by each paper, their significance and their results.

A current bottleneck that prevents Machine Learning (ML) from being successful outside of a few restricted fields such as chess playing and highway driving is its impairment in ap- propriately using context to infer the meaning of what it is observing. This paper describes techniques to allow ML systems to derive meaning from context, derived from how the human cortex works. In particular, this paper shows how the multiplication a horizontal vector representing a Sparse Distributed Representation (SDR) of patterns in sensory data by a vertical vector representing a SDR of patterns in context data followed by a pattern recognition operation on the result- ing matrix results in the integration of relevant context and in the output of data containing meaning.

Context The AICE Project Dataset is a dataset of small intestine capsule endoscopy (CE) images taken by PillCam™ SB 3 (Medtronic, Minneapolis, MN, USA), which we use in the paper (Practical Artificial Intelligence Model and Data Repository of Small intestine Capsule Endoscopy: The AICE Project ; Paper is in submission.). . The Ethics Review Committee of Kyushu University has approved this study regarding the collection and analysis of images for AI modeling by collecting images from stored CE videos and the publication of the analysis results and images in an appropriate form under the rules of Scientific Data ("Approval No. 2021-213"). This dataset contains 18,481 images extracted from 523 small bowel capsule endoscopy videos. We annotated 12,3320 images with 23,033 disease lesions and combined them with 6,161 normal mucosa images. The annotations are set in YOLO format. Although automated or assisted reading techniques for small bowel CE are eagerly awaited, the AI model alone has not been able to pick up multiple types of clinically necessary lesions from CE images to the same level of expertise. The main reason for this is that small intestine have certain amount of images that are difficult to annotate and set, and appropriate data sets have not been constructed. In the above paper, we tested an object detection model using YOLO ver5. The annotations are our own creations, and we believe that better ways of setting annotations should be further researched. We hope that this dataset will be helpful for future small bowel CE object detection. ### License We have presented the dataset of the AICE project at OSF and Kaggle(https://www.kaggle.com/), the world’s largest data science online community. Our data are licensed under a Creative Commons Attribution 4.0 International (CC BY 4.0) License. The material is free to copy and redistribute in any medium or format and can be remixed, transformed, and built upon for any purpose if appropriate credit is given. "Credit" Practical Artificial Intelligence Model and Data Repository of Small intestine Capsule Endoscopy: The AICE Project Yokote Akihito, Umeno Junji, Kawasaki Keisuke, Fujioka Shin, Fuyuno Yuta, Matsuno Yuichi, Yoshida Yuichiro, Imazu Noriyuki, Moriyama Tomohiro, Kitazono Takanori, and Torisu Takehiro ### Content The main content of The AICE Project Dataset includes image data and annotation data. 18,481 images and annotation data in YOLO format are available. The annotations are written in the txt data whose filenames match the image data. There are also empty txt data for images without annotations. ### Acknowledgements We want to thank Department of Medicine and Clinical Science, Kyushu University, for their cooperation in data collection. We also thank Ultralytics for making YOLO ver5 available. ### Inspiration We would be grateful if you could consider setting up better annotation and colleting small intestine CE images. We hope that many more facilities will collect CE images in the future, and datasets will become larger.

Technologies can have profound effects on social moral systems. Is there any way to systematically investigate and anticipate these potential effects? This paper aims to contribute to this emerging field on inquiry through a case study method. It focuses on two core human values—truth and trust—describes their structural properties and conceptualisations, and then considers various mechanisms through which technology is changing and can change our perspective on those values. In brief, the paper argues that technology is transforming these values by changing the costs/benefits of accessing them; allowing us to substitute those values for other, closely-related ones; increasing their perceived scarcity/abundance; and disrupting traditional value-gatekeepers. This has implications for how we study other, technologically-mediated, value changes.

Artificial Intelligence (AI) is not new, but recent years have seen a growing concern about the technology’s political, economic and social impact, including debates about its governance. This paper describes how an analysis of the technology’s governance should build on the understanding of AI as the creation of artificial agents, and that the challenge that governance seeks to address is best understood as one of material agency. It describes how relevant rules can be systematically analyzed across different applications by considering the fundamental properties of artificial agents. The paper concludes by describing how the framework can be applied for further governance studies, and as a means to bridge insights across social science and technical perspectives on AI.

