Artificial Intelligence Community Research Articles

Predicting non-muscle invasive bladder cancer outcomes using artificial intelligence: a systematic review using APPRAISE-AI.

Accurate prediction of recurrence and progression in non-muscle invasive bladder cancer (NMIBC) is essential to inform management and eligibility for clinical trials. Despite substantial interest in developing artificial intelligence (AI) applications in NMIBC, their clinical readiness remains unclear. This systematic review aimed to critically appraise AI studies predicting NMIBC outcomes, and to identify common methodological and reporting pitfalls. MEDLINE, EMBASE, Web of Science, and Scopus were searched from inception to February 5th, 2024 for AI studies predicting NMIBC recurrence or progression. APPRAISE-AI was used to assess methodological and reporting quality of these studies. Performance between AI and non-AI approaches included within these studies were compared. A total of 15 studies (five on recurrence, four on progression, and six on both) were included. All studies were retrospective, with a median follow-up of 71 months (IQR 32-93) and median cohort size of 125 (IQR 93-309). Most studies were low quality, with only one classified as high quality. While AI models generally outperformed non-AI approaches with respect to accuracy, c-index, sensitivity, and specificity, this margin of benefit varied with study quality (median absolute performance difference was 10 for low, 22 for moderate, and 4 for high quality studies). Common pitfalls included dataset limitations, heterogeneous outcome definitions, methodological flaws, suboptimal model evaluation, and reproducibility issues. Recommendations to address these challenges are proposed. These findings emphasise the need for collaborative efforts between urological and AI communities paired with rigorous methodologies to develop higher quality models, enabling AI to reach its potential in enhancing NMIBC care.

Epistemic integration and social segregation of AI in neuroscience

In recent years, Artificial Intelligence (AI) shows a spectacular ability of insertion inside a variety of disciplines which use it for scientific advancements and which sometimes improve it for their conceptual and methodological needs. According to the transverse science framework originally conceived by Shinn and Joerges, AI can be seen as an instrument which is progressively acquiring a universal character through its diffusion across science. In this paper we address empirically one aspect of this diffusion, namely the penetration of AI into a specific field of research. Taking neuroscience as a case study, we conduct a scientometric analysis of the development of AI in this field. We especially study the temporal egocentric citation network around the articles included in this literature, their represented journals and their authors linked together by a temporal collaboration network. We find that AI is driving the constitution of a particular disciplinary ecosystem in neuroscience which is distinct from other subfields, and which is gathering atypical scientific profiles who are coming from neuroscience or outside it. Moreover we observe that this AI community in neuroscience is socially confined in a specific subspace of the neuroscience collaboration network, which also publishes in a small set of dedicated journals that are mostly active in AI research. According to these results, the diffusion of AI in a discipline such as neuroscience didn’t really challenge its disciplinary orientations but rather induced the constitution of a dedicated socio-cognitive environment inside this field.

A Scoping Literature Review of Artificial Intelligence in Epidemiology: Uses, Applications, Challenges and Future Trends

Artificial Intelligence (AI) has been applied to many human endeavors, and epidemiology is no exception. The AI community has recently seen a renewed interest in applying AI methods and approaches to epidemiological problems. However, a number of challenges are impeding the growth of the field. This work reviews the uses and applications of AI in epidemiology from 1994 to 2023. The following themes were uncovered: epidemic outbreak tracking and surveillance, Geo-location and visualization of epidemics data, Tele-Health, vaccine resistance and hesitancy sentiment analysis, diagnosis, predicting and monitoring recovery and mortality, and decision support systems. Disease detection received the most interest during the time under review. Furthermore, the following AI approaches were found to be used in epidemiology: prediction, geographic information systems (GIS), knowledge representation, analytics, sentiment analysis, contagion analysis, warning systems, and classification. Finally, the work makes the following findings: the absence of benchmark datasets for epidemiological purposes, the need to develop ethical guidelines to regulate the development of AI for epidemiology as this is a major issue impeding it’s growth, a concerted and continuous collaboration between AI and Epidemiology experts to grow the field, the need to develop explainable and privacy retaining AI methods for more secured and human understandable AI solutions.

