Risk Of AIS Research Articles

Retrospective Single-Center Analysis of 5575 Spinal Surgeries for Complication Associations and Potential Future Use of Generated Data.

Background: This study aims to retrospectively detect associations with postoperative complications in spinal surgeries during the hospitalization period using standardized, single-center data to validate a method for complication detection and discuss the potential future use of generated data. Methods: Data were generated in 2006-2019 from a standardized, weekly complications conference reviewing all neurosurgical operations at the University Hospital Luebeck. Paper-based data were recorded in a standardized manner during the conference and transferred with a time delay of one week into a proprietary complication register. A total of 5575 cases were grouped based on the diagnosis, surgical localization, approach, instrumentation, previous operations, surgery indication, age, ASA score, and pre-existing conditions. Retrospective analysis was performed using a logistic regression detecting complication associations. The results were compared to the literature validating the method of complication detection. Results: Mean cohort age: 58.83 years. Overall complication rate: 10.9%. Mortality rate: 0.25%. The statistically significant complication associations were age; an age of >60; the localization (cervical, thoracic); a cervical tumor or trauma diagnosis; lumbar degenerative conditions, tumor, trauma, or infection; a cervical hemi-/laminectomy and vertebral body replacement; a lumbar hemi-/laminectomy, posterior spondylodesis, and 360° fusion; lumbar instrumentation, with an ASA score of three and four; a ventral and combined/360° approach; a lumbar combined/360° revision; two, three and ≥four pre-existing conditions; hypertension; osteoporosis; arrhythmia; an oncological condition; kidney dysfunction; stroke; and thrombosis. Conclusions: Documenting risk profiles for spinal procedures is important in identifying postoperative complications. The available data provide a comprehensive overview within a single center for spinal surgeries. Standardized complication recording during an established complication conference in the clinical routine enables the detection of significant complications. It is desirable to standardize the registration of postoperative complications to facilitate comparability across different institutions. The results may contribute to national or international databases used for automated AI risk profiling.

Getting democracy wrong

Recent developments in large language models and computer automated systems more generally (colloquially called ‘artificial intelligence’) have given rise to concerns about potential social risks of AI. Of the numerous industry-driven principles put forth over the past decade to address these concerns, the Future of Life Institute’s Asilomar AI principles are particularly noteworthy given the large number of wealthy and powerful signatories. This paper highlights the need for critical examination of the Asilomar AI Principles. The Asilomar model, first developed for biotechnology, is frequently cited as a successful policy approach for promoting expert consensus and containing public controversy. Situating Asilomar AI principles in the context of a broader history of Asilomar approaches illuminates the limitations of scientific and industry self-regulation. The Asilomar AI process shapes AI’s publicity in three interconnected ways: as an agenda-setting manoeuvre to promote longtermist beliefs; as an approach to policy making that restricts public engagement; and as a mechanism to enhance industry control of AI governance.

Grafiki generowane przez sztuczną inteligencję na lekcjach języka włoskiego. Przykłady użycia

The article examines the potential of artificial intelligence in generating images for Italian language lessons. It discusses AI’s educational applications, particularly in language teaching, and reviews practical tools for image creation. Key sections explore the use of AI-generated images in lessons and suggest beneficial types of images. The article introduces AI as a tool for educators, enhancing lesson preparation, exercise personalization, and developing linguistic, communicative, cultural, and digital skills while considering the opportunities and risks of AI’s rapid advancement.

AI-powered business model innovation: Analyzing the implications of generative AI on value creation, operational efficiency and market dynamics

The revolutionary way that generative AI disrupts business models today is by delivering radical shifts in important things like value delivery, business operations, and competitiveness. It allows businesses to perform difficult tasks, orchestrate customized client interactions, and foster change throughout sectors. With endless possibilities, generative AI's risks upon implementation are as follows: Data protection, Ethical and legal spheres, and Talent demand. Some challenges must be overcome when adopting AI in any organization to harness this technology for work benefits. Generative AI will continue to define future business models ' directions and contours through innovation, support of the decision-making process, and a focus on the principles of ethical entrepreneurship. This paper discusses the opportunities of generative AI for business model innovations, its effect on operational improvement, and entering a new kind of competition in a market. Finally, it reveals the factors that companies experience when implementing AI and presents the possible future trend of deploying business models based on artificial intelligence.