Erfaringsbaserede kollaborative læringsformer (ECL), eksempelvis problembaseret læring og refleksiv praksislæring, bidrager til udvikling af 21. århundredes kompetencer, som er centrale for udvikling af praksis og erhverv. Traditionelt har disse læringsformer ofte været fysisk situerede, men i de seneste år, og især under nedlukninger i forbindelse med covid-19, har de været omlagt til fuldt digitale eller blendede og hybride former med henblik på at styrke fleksibilitet,tilgængelighed til og anvendelse af læring i uddannelse og erhverv. Transformation af læring til digitale platforme er dog ofte kritiseret for ikke at opnå en passende balance mellem hensyn til tekniske begrænsninger og læringsteoretiske forudsætninger og værdier samt have fokus på de dilemmaer, der kan opstå i forsøget på at oversætte denne type af læring til digitale forløb på læringsplatforme. Denne artikel præsenterer med udgangspunkt i forsknings- og udviklingsprojektet ‘Unified platform for the Future of Learning and Development’ (UnFoLD) en visionsbaseret designmetodik og fokuseret designproces som en måde, hvorpå dilemmaer, deropstår i den digitale translation og transformation af ECL, kan identificeres, kommunikeres og løses. Med afsæt i denne metode diskuterer artiklen to centrale dilemmaer, som opstår, når de fundamentale læringsprincipper i ECL skal omsættes til funktioner og algoritmer i en digital læringsplatforms arkitektur, samt metodens potentiale og mulige konsekvenser for feltet. Experientialcollaborative learning (ECL) such as problem-based learningandreflectivepractice-based learningcontribute to the development of 21stcentury competencies central to the advancement of practice and profession. Traditionally, these forms of learning have often been physically situated, but inrecent years, and particularlyduringCovid-19 lockdowns, transitionedto fully digital or blendedor hybrid formatsin order to enhance flexibility, accessibility and application in practice.The transformation of learning to digital platforms is often criticized forfailing to strike an appropriate balancebetweenconsiderationsoftechnological constraintsandthe assumptions and values of learning theory, as well as a focus on the dilemmas that arise with the attempt to translate these types of learning to digital platforms.Drawing on the research and development project ‘Unified platform forthe Future ofLearning and Development’ (UnFoLD), this paper presentsan envisioning design methodology and focused design processas a way to identify, communicate and resolvedilemmas that may arise in the digital translation and transformation of ECL.The paper discusses two key dilemmas identified using the method arising from the transformation of fundamental ECL principlesto functionality and algorithms to the architectureof a digital learning platformanddiscusses potentialsand implications for the field.

More than ever before, AI is now an area of national strategic importance. This has become quite evident with the proliferation of national AI strategies since the first was launched in Canada in 2017. There is now an ever-growing body of national AI strategies especially in countries situated in the Global South. AI is seen as a key driver of economic development and the strategies describe how countries plan to exploit AI technologies to achieve national development goals. However, AI technologies also generate problematic and unintended consequences, and the national strategies often describe governance mechanisms for mitigating such issues. As the national development goals of many countries also align with the UN SDGs, this paper explores the relationship between responsible governance of AI, the attainment of the UN SDGs and the implications for African countries. The paper shows that there is a clear link between the development of AI and the attainment of the SDGs. Also, based on an analysis of two AI policy tracking repositories - the OECD AI Policy Observatory and Oxford AI Readiness Index – this paper shows how African countries have lagged behind countries in the Global South in terms of the development of governance structures for AI. This has far-reaching implications for the attainment of the SGDs and the paper provides recommendations in this area