Discrete preference games with logic-based agents: Formal framework, complexity, and islands of tractability

Analyzing and predicting the dynamics of opinion formation in the context of social environments are problems that attracted much attention in literature. While grounded in social psychology, these problems are nowadays popular within the artificial intelligence community, where opinion dynamics are often studied via game-theoretic models in which individuals/agents hold opinions taken from a fixed set of discrete alternatives, and where the goal is to find those configurations where the opinions expressed by the agents emerge as a kind of compromise between their innate opinions and the social pressure they receive from the environments. As a matter of facts, however, these studies are based on very high-level and sometimes simplistic formalizations of the social environments, where the mental state of each individual is typically encoded as a variable taking values from a Boolean domain. To overcome these limitations, the paper proposes a framework generalizing such discrete preference games by modeling the reasoning capabilities of agents in terms of weighted propositional logics. It is shown that the framework easily encodes different kinds of earlier approaches and fits more expressive scenarios populated by conformist and dissenter agents. Problems related to the existence and computation of stable configurations are studied, under different theoretical assumptions on the structural shape of the social interactions and on the class of logic formulas that are allowed. Remarkably, during its trip to identify some relevant tractability islands, the paper devises a novel technical machinery whose significance goes beyond the specific application to analyzing opinion formation and diffusion, since it significantly enlarges the class of Integer Linear Programs that were known to be tractable so far.

Mixed Fair Division: A Survey

The fair allocation of resources to agents is a fundamental problem in society and has received significant attention and rapid developments from the game theory and artificial intelligence communities in recent years. The majority of the fair division literature can be divided along at least two orthogonal directions: goods versus chores, and divisible versus indivisible resources. In this survey, besides describing the state of the art, we outline a number of interesting open questions in three mixed fair division settings: (i) indivisible goods and chores, (ii) divisible and indivisible goods (i.e., mixed goods), and (iii) fair division of indivisible goods with subsidy.

Use case cards: a use case reporting framework inspired by the European AI Act

Despite recent efforts by the Artificial Intelligence (AI) community to move towards standardised procedures for documenting models, methods, systems or datasets, there is currently no methodology focused on use cases aligned with the risk-based approach of the European AI Act (AI Act). In this paper, we propose a new framework for the documentation of use cases that we call use case cards, based on the use case modelling included in the Unified Markup Language (UML) standard. Unlike other documentation methodologies, we focus on the intended purpose and operational use of an AI system. It consists of two main parts: firstly, a UML-based template, tailored to allow implicitly assessing the risk level of the AI system and defining relevant requirements, and secondly, a supporting UML diagram designed to provide information about the system-user interactions and relationships. The proposed framework is the result of a co-design process involving a relevant team of EU policy experts and scientists. We have validated our proposal with 11 experts with different backgrounds and a reasonable knowledge of the AI Act as a prerequisite. We provide the 5 use case cards used in the co-design and validation process. Use case cards allows framing and contextualising use cases in an effective way, and we hope this methodology can be a useful tool for policy makers and providers for documenting use cases, assessing the risk level, adapting the different requirements and building a catalogue of existing usages of AI.

Extraction of object-action and object-state associations from Knowledge Graphs

Infusing autonomous artificial systems with knowledge about the physical world they inhabit is a critical and long-held aim for the Artificial Intelligence community. Training systems with relevant data is a typical approach; however, finding the data required is not always possible, especially when much of this knowledge is commonsense. In this paper, we present a comparison of topology-based and semantics-based methods for extracting information about object-action and object-state association relations from knowledge graphs, such as ConceptNet, WordNet, ATOMIC, YAGO, WebChild and DBpedia. Moreover, we propose a novel method for extracting information about object-action and object-state associations from knowledge graphs. Our method is composed of a set of techniques for locating, enriching, evaluating, cleaning and exposing knowledge from such resources, relying on semantic similarity methods. Some important aspects of our method are the flexibility in deciding how to deal with the noise that exists in the data, and the capability to determine the importance of a path through training, rather than through manual annotation.

Meta-research on reporting guidelines for artificial intelligence: are authors and reviewers encouraged enough in radiology, nuclear medicine, and medical imaging journals?