Government regulation or industry self-regulation of AI? Investigating the relationships between uncertainty avoidance, people’s AI risk perceptions, and their regulatory preferences in Europe

Government regulation or industry self-regulation of AI? Investigating the relationships between uncertainty avoidance, people’s AI risk perceptions, and their regulatory preferences in Europe

Artificial intelligence & Trusts and Trustees: a new dawn of investment opportunities and risks?

Abstract This article examines the opportunities and risks that can be created for trusts by generative artificial intelligence. In particular, the work is concerned with how AI investment tools may affect trusts, given their growing use in investment management. It is argued that trusts can be exposed to the risks and opportunities that this technology may create through the trustees’ general investment power. However, currently, trustees can undertake appropriate risk management by exercising their section 4 and 5 duties relating to investment. The work ends by suggesting that targeted statutory reform and guidance is needed to deal with AI risks.

Abstract 4147614: Prevalence and Impact of Stroke In Young (18-44 Years) ACHD-Related Admissions by Median Household Income Quartiles: A 2019 National Inpatient Sample Analysis

Background: The risk of stroke in Adult Congenital Heart Disease (ACHD) is well established. However, the prevalence and impact of stroke in young ACHD-related admissions stratified by median household income remain underexplored. Methods: The National Inpatient Sample (2019) was utilized to identify the rate of stroke admissions among young (18-44 years) ACHD patients. We also evaluated comorbidities and outcomes (all-cause mortality [ACM], length of stay [LOS], disposition,&charges) based on the median household income quartiles and compared two cohorts of lowest (0-25th percentile) and highest (75-100thpercentile) median household income (LMHI vs HMHI). Results: Of 41950 young (18-44 years) ACHD patients, 5360 patients had stroke (12.8%). Patients in the LMHI group were relatively older (median age 36 years vs 35 years) and had a greater proportion of males (53.8% vs 46.0%) when compared to patients in the HMHI group. Patients admitted with stroke were of greater proportion in the LMHI group (13.2% vs 12.1%) compared to the HMHI group. Comorbidities like alcohol abuse (4.7% vs. 2.3%), depression (9.8% vs. 7.0%), hypertension (13.0% vs. 5.6%), obesity (21.2% vs. 18%), drug abuse (12.3% vs 4.2%), and tobacco use disorder (30.4% vs 11.2%) were higher for LMHI when compared to HMHI. Regarding in-hospital outcomes, ACM (3.7 vs. 1.9%) and discharge to home (71.6 vs. 66.5) were higher with HMHI, whereas the LMHI cohort demonstrated fewer routine discharges, higher transfers to other facilities, and prolonged LOS (5 vs. 3 days, p<0.001). When adjusted for confounders, there were statistically higher odds of AIS in ACHD patients from the LMHI group vs. the HMHI group [aOR 1.33; 1.02-1.74; p=0.045]. However, no association of income quartile with overall stroke risk was observed. Conclusion: The prevalence of stroke is significantly higher in the LMHI group. When controlled for confounders, there was a higher risk of AIS without any impact on overall stroke. Furthermore, the lowermost income quartile was associated with fewer routine dispositions, and frequent transfers can further adversely increase healthcare inequalities and healthcare costs.

A risk-based approach to mitigating the Gen(AI) challenge to assessment integrity

