Search

Introduction: The 2024 Voice AI Symposium, presented by the Bridge2AI-Voice Consortium, featured deep-dive educational workshops conducted by experts from diverse fields to explore the latest advancements in voice biomarkers and artificial intelligence (AI) applications in healthcare. Through five workshops, attendees learned about topics including international standardization of vocal biomarker data, real-world deployment of AI solutions, assistive technologies for voice disorders, best practices for voice data collection, and deep learning applications in voice analysis. These workshops aimed to foster collaboration between academia, industry, and healthcare to advance the development and implementation of voice-based AI tools. Methods: Each workshop featured a combination of lectures, case studies, and interactive discussions. Transcripts of audio recordings were generated using Whisper (Version 7.13.1) and summarized by ChatGPT (Version 4.0), then reviewed by the authors. The workshops covered various methodologies, from signal processing and machine learning operations (MLOps) to ethical concerns surrounding AI-powered voice data collection. Practical demonstrations of AI-driven tools for voice disorder management and technical discussions on implementing voice AI models in clinical and non-clinical settings provided attendees with hands-on experience. Results: Key outcomes included the discussion of international standards to unify stakeholders in vocal biomarker research, practical challenges in deploying AI solutions outside the laboratory, review of Bridge2AI-Voice data collection processes, and the potential of AI to empower individuals with voice disorders. Additionally, presenters shared innovations in ethical AI practices, scalable machine learning frameworks, and advanced data collection techniques using diverse voice datasets. The symposium highlighted the successful integration of AI in detecting and analyzing voice signals for various health applications, with significant advancements in standardization, privacy, and clinical validation processes. Discussion: The symposium underscored the importance of interdisciplinary collaboration to address the technical, ethical, and clinical challenges in the field of voice biomarkers. While AI models have shown promise in analyzing voice data, challenges such as data variability, security, and scalability remain. Future efforts must focus on refining data collection standards, advancing ethical AI practices, and ensuring diverse dataset inclusion to improve model robustness. By fostering collaboration among researchers, clinicians, and technologists, the symposium laid a foundation for future innovations in AI-driven voice analysis for healthcare diagnostics and treatment. [ABSTRACT FROM AUTHOR]

Foundation Models (FMs) are gaining increasing attention in the biomedical artificial intelligence (AI) ecosystem due to their ability to represent and contextualize multimodal biomedical data. These capabilities make FMs a valuable tool for a variety of tasks, including biomedical reasoning, hypothesis generation, and interpreting complex imaging data. In this review paper, we address the unique challenges associated with establishing an ethical and trustworthy biomedical AI ecosystem, with a particular focus on the development of FMs and their downstream applications. We explore strategies that can be implemented throughout the biomedical AI pipeline to effectively tackle these challenges, ensuring that these FMs are translated responsibly into clinical and translational settings. Additionally, we emphasize the importance of key stewardship and co-design principles that not only ensure robust regulation but also guarantee that the interests of all stakeholders—especially those involved in or affected by these clinical and translational applications—are adequately represented. We aim to empower the biomedical AI community to harness these models responsibly and effectively. As we navigate this exciting frontier, our collective commitment to ethical stewardship, co-design, and responsible translation will be instrumental in ensuring that the evolution of FMs truly enhances patient care and medical decision-making, ultimately leading to a more equitable and trustworthy biomedical AI ecosystem. [ABSTRACT FROM AUTHOR]

The rapid advancement of emerging technologies has brought about unprecedented opportunities for empowerment across various domains. This research paper delves into the transformative potential of technologies such as artificial intelligence (AI), Internet of Things (IoT), big data analytics, and cloud computing in enabling access to information, resources, and opportunities. It examines how these technologies can contribute to economic empowerment, social inclusion, and overall human development. The paper explores the applications of AI in sectors like healthcare, education, and finance, highlighting its ability to provide personalized solutions and enhance access to services. Additionally, it investigates the role of IoT in enabling smart cities, connected healthcare, and efficient resource management, empowering individuals and communities through data-driven decision-making. The paper also discusses the applications of big data analytics in sectors like healthcare, education, and disaster management, and how it can provide insights into societal challenges and inform policy decisions. Furthermore, it explores the potential of cloud computing in democratizing technology, fostering innovation, and enabling access to resources and services for individuals and organizations. However, the paper also addresses the challenges associated with the adoption of emerging technologies, such as the digital divide, privacy concerns, and ethical implications, emphasizing the need for responsible and inclusive technological advancements. Overall, this research paper highlights the transformative potential of emerging technologies in empowering individuals and communities, while also acknowledging the challenges and ethical considerations that must be addressed for a more equitable and sustainable future [ABSTRACT FROM AUTHOR]