To determine how radiology, nuclear medicine, and medical imaging journals encourage and mandate the use of reporting guidelines for artificial intelligence (AI) in their author and reviewer instructions. The primary source of journal information and associated citation data used was the Journal Citation Reports (June 2023 release for 2022 citation data; Clarivate Analytics, UK). The first- and second-quartile journals indexed in the Science Citation Index Expanded and the Emerging Sources Citation Index were included. The author and reviewer instructions were evaluated by two independent readers, followed by an additional reader for consensus, with the assistance of automatic annotation. Encouragement and submission requirements were systematically analyzed. The reporting guidelines were grouped as AI-specific, related to modeling, and unrelated to modeling. Out of 102 journals, 98 were included in this study, and all of them had author instructions. Only five journals (5%) encouraged the authors to follow AI-specific reporting guidelines. Among these, three required a filled-out checklist. Reviewer instructions were found in 16 journals (16%), among which one journal (6%) encouraged the reviewers to follow AI-specific reporting guidelines without submission requirements. The proportions of author and reviewer encouragement for AI-specific reporting guidelines were statistically significantly lower compared with those for other types of guidelines (P < 0.05 for all). The findings indicate that AI-specific guidelines are not commonly encouraged and mandated (i.e., requiring a filled-out checklist) by these journals, compared with guidelines related to modeling and unrelated to modeling, leaving vast space for improvement. This meta-research study hopes to contribute to the awareness of the imaging community for AI reporting guidelines and ignite large-scale group efforts by all stakeholders, making AI research less wasteful. This meta-research highlights the need for improved encouragement of AI-specific guidelines in radiology, nuclear medicine, and medical imaging journals. This can potentially foster greater awareness among the AI community and motivate various stakeholders to collaborate to promote more efficient and responsible AI research reporting practices.

Exploring the drivers of AI-seeking intention among AI community canteen customers

PurposeThe purpose of the present research is to address the issue by conceptualizing artificial intelligence (AI) experience quality and its dimensions, and furthermore, to empirically test the relationships among AI experience quality, positive affective reactions, AI experience satisfaction and AI-seeking intention.Design/methodology/approachThe data were collected from an AI community canteen in Shanghai. They were also analyzed using exploratory and confirmatory factor analyses (EFA and CFA) and structural equation modeling (SEM).FindingsFour primary dimensions and 15 sub-dimensions of AI experience quality for community canteens were identified. The hypothesized paths between the higher-order constructs – AI experience quality, positive affective reactions, AI experience satisfaction and AI-seeking intention – were confirmed as well.Originality/valueThis is the first study to synthesize AI experience quality, positive affective reactions, AI experience satisfaction and AI-seeking intention in an AI restaurant setting.

Environmental Justice and Lessons Learned from COVID-19 Outcomes—Uncovering Hidden Patterns with Geometric Deep Learning and New NASA Satellite Data

Abstract Virtually all aspects of our societal functioning—from food security to energy supply to healthcare—depend on the dynamics of environmental factors. Nevertheless, the social dimensions of weather and climate are noticeably less explored by the artificial intelligence community. By harnessing the strength of geometric deep learning (GDL), we aim to investigate the pressing societal question the potential disproportional impacts of air quality on COVID-19 clinical severity. To quantify air pollution levels, here we use aerosol optical depth (AOD) records that measure the reduction of the sunlight due to atmospheric haze, dust, and smoke. We also introduce unique and not yet broadly available NASA satellite records (NASAdat) on AOD, temperature, and relative humidity and discuss the utility of these new data for biosurveillance and climate justice applications, with a specific focus on COVID-19 in the states of Texas and Pennsylvania. The results indicate, in general, that the poorer air quality tends to be associated with higher rates for clinical severity and, in the case of Texas, that this phenomenon particularly stands out in Texan counties characterized by higher socioeconomic vulnerability. This, in turn, raises a concern of environmental injustice in these socioeconomically disadvantaged communities. Furthermore, given that one of NASA’s recent long-term commitments is to address such inequitable burden of environmental harm by expanding the use of Earth science data such as NASAdat, this project is one of the first steps toward developing a new platform integrating NASA’s satellite observations with deep learning (DL) tools for social good. Significance Statement By leveraging the strengths of modern deep learning models, particularly, graph neural networks to describe complex spatiotemporal dependencies and by introducing new NASA satellite records, this study aims to investigate the problem of potential environmental injustice associated with COVID-19 clinical severity and caused by disproportional impacts of poor air quality on disadvantaged socioeconomic populations.

Bayesian Neural Networks for predicting the severity of symptoms: a case study

Childhood allergies are a problem that seems to be forgotten by the Artificial Intelligence community, even if they are affecting millions of children. In this paper we are interested in studying the prevalence of childhood allergies, some demographic stats, and to predict the severity of the most encountered allergy, asthma. For this we have used two publicly available datasets, one for Data Engineering and Exploratory Data Analysis, and the other for Bayesian Neural Networks.