SOCIOTECHNICAL SYNTHESIS As online tools become increasingly prevalent in higher education environments, there is a need for easy-to-use web tools that can adopt the newest technologies in a responsible way. To that end, the technical report details the development and deployment of a new online course tool called Satori for CS classes at the University of Virginia (UVA). The new tool was built in order to allow for more efficient queueing of students in office hours which has been plagued with long wait times; additionally, the tool is built using modern tools that ensure that it can easily be enhanced in the future. The STS research paper is concerned with examining data bias in Artificial Intelligence (AI) systems through the sociotechnical lens of Actor-Network-Theory; an approach that was adapted from a similar examination by STS scholars John Law and Michael Callon. Currently, AI systems are rapidly being adopted into educational contexts without a thorough examination of what the consequences of a biased AI would be in higher education. Additionally, both projects are connected through the choice to examine AI data bias within the context of higher education. As AI systems are inevitably added to Satori, it is imperative that proper consideration is given to how to eliminate data bias within the systems to limit negative consequences. The technical research project is concerned with the replacement of an old online course tool for CS 2150 at UVA for a new one built from the ground up with modern web development tools and techniques. As the shortcomings of the old system were identified, the immediate need for a new tool became apparent. The core functionality of the old system was quickly reproduced within the new system and was immediately available to students with additions added after. Development of the system was done under the supervision of professor Aaron Bloomfield, using the Python based Django web framework. Satori’s success was determined from anectodical feedback from CS 2150 teaching assistants. The feedback has been positive with helpful suggestions for the future of the project. The results and the lessons learned from development have led to the conclusion that although Satori is a significant first step in creating a more helpful online tool for CS classes at UVA, more work must be done to spread the tool to additional courses. The STS research aims to answer the question of: “What considerations should be made to collect unbiased data for AI systems in U.S. universities?”. The paper posits that there is a concerning lack of attention given to the underlying data and processes of AI systems. This was explored through a thorough examination of newspaper and STS journal articles on data bias in AI systems that examine both the underlying causes of the data bias as well as what steps have been taken to address this issue. The primary motivation for the STS research is the trend in AI development where systems are granted autonomy from its creators, alleviating them from the negative consequences of their system’s behavior. To address this, the common issue of data bias within AI systems was examined using Actor-Network-Theory to expose the socio-technical nature of these systems, with a heavy emphasis on responsibility and transparency. The research suggests that in addition to developers taking responsibility for their systems, the European Union’s General Data Protection Regulation should be expanded on and adopted by AI regulators in the future The technical research addresses a current problem facing the CS students at UVA and the STS research looks towards the future of the project to ensure the system remains equitable as it is expanded upon. The STS research heavily emphasizes the sociotechnical nature of AI systems, ensuring that responsibility for eliminating bias and dealing with consequences is placed in the hands of the developers.

Automation of Radio Access Network (RAN) operation is a fundamental feature to manage sustainable and efficient Beyond Fifth-generation wireless (5G) networks, in the context of the Next Generation Self-Organizing Network (NG-SON) vision. Machine Learning (ML) is already identified as the key ingredient of this vision, with new standardized and open architectures, like Open-RAN (O-RAN), taking momentum. In this paper, we propose models based on single-task and Multi-Task Learning (MTL) paradigms to address two RAN use cases, handover management and initial Modulation and Coding Scheme (MCS) selection. Traditional handover schemes have the drawback of taking into account the quality of the signals from the serving, and the target cell, before the handover. Also, initial MCS at the start of the session and after a handover usually is handled conservatively. The proposed ML solutions allow to address these drawbacks by 1) considering the expected Quality of Experience (QoE) resulting from the decision of a target cell to handover, as the driving principle of the handover decision and 2) using the experience extracted from network data to make smarter initial MCS allocations. In this line, we implement a realistic cellular simulation scenario by incorporating coverage holes to build an extensive database to train and test the proposed models. The results show that the ML-based models outperform the 3rd Generation Partnership Project (3GPP) standardized handover and initial MCS selection approaches by improving the QoE of users resulting from a handover and the throughput obtained upon establishing a new connection with a network. Besides that, using the obtained results, this paper extensively discusses the merits of leveraging the MTL model to address different, but related multiple RAN functions because it allows reusing a common learning architecture for multiple RAN use cases, which provides significant implementation advantages., This work was supported in part by under Grant PID2020-113832RB-C22 (ORIGIN)/AEI/10.13039/501100011033, and in part by Huawei Technologies, Sweden AB.