In the evolving landscape of higher education, emerging technologies such as Generative AI (GenAI) present both opportunities and challenges. The rapid increase in access to, and sophistication of, GenAI poses significant challenges for our institution and the wider sector (Bearman & Luckin 2020, Bobula 2024, Chan & Hu 2023). The most immediate of these challenges is the increased risk to the existing approaches used to assure that students achieve the stated outcomes of their learning experiences. Director of Higher Education Integrity Unit, TEQSA, Dr Helen Gniel argues ‘We are entering an age where institutions can no longer ensure the integrity of every single assessment, and it makes sense to think at that bigger level […] what are the mechanisms that you have in place to be confident that an individual has demonstrated the learning outcomes to receive that degree’(TEQSA information request: AI risk mitigation, webinar, 21 March 2024). While higher education institutions are exploring opportunities to realise the potential benefits of GenAI, our immediate focus is on mitigating these risks. This poster describes institutional Programmatic Assessment Security Project (PASP) at the University of Newcastle (UON), a pioneering four-stage initiative designed to address academic integrity risks associated with inappropriate uses of GenAI in summative assessment (Lodge 2024). Academic integrity risks in this context refers to the potential for students to engage in academic misconduct by using GenAI tools in ways that violate institutional policies or ethical standards for assessment. These risks pose a threat to the validity and reliability of summative assessments, potentially compromising the integrity of academic qualifications and the learning process itself. Led by the central teaching and learning support unit, in collaboration with the Artificial Intelligence Working Group (AIWG) a group of expert practitioners and academic stakeholders at UON, the PASP aims to secure assessments across a program of study, assuring student attainment of program learning outcomes. This innovative approach aligns with the sector’s focus on programmatic assessment and supports compliance with regulatory requirements, including TEQSA’s mid 2024 request for institutions to submit a detailed action plan to mitigate the risks that GenAI poses to the integrity of awards (TEQSA 2024). This poster showcases exemplary innovation in practice through the PASP, advancing research and practice in technology-enhanced learning (TEL). The PASP employs a risk-based approach, identifying seven risk factors for inappropriate student use of GenAI in assessment tasks. The first step in this project involved Course Coordinators completing an Assessment Reflection Survey, guiding them to reflect on individual assessment tasks in relation to these risk factors. The survey data were then used to compile an understanding of the varying levels of assessment risk across the institution’s programs. The PASP will deliver a defined series of secure assessments, delivered at key moments across a program of study to assure student attainment of program learning outcomes. This poster provides insight into UON’s contemporary and agile approach to TEL and assessment redesign, with potential for staff at other institutions to adopt similar responses.

Potential benefits and risks of advanced AI in healthcare

Potential benefits and risks of advanced AI in healthcare

Does trust in government moderate the perception towards deepfakes? Comparative perspectives from Asia on the risks of AI and misinformation for democracy

There have recently been growing global concerns about misinformation, and more specifically about how deepfake technologies have been used to run disinformation campaigns. These concerns, in turn, have influenced people's perceptions of deepfakes, often associating them with threats to democracy and fostering less positive views. But does high trust in government mitigate these influences, thereby strengthening positive perceptions of deepfakes? In a cross-national survey conducted in Malaysia, Singapore, and India, we found no evidence of a negative association either between concern about the spread of misinformation online or perceived risks of AI to democracy, with positive attitudes towards deepfakes. However, when accounting for the moderating factor of trust in government, respondents in Singapore who have high trust levels exhibited more positive attitudes towards deepfakes, despite their concerns about misinformation. Similarly, higher trust in government correlated with more favorable perceptions of deepfakes even among those who view AI as a risk to democracy; this effect is evident across all three countries. In the conclusion, we spell out the implications of these findings for politics in Asia and beyond.

Resistance to medical artificial intelligence: Integrating AI awareness, AI risks, and displacement of responsibility

Resistance to medical artificial intelligence: Integrating AI awareness, AI risks, and displacement of responsibility

Co-designing an AI Impact Assessment Report Template with AI Practitioners and AI Compliance Experts

In the evolving landscape of AI regulation, it is crucial for companies to conduct impact assessments and document their compliance through comprehensive reports. However, current reports lack grounding in regulations and often focus on specific aspects like privacy in relation to AI systems, without addressing the real-world uses of these systems. Moreover, there is no systematic effort to design and evaluate these reports with both AI practitioners and AI compliance experts. To address this gap, we conducted an iterative co-design process with 14 AI practitioners and 6 AI compliance experts and proposed a template for impact assessment reports grounded in the EU AI Act, NIST's AI Risk Management Framework, and ISO 42001 AI Management System. We evaluated the template by producing an impact assessment report for an AI-based meeting companion at a major tech company. A user study with 8 AI practitioners from the same company and 5 AI compliance experts from industry and academia revealed that our template effectively provides necessary information for impact assessments and documents the broad impacts of AI systems. Participants envisioned using the template not only at the pre-deployment stage for compliance but also as a tool to guide the design stage of AI uses.