The embeddings world and Artificial General Intelligence

From early days, a key and controversial question inside the artificial intelligence community was whether Artificial General Intelligence (AGI) is achievable. AGI is the ability of machines and computer programs to achieve human-level intelligence and do all tasks that a human being can. While there exist a number of systems in the literature claiming they realize AGI, several other researchers argue that it is impossible to achieve it.In this paper, we take a different view to the problem. First, we discuss that in order to realize AGI, along with building intelligent machines and programs, an intelligent world should also be constructed which is on the one hand, an accurate approximation of our world and on the other hand, a significant part of reasoning of intelligent machines is already embedded in this world. Then we discuss that AGI is not a product or algorithm, rather it is a continuous process which will become more and more mature over time (like human civilization and wisdom). Then, we argue that pre-trained embeddings play a key role in building this intelligent world and as a result, realizing AGI. We discuss how pre-trained embeddings facilitate achieving several characteristics of human-level intelligence, such as embodiment, common sense knowledge, unconscious knowledge and continuality of learning, by machines.

Special Issue NL4AI 2022: Workshop on natural language for artificial intelligence

The 2022 edition of NL4AI was co-located with the 21st International Conference of the Italian Association for Artificial Intelligence (AIxIA 2022) and took place on November 30th in Udine, Italy. The call for papers attracted 17 submissions by 52 different authors from Italy (44), the UK (2), Algeria (4), and Germany (2). After the review process, 13 of 17 papers were accepted for publication (acceptance rate 76.47%). In terms of topics, the contributions to the workshop span from pure NLP works to broader proposals bridging NLP with other AI applications. Among the accepted articles, we selected two that we considered the most relevant and inspiring for the Italian Natural Language Processing and Artificial Intelligence communities. The authors of these papers have been invited to extend their contribution to this volume, creating an exclusive venue to provide visibility to their egregious work.

Adaptive online mean-variance portfolio selection with transaction costs

Online portfolio selection is attracting increasing attention in both artificial intelligence and finance communities due to its efficiency and practicability in deriving optimal investment strategies in real investment activities where the market information is constantly renewed every second. The key issues in online portfolio selection include predicting the future returns of risky assets accurately given historical data and providing optimal investment strategies for investors in a short time. In the existing online portfolio selection studies, the historical return data of one risky asset is used to estimate its future return. In this paper, we incorporate the peer impact into the return prediction where the predicted return of one risky asset not only depends on its past return data but also the other risky assets in the financial market, which gives a more accurate prediction. An adaptive moving average method with peer impact (AOLPI) is proposed, in which the decaying factors can be adjusted automatically in the investment process. In addition, the adaptive mean-variance (AMV) model is firstly applied in online portfolio selection where the variance is employed to measure the investment risk and the covariance matrix can be linearly updated in the investment process. The adaptive online moving average mean-variance (AOLPIMV) algorithm is designed to provide flexible investment strategies for investors with different risk preferences. Finally, numerical experiments are presented to validate the effectiveness and advantages of AOLPIMV.

AI-driven sustainability brand activism for family businesses: a future-proofing perspective article

PurposeArtificial intelligence (AI) and sustainable business represent the irrefutable future of all forward looking businesses in the world today. In this perspective article, the authors explore the confluence of these important topics by highlighting the role of family businesses in advancing sustainable brand activism aligned with the United Nations Sustainable Development Goals (UNSDGs), like SDG 1, which emphasises poverty eradication. The authors fall on the transformative potential of artificial intelligence (AI) and online brand communities in family businesses as an anchor for promoting sustainability practices that align with UNSDGs.Design/methodology/approachUsing literature review, the authors fall on the transformative potential of AI and online brand communities in family businesses as an anchor for promoting sustainability practices that align with UNSDGs.FindingsScholarly research on AI-driven sustainability brand activism in family businesses is either limited or nonexistent. Family businesses have a unique opportunity to use AI for eco-friendly operations, personalised brand engagement, eco-friendly product development, global collaborations and education and advocacy in support of the UNSDGs. Future research could look at how family businesses align their values, their long-term effects, how they work across generations, how resilient and flexible they are and how they compare to non-family businesses when it comes to using AI and brand activism as long-term strategies for sustainability and survival.Originality/valueThe authors call for family businesses, governments and stakeholders to take theoretical and practical actions in promoting AI-driven sustainability brand activism aligned with the UNSDGs. It underscores the distinctive role of family businesses in driving sustainability and fostering brand activism through AI in a digital age.

There Was No 'First AI Winter'

Despite challenges and failures, the artificial intelligence community grew steadily during the 1970s.

Design Thinking and AI: A New Frontier for Designing Human‐Centered AI Solutions

AbstractIn recent years, design thinking (DT) has become a pervasive innovation approach in the managerial community as a structured way of solving challenging problems through a human‐centered perspective. The approach fosters lateral thinking and has a huge impact on the organizational culture and the core elements of the innovation process. At the same time, the number of organizations trying to solve problems through the application of artificial intelligence (AI) is constantly growing, although more than 80% of AI projects never reach deployment due to the wrong strategic approach, poor data quality, or a lack of AI awareness among employees—and those that do remain below profitability expectations. The aim of this paper is therefore to connect the AI and DT communities by proposing a first framework supporting the development of AI solutions in a human‐centered way. The contribution of this paper is thus twofold: First, we investigate ways in which design thinking adds real value along the design process of intelligent solutions, thereby enriching the present body of design management literature. Second, we propose a first framework by linking core elements of both worlds to support strategic designers and data engineers in designing such applications. Finally, we propose a research agenda that can serve as a basis for future research directions in order to advance contributions at the intersection of the two fields.

CAR VEHICLE IMAGE OBJECT DETECTION USING YOU ONLY LIVE ONCE (YOLO)

This research aims to analyze the performance of image object detection methods using You Only Live Once (YOLO) specifically in the context of car detection. YOLO-based object detection methods have gained great attention in the artificial intelligence community due to their ability to perform real-time object detection. In this research, we focus on using YOLO to detect car objects in images. The YOLO method will be tested for performance using a dataset of car images that have been collected from various sources. This dataset includes various lighting conditions, backgrounds, and car positions. The training process will be performed using the YOLO architecture that has been pre-trained with an extensive dataset. In the testing phase, the performance of the YOLO method in car object detection will be evaluated using standard evaluation metrics such as precision, detection speed, and recall. The results of this study will show the success rate of YOLO in car detection in images and provide a better understanding of the limitations and advantages of this method. The conclusion of this research is expected to provide valuable insight into the use of the YOLO method in car object detection. This information can be used as a basis for the development and improvement of YOLO-based object detection methods, and can be applied in various applications such as automated vehicle security systems and traffic analysis.

A geometric framework for multiclass ensemble classifiers

Ensemble classifiers have been investigated by many in the artificial intelligence and machine learning community. Majority voting and weighted majority voting are two commonly used combination schemes in ensemble learning. However, understanding of them is incomplete at best, with some properties even misunderstood. In this paper, we present a group of properties of these two schemes formally under a geometric framework. Two key factors, every component base classifier’s performance and dissimilarity between each pair of component classifiers are evaluated by the same metric—the Euclidean distance. Consequently, ensembling becomes a deterministic problem and the performance of an ensemble can be calculated directly by a formula. We prove several theorems of interest and explain their implications for ensembles. In particular, we compare and contrast the effect of the number of component classifiers on these two types of ensemble schemes. Some important properties of both combination schemes are discussed. And a method to calculate the optimal weights for the weighted majority voting is presented. Empirical investigation is conducted to verify the theoretical results. We believe that the results from this paper are very useful for us to understand the fundamental properties of these two combination schemes and the principles of ensemble classifiers in general. The results are also helpful for us to investigate some issues in ensemble classifiers, such as ensemble performance prediction, diversity, ensemble pruning, and others.

Mediating Role of Organizational Learning in the Relationship between Use of Artificial Intelligence Security Technology and Community Security

This study focuses on developing a robust model to augment community security through the implementation of AI, while simultaneously investigating the mediating influence of organizational learning within the context of vital AI factors in the UAE. The model examines the interplay among key AI elements, such as compatibility (COMPAT), complexity (COMPLEX), management support (MS), ethics (ETH), and staff capabilities (SC), concerning their impact on the effectiveness of Community Security (ESC). Data collection was conducted via a questionnaire survey utilizing the Abu Dhabi Police as a representative case study for public organizations in the UAE, involving 138 participants spanning both managerial and operational roles, with responses acquired through randomized distribution using online tools. The amassed data was employed to construct the model using SmartPLS software, and its evaluation adhered to assessment criteria encompassing measurement and structural components. A goodness-of-fit score of 0.751 indicated a high level of overall predictive performance for the model. The study's findings revealed that organizational learning (OL) serves as a partial mediator in the relationship between the complexity construct (COMPLEX) and the effectiveness of Community Security (ESC), with no observed mediation effects in other relationships. The research outcomes culminated in the creation of a versatile model that enhances community security through AI technology, applicable across diverse scenarios, and benefiting individuals invested in AI and community security, such as academics, researchers, and practitioners. The study's methodology provides valuable insights for practitioners and researchers in the UAE and related fields, affording opportunities for replication or adaptation to suit specific investigative contexts.