The research on chatbots has gained momentum over the past few years. Academics and practitioners investigate how these tools for communication with customers or internal team can be improved in terms of their performance, acceptance, and deployment. Although there is a plethora of recent studies available, not all of them deal with the digital business transformation implications of chatbots. The main aim of the research presented in this paper was to conduct a systematic literature review of high-quality journal research papers in order to summarise the current state of research on chatbots, identify their role in digital business transformation and suggest the areas warranting further attention. 74 papers were included in the research. Topical (focus and applications), methodological (methods used, sample size, sample type,and countries studied) and bibliometric (publication outlet, citations, and Altmetric Attention Score) aspects are evaluated and described. Scholars and practitioners can use the results to identify topics, areas, and applications that are intensely discussed in the literature and require further attention, select a methodology for their research that is well established in the field or is emerging, identify the most influential publications not to be missed in their research or identify publication outlets for publishing their research on chatbots. Refereed/Peer-reviewed

Artificial Intelligence (AI) agents are predicted to infiltrate most industries within the next decade, creating a personal, industrial, and social shift towards the new technology. As a result, there has been a surge of interest and research towards user acceptance of AI technology in recent years. However, the existing research appears dispersed and lacks systematic synthesis, limiting our understanding of user acceptance of AI technologies. To address this gap in the literature, we conducted a systematic review following the Preferred Reporting Items for Systematic Reviews and meta-Analysis guidelines using five databases: EBSCO host, Embase, Inspec (Engineering Village host), Scopus, and Web of Science. Papers were required to focus on both user acceptance and AI technology. Acceptance was defined as the behavioural intention or willingness to use, buy, or try a good or service. A total of 7912 articles were identified in the database search. Sixty articles were included in the review. Most studies (n = 31) did not define AI in their papers, and 38 studies did not define AI for their participants. The extended Technology Acceptance Model (TAM) was the most frequently used theory to assess user acceptance of AI technologies. Perceived usefulness, performance expectancy, attitudes, trust, and effort expectancy significantly and positively predicted behavioural intention, willingness, and use behaviour of AI across multiple industries. However, in some cultural scenarios, it appears that the need for human contact cannot be replicated or replaced by AI, no matter the perceived usefulness or perceived ease of use. Given that most of the methodological approaches present in the literature have relied on self-reported data, further research using naturalistic methods is needed to validate the theoretical model/s that best predict the adoption of AI technologies.

open access article Multitudinous health data are continually being produced as our activities, including medicine, evolve into the digital age where data plays a decisive role. Challenges come along as well, concerning the collection, secure storage, verification and secure access to the continuously growing data at such a broad scale before valuable information can be extracted to contribute to medical advancement nowadays. With the decentralization feature, huge successes of blockchain technology in overcoming similar challenges in the finance and cryptocurrency sector brought us the confidence to investigate and reveal its immeasurable potential for the health sector, specifically in dentistry. Dentistry is an important area of healthcare, but there is relatively little research focusing on its interactions with blockchain technology. Given the limited amount of existing research on this specific subject, this paper focuses on blockchain in dentistry and aims to provide a conceptual framework for the possible applications of blockchain in dentistry. The framework is organised by different areas of dentistry operations so that dental professionals can easily refer to and identify areas of interest. This contributes to increasing the awareness of blockchain technology among dental professionals and promoting blockchain-empowered revolutions in dentistry. This paper also discusses how blockchain fits alongside other emerging technologies, the challenges that have to be overcome to maximise the functionality and efficiency of this technology, as well as future research directions concerning blockchain implementations in the dental industry.

This paper discusses the design of the legal and regulatory framework for using artificial intelligence (AI) in the financial services markets to enhance access to finance (financial inclusion). The author argues that the development of AI should continue to adhere to the regulatory objectives of market safety, consumer protection, and market integrity. However, to ensure equality and fairness, access to finance should be made a clear policy choice. In the first part, the author discusses how AI can lead to systemic risks and market manipulation on trading platforms. For example, by examining the use of algorithms for trading on the capital market, the author discerns the regulatory objectives and the possible methods of regulation for peer-to-peer platforms. In the second part, the author discusses how the use of AI to provide consumers with investment advice, such as financial advice provided from robo-advisers, can close the investment advisory gap and provide consumers with access to finance. The current regime does not provide adequate protection to financial consumers in this regard. In the third part, the author discusses how AI can be used as a form of RegTech to streamline compliance processes, thereby increasing competition in financial markets and providing a benefit to consumers. However, this use may be in conflict with privacy, data protection, and ethical concerns. The author makes policy recommendations and suggests some directions for governance in the use of AI in financial services to enhance access to finance. The findings of this paper are relevant to research on the future governance of AI in financial services, public policy innovation, and urban development.

It has now become a realistic prospect for smart care to be provided at home for those living with long-term conditions such as dementia. In the contemporary smart care scenario, homes are fitted with an array of sensors for remote monitoring providing data that feed into intelligent systems developed to highlight concerning patterns of behaviour or physiological measurements and to alert healthcare professionals to the need for action. This paper explores some ethical issues that may arise within such smart care systems, focusing on the extent to which ethical issues can be addressed at the system design stage. Artificial intelligence has been widely portrayed as an ethically risky technology, posing challenges for privacy and human autonomy and with the potential to introduce and exacerbate bias and inequality. While broad principles for ethical artificial intelligence have become established, the mechanisms for governing ethical artificial intelligence are still evolving. In healthcare settings the implementation of smart technologies falls within the existing frameworks for ethical review and governance. Feeding into this ethical review there are many practical steps that designers can take to build ethical considerations into the technology. After exploring the pre-emptive steps that can be taken in design and governance to provide for an ethical smart care system, the paper reviews the potential for further ethical challenges to arise within the everyday implementation of smart care systems in the context of dementia, despite the best efforts of all concerned to pre-empt them. The paper concludes with an exploration of the dilemmas that may thus face healthcare professionals involved in implementing this kind of smart care and with a call for further research to explore ethical dimensions of smart care both in terms of general principles and lived experience.

The topic of this study titled ‘Artificial Intelligence (AI), the media and security challenges in Nigeria’ is a theoretical paper. To carry out this study phenomenological method in qualitative research was used. The work was guided by three (3) objectives, one of which is to ascertain the role of the media in awareness creation on artificial intelligence. Two (2) theories namely: agenda setting theory and value change theory were used. The paper delved into - application of AI in emerging terrorist sophistication, role of the media in information dissemination, challenges of deploying AI to curb the excesses of herdsmen and Boko Haram insurgency in Nigeria. Four (4) recommendations were proffered, one of which was that the media should continue to create awareness on the use of AI in combating farmers/herders conflicts and Boko Haram insurgency in Nigeria. Intelligence artificielle (IA), les défis des médias et de la sécurité au Nigeria est une étude basée sur la méthode phénoménologique en recherche qualitative. Il s'agit de déterminer le rôle des médias dans la sensibilisation à l'intelligence artificielle. Deux théories, à savoir la théorie de l'agenda et la théorie du changement de valeur, ont été mobilisées. Il s'agit à la fois d'examiner le rôle des médias dans la diffusion de l'information et les défis du déploiement de l'IA dans la lutte contre Boko Haram au Nigeria.

Graphic abstract The recent COVID-19 pandemic, which broke at the end of the year 2019 in Wuhan, China, has infected more than 98.52 million people by today (January 23, 2021) with over 2.11 million deaths across the globe. To combat the growing pandemic on urgent basis, there is need to design effective solutions using new techniques that could exploit recent technology, such as machine learning, deep learning, big data, artificial intelligence, Internet of Things, for identification and tracking of COVID-19 cases in near real time. These technologies have offered inexpensive and rapid solution for proper screening, analyzing, prediction and tracking of COVID-19 positive cases. In this paper, a detailed review of the role of AI as a decisive tool for prognosis, analyze, and tracking the COVID-19 cases is performed. We searched various databases including Google Scholar, IEEE Library, Scopus and Web of Science using a combination of different keywords consisting of COVID-19 and AI. We have identified various applications, where AI can help healthcare practitioners in the process of identification and monitoring of COVID-19 cases. A compact summary of the corona virus cases are first highlighted, followed by the application of AI. Finally, we conclude the paper by highlighting new research directions and discuss the research challenges. Even though scientists and researchers have gathered and exchanged sufficient knowledge over last couple of months, but this structured review also examined technological perspectives while encompassing the medical aspect to help the healthcare practitioners, policymakers, decision makers, policymakers, AI scientists and virologists to quell this infectious COVID-19 pandemic outbreak.

Robotics is a machine which is supported by the Artificial Intelligence and Expert Systems. This is basically run with the programmable computer which is run and does the performance and activities automatically. The robotics systems can be done with the integrated systems built inside or it may be done by the external controlling mechanism. It is not mandatory that a robot always looks like a human being. It could be of any form and designing or shape, however, the things in robotics is that it is operated automatically. Robots are applicable in diverse areas and sectors and among these important are include healthcare, business, agriculture, transportation, manufacturing etc. As far as agriculture is concerned, Robotics is very much associated with the various pre and postproduction of agriculture and similar sectors. Initially in harvesting only the robotics were used but currently, there are more concerns where robotics can be applicable viz. drone for the weed controlling, plant seeding, environmental assessing and monitoring, soil mapping and analysis, etc. Initially, only basic Information Technology tools were common in agricultural practices but in recent past, many technologies are using in agriculture and among these important are Cloud Computing, Big Data, HCI, Usability Engineering, Robotics and AI, etc. According to the market analysis companies the growth of the robotics is very increasing and will touch the high very soon. This paper talks about the basics of robotics including a special focus on agriculture. Paper also highlighted the areas of applications with the concern of issues and challenges in brief.

Artificial Intelligence (AI) has been used in different research areas in aerospace to create an intelligent system. Especially, an unmanned aerial vehicle (UAV), known as a drone, can be controlled by AI methods such as deep reinforcement learning (DRL) in different purposes. Drones with DRL become more intelligent and eventually they can be fully autonomous. In this paper, DRL method supported by real time object detection model is proposed to detect and catch a drone. Additionally, the results are analyzed by comparing the time to catch the target drone in seconds between DRL method, human pilot and an algorithm which directs the drone towards the target position without using any AI method or navigation and guidance method. The main idea is to catch a drone in an environment as fast as possible without crashing any obstacles inside the environment. In DRL method, the agent is a quadcopter drone and it is rewarded in each time step by the environment provided by Airsim flight simulator. Drone is trained to catch the target drone by using DRL model which is based on deep Q-Network algorithm. After training, the tests have been made by the agent drone with DRL model and human pilots to catch stationary and non-stationary target drone. The training and test results show that the agent drone learns to catch target drone which can be a stationary and a non-stationary. In addition. the agent avoids crashing any obstacles in the environment with a minimum success rate of 94%. Also, DRL model performance is compared with the human pilot performances and the agent with DRL model shows better time to catch the target drone. Human pilots struggle to control the drone by using remote controller when catching the target in simulation. However, the agent with DRL model is rarely missing the target when trying to catch the target. This work was funded partially by the AGAUR under grant 2020PANDE00141, the Ministry of Science and Innovation of Spain under grant PID2020-116377RB-C21 and the SESAR Joint Undertaking (JU) project CORUS-XUAM, under grant SESAR-VLD2 101017682. The JU receives support from the European Union’s Horizon 2020 research and innovation programme and the SESAR JU members other than the Union. Special thanks to Dr. Pablo Royo Chic for his assistance to our paper by piloting a drone to catch the target drone.

Nowadays, one of the important aspects of research on call/contact centre (CC) systems is how to automate their operations. Process automation is influenced by the continuous development in the implementation of virtual assistants. The effectiveness of virtual assistants depends on numerous factors. One of the most important is correctly recognizing the intent of clients conversing with the machine. Recognizing intentions is not an easy process, as often the client’s actual intentions can only be correctly identified after considering the client’s emotional state. When it comes to human–machine communication, the ability of a virtual assistant to recognize the client’s emotional state would greatly improve its effectiveness. This paper proposes a new method for recognizing interlocutors’ emotions dedicated directly to contact centre systems. The developed method provides opportunities to determine emotional states in text and voice channels. It provides opportunities to explore both the client’s and the agent’s emotional states. Information about agents’ emotions can be used to build their behavioural profiles, which is also applicable in contact centres. In addition, the paper explored the possibility of emotion assessment based on automatic transcriptions of recordings, which also positively affected emotion recognition performance in the voice channel. The research used actual conversations that took place during the operation of a large, commercial contact centre. The proposed solution makes it possible to recognize the emotions of customers contacting the hotline and agents handling these calls. Using this information in practical applications can increase the efficiency of agents’ work, efficiency of bots used in CC and increase customer satisfaction.

Recent progress in artificial intelligence (AI) raises a wide array of ethical and societal concerns. Accordingly, an appropriate policy approach is needed today. While there has been a wave of scholarship in this field, the research community at times appears divided amongst those who emphasize ‘near-term’ concerns, and those focusing on ‘long-term’ concerns and corresponding policy measures. In this paper, we seek to map and critically examine this alleged ‘gulf’, with a view to understanding the practical space for inter-community collaboration on AI policy. This culminates in a proposal to make use of the legal notion of an ‘incompletely theorized agreement’. We propose that on certain issue areas, scholars working with near-term and long-term perspectives can converge and cooperate on selected mutually beneficial AI policy projects all the while maintaining divergent perspectives., During most of the writing process of this paper, Matthijs Maas was funded under a PhD grant by the University of Copenhagen Faculty of Law. During the fnal stages of re-editing, he was a postdoctoral Research Associate at the Centre for the Study of Existential Risk, University of Cambridge

We present here the first machine-generated law review article. Our self-interest motivates us to believe that knowledge workers who write complex articles drawing upon years of research and effort are safe from AI developments. However, how reasonable is it to persist in this belief given recent advances in AI research? With that topic in mind, we caused GPT-3, a state-of-the-art AI, to generate a paper that explains “why humans will always be better lawyers, drivers, CEOs, presidents, and law professors than artificial intelligence and robots can ever hope to be.” The resulting paper, with no edits apart from giving it a title and bolding the headings generated by GPT-3, is reproduced below. It is imperfect in a humorous way. Ironically, it is publishable “as-is” only because it is machine-generated. Nevertheless, the resulting paper is good enough to give us some pause for thought. Although GPT-3 is not up to the task of replacing law review authors currently, we are far less confident that GPT-5 or GPT-100 might not be up to the task in future.

This paper presents the architecture and results achieved by the EO-ALERT H2020 project. EO-ALERT proposes the definition and development of the next-generation Earth Observation (EO) data processing chain, based on a novel flight segment architecture that moves optimised key EO data processing elements from the ground segment to onboard the satellite, with the aim of delivering the EO products to the end user with very low latency (in almost real-time). This paper presents the EO-ALERT architecture, its performance and hardware. Performances are presented for two reference user scenarios; ship detection and extreme weather nowcasting/monitoring. The hardware testing results show that, when implemented using Commercial Off-The-Shelf (COTS) components and available communication links, the proposed architecture can deliver EO products and alerts to the end user with a latency lower than one-point-five minutes, for both SAR and Optical Very High Resolution (VHR) missions, demonstrating the viability of the EO-ALERT concept and architecture.

Large-scale fading models play an important role in estimating radio coverage, optimizing base station deployments and characterizing the radio environment to quantify the performance of wireless networks. In recent times, multi-frequency path loss models are attracting much interest due to their expected support for both sub-6 GHz and higher frequency bands in future wireless networks. Traditionally, linear multi-frequency path loss models like the ABG model have been considered, however such models lack accuracy. The path loss model based on a deep learning approach is an alternative method to traditional linear path loss models to overcome the time-consuming path loss parameters predictions based on the large dataset at new frequencies and new scenarios. In this paper, we proposed a feed-forward deep neural network (DNN) model to predict path loss of 13 different frequencies from 0.8 GHz to 70 GHz simultaneously in an urban and suburban environment in a non-line-of-sight (NLOS) scenario. We investigated a broad range of possible values for hyperparameters to search for the best set of ones to obtain the optimal architecture of the proposed DNN model. The results show that the proposed DNN-based path loss model improved mean square error (MSE) by about 6 dB and achieved higher prediction accuracy R2 compared to the multi-frequency ABG path loss model. The paper applies the XGBoost algorithm to evaluate the importance of the features for the proposed model and the related impact on the path loss prediction. In addition, the effect of hyperparameters, including activation function, number of hidden neurons in each layer, optimization algorithm, regularization factor, batch size, learning rate, and momentum, on the performance of the proposed model in terms of prediction error and prediction accuracy are also investigated.