The rapid integration of Artificial Intelligence (AI) into critical domains such as healthcare, finance, and criminal justice has raised significant ethical concerns, particularly around bias and fairness in machine learning models. Despite their potential for improving decision-making processes, these models can perpetuate or even exacerbate existing societal biases. This study aims to investigate approaches to bias mitigation in AI systems, focusing on balancing fairness and performance. A systematic review of 150 research articles published between 2018 and 2023 was conducted, along with experiments on 25 benchmark datasets to evaluate various machine learning algorithms and bias mitigation techniques. Results showed a 23% reduction in bias and an average 17% improvement in nine fairness metrics during model training, though at the cost of up to 9% in overall accuracy. The study highlights the trade-offs between fairness and performance, suggesting that creating AI systems that are both fair and effective remains an ongoing challenge. The findings underscore the need for adaptive frameworks that address bias without significantly compromising model performance. Future research should explore domain-specific adaptations and scalable solutions for integrating fairness throughout the AI development process to ensure more equitable outcomes.

This study aimed to investigate the barriers to responsible interaction between the global employer, technology provider, and trade union to realize the postulate of ethical artificial intelligence in the algorithmic management process. To overcome the barriers, this study offers a pedagogical explanation based on a universal schema for shaping a machine-learning model

Abstract The adoption of artificial intelligence into industrial settings promises notable enhancements in productivity, quality, efficiency, competitiveness, and innovations. However, transitioning AI models from concept to full-scale industrial applications involves various complexities and challenges. These challenges are not only technical but also extend into the ethical and regulatory realms, calling for a comprehensive approach to AI integration. This paper examines the diverse hurdles faced during developing and deploying AI applications in the industrial domain. It addresses challenges in collecting the right data, construction of AI models, and ensuring that these models work accurately and responsibly when deployed in real industrial environment. Furthermore, the paper presents strategic recommendations, underscoring the necessity of ethical considerations and regulatory compliance to effectively overcome these obstacles. We provide guidelines aimed at maximizing AI's benefits in industrial environments while minimizing potential risks.

Artificial intelligence has been talked about for over half a century now. Still, it became a fast-growing reality in 2023 through modern technologies, such as Meta AI, Open AI, or ChatGPT, and has created some ethical concerns. This research provides examples of how AI is being used in academia, how it can be used, and how to assess college students’ familiarity with such technologies, their perception of it, and level of usage. Using an AI-generated short survey to gather quantitative and qualitative data through a discussion exercise, 126 undergraduates with four different professors were asked to share their answers and views. The findings show that many of today’s college students in South Florida see the usage of AI as ethical and legal. However, a few respondents remain uncertain due to a lack of clear guidelines from professors and the institution. Thus, most respondents reported that they are familiar with AI as they use it multiple times weekly. Consequently, educators and administrators must sharpen their students’ AI skills so they can be ethical and competitive in the workplace. Implications for students, educators and administrators in the higher education arena are explored. Besides serving as a person’s second brain, using AI can be an excellent way for students to mitigate and overcome procrastination, enhance their productivity, and comprehensively complete academic projects on time. Furthermore, the proper use of AI tools can reduce errors, quickly assess large amounts of data, automate repetitive functions, lead to better decisions, and help learners move forward amid challenging obstacles. As such, academic institutions must do more to ensure they are “sharpening their students’ AI saw” before they graduate and embark on their professional endeavors. Artificial intelligence, when used properly, ethically, and legally following established industry norms and guidelines, offers many transformative benefits across diverse fields to benefit human beings and society. Students pursuing a healthcare career can use AI to aid in early disease detection, accelerate drug discovery, and improve patient care through precision medicine. Graduates in the engineering or transportation industries can use AI to optimize traffic flow, enhance safety with autonomous vehicles, and reduce emissions through predictive maintenance. Moreover, those who remain in the education field after graduation can use AI to facilitate personalized learning experiences tailored to individual student needs while fostering greater engagement and academic success for all learners. The latest advancements underscore AI’s potential to drive innovation, increase efficiency, and address complex challenges while ultimately shaping a more interconnected and prosperous future for everyone in society.

The adoption of artificial intelligence into industrial settings promises notable enhancements in productivity, quality, efficiency, competitiveness, and innovations. However, transitioning AI models from concept to full-scale industrial applications involves various complexities and challenges. These challenges are not only technical but also extend into the ethical and regulatory realms, calling for a comprehensive approach to AI integration. This paper examines the diverse hurdles faced during developing and deploying AI applications in the industrial domain. It addresses challenges in collecting the right data, construction of AI models, and ensuring that these models work accurately and responsibly when deployed in real industrial environment. Furthermore, the paper presents strategic recommendations, underscoring the necessity of ethical considerations and regulatory compliance to effectively overcome these obstacles. We provide guidelines aimed at maximizing AI