Atlas of AI Risks: Enhancing Public Understanding of AI Risks

The prevailing methodologies for visualizing AI risks have focused on technical issues such as data biases and model inaccuracies, often overlooking broader societal risks like job loss and surveillance. Moreover, these visualizations are typically designed for tech-savvy individuals, neglecting those with limited technical skills. To address these challenges, we propose the Atlas of AI Risks—a narrative-style tool designed to map the broad risks associated with various AI technologies in a way that is understandable to non-technical individuals as well. To both develop and evaluate this tool, we conducted two crowdsourcing studies. The first, involving 40 participants, identified the design requirements for visualizing AI risks for decision-making and guided the development of the Atlas. The second study, with 140 participants reflecting the US population in terms of age, sex, and ethnicity, assessed the usability and aesthetics of the Atlas to ensure it met those requirements. Using facial recognition technology as a case study, we found that the Atlas is more user-friendly than a baseline visualization, with a more classic and expressive aesthetic, and is more effective in presenting a balanced assessment of the risks and benefits of facial recognition. Finally, we discuss how our design choices make the Atlas adaptable for broader use, allowing it to generalize across the diverse range of technology applications represented in a database that reports various AI incidents.

Exploring the impact of generative AI technologies on education: Academic expert perspectives, trends, and implications for sustainable development goals

This study provides empirical data on the impact of generative AI in education, with special emphasis on sustainable development goals (SDGs). By conducting a thorough analysis of the relationship between generative AI technologies and educational outcomes, this research fills a critical gap in the literature. The insights offered are valuable for policymakers seeking to leverage new educational technologies to support sustainable development. Using Smart-PLS4, five hypotheses derived from the research questions were tested based on data collected from an E-Questionnaire distributed to academic faculty members and education managers. Of the 311 valid responses, the measurement model assessment confirmed the validity and reliability of the data, while the structural model assessment validated the hypotheses. The study’s findings reveal that New Approaches to Learning Outcome Assessment (NALOA) significantly contribute to achieving SDGs, with a path coefficient of 0.477 (p < 0.001). Similarly, the Use of Generative AI Technologies (UGAIT) has a notable positive impact on SDGs, with a value of 0.221 (p < 0.001). A Paradigm Shift in Education and Educational Process Organization (PSEPQ) also demonstrates a significant, though smaller, effect on SDGs with a coefficient of 0.142 (p = 0.008). However, the Opportunities and Risks of Generative AI in Education (ORGIE) study did not find statistically significant evidence of an impact on SDGs (p = 0.390). These findings highlight the potential opportunities and challenges of using generative AI technologies in education and underscore their key role in advancing sustainable development goals. The study also offers a strategic roadmap for educational institutions, particularly in Oman to harness AI technology in support of sustainable development objectives.

The need for a risk management framework for data science projects: a systematic literature review

Many data science endeavors encounter failure, surfacing at any project phase. Even after successful deployments, data science projects grapple with ethical dilemmas, such as bias and discrimination. Current project management methodologies prioritize efficiency and cost savings over risk management. The methodologies largely overlook the diverse risks of sociotechnical systems and risk articulation inherent in data science lifecycles. Conversely, while the established risk management framework (RMF) by NIST and McKinsey aims to manage AI risks, there is a heavy reliance on normative definitions of risk, neglecting the multifaceted subjectivities of data science project failures. This paper reports on a systematic literature review that identifies three main themes: Big Data Execution Issues, Demand for a Risk Management Framework tailored for Large-Scale Data Science Projects, and the need for a General Risk Management Framework for all Data Science Endeavors. Another overarching focus is on how risk is articulated by the institution and the practitioners. The paper discusses a novel and adaptive data science risk management framework – “DS EthiCo RMF” – which merges project management, ethics, and risk management for diverse data science projects into one holistic framework. This agile risk management framework DS EthiCo RMF can bridge the current divide between normative risk standards and the multitude of data science requirements, offering a human-centric method to navigate the intertwined sociotechnical risks of failure in data science projects.

AI Integration and Economic Divides: Analyzing Global AI Strategies

Abstract This study investigates the impact of socio-economic factors on national AI strategies in India, Bangladesh, Germany, UAE, Egypt, and the USA through quantitative content analysis. The analysis explores the correlation between GDP per capita, the share of manufacturing, and the frequency of risk-related terms in AI strategy documents. It is found that wealthier nations emphasize AI risks more, correlating with deeper technological integration into their societal structures. Conversely, the emphasis on AI risks shows a weak correlation with the share of manufacturing, indicating broader AI impacts in service-oriented sectors. Lower-middle-income countries appear more optimistic, focusing on AI's economic benefits. The study underscores the need for balanced AI strategies that promote innovation while ensuring worker well-being, advocating for adaptive governance frameworks that enhance workplace safety and efficiency.

Navigating the Ethical Dilemma of Generative AI in Higher Educational Institutions in Nigeria using the TOE Framework

Generative AI tools stand at the threshold of innovation and the erosion of the long-standing values of creativity, critical thinking, authorship, and research in higher education. This research crafted a novel framework from the technology, organization, and environment (TOE) framework to guide higher educational institutions in Nigeria to navigate the ethical dilemma of generative AI. A questionnaire was used to collect data from twelve higher institutions among lecturers, students, and researchers across the six (6) geopolitical zones of Nigeria. The structural equation modeling was used to analyze the data using the SPPS Amos version 23. The results revealed that factors such as perceived risks of generative AI, Curriculum support, institutional policy, and perceived generative AI trends positively impact the need for a generative AI ethical framework in higher educational institutions in Nigeria. Furthermore, the study contributes to the adoption of theory to navigate the ethical dilemma in the use of generative AI tools in higher educational institutions in Nigeria. It also provides some practical implications that suggest the importance of inculcating ethical discussions into the curriculum as part of institutional policy to create awareness and guidance on the use of generative AI.

CardioRiskNet: A Hybrid AI-Based Model for Explainable Risk Prediction and Prognosis in Cardiovascular Disease.

The global prevalence of cardiovascular diseases (CVDs) as a leading cause of death highlights the imperative need for refined risk assessment and prognostication methods. The traditional approaches, including the Framingham Risk Score, blood tests, imaging techniques, and clinical assessments, although widely utilized, are hindered by limitations such as a lack of precision, the reliance on static risk variables, and the inability to adapt to new patient data, thereby necessitating the exploration of alternative strategies. In response, this study introduces CardioRiskNet, a hybrid AI-based model designed to transcend these limitations. The proposed CardioRiskNet consists of seven parts: data preprocessing, feature selection and encoding, eXplainable AI (XAI) integration, active learning, attention mechanisms, risk prediction and prognosis, evaluation and validation, and deployment and integration. At first, the patient data are preprocessed by cleaning the data, handling the missing values, applying a normalization process, and extracting the features. Next, the most informative features are selected and the categorical variables are converted into a numerical form. Distinctively, CardioRiskNet employs active learning to iteratively select informative samples, enhancing its learning efficacy, while its attention mechanism dynamically focuses on the relevant features for precise risk prediction. Additionally, the integration of XAI facilitates interpretability and transparency in the decision-making processes. According to the experimental results, CardioRiskNet demonstrates superior performance in terms of accuracy, sensitivity, specificity, and F1-Score, with values of 98.7%, 98.7%, 99%, and 98.7%, respectively. These findings show that CardioRiskNet can accurately assess and prognosticate the CVD risk, demonstrating the power of active learning and AI to surpass the conventional methods. Thus, CardioRiskNet's novel approach and high performance advance the management of CVDs and provide healthcare professionals a powerful tool for patient care.

Data Scientists Discuss AI Risks and Opportunities

Data Scientists Discuss AI Risks and Opportunities

AI Enhanced Cloud DevOps and Automation

Modern cloud computing and DevOps widely embrace AI technology, which accelerated the constantly changing and developing paradigm and reached new heights of productivity, expansibility, and stability in computer science. Subsequently, cloud-based DevOps and the automation of workloads enable further discovery of promising AI components, its advantages and risks observed, and the prospects for advancing organization’s digital transformation. This paper explores and supports, by reviewing case studies and trends, how AI can complement and extend human capacities, optimize operations, and improve choices in a more constant deployment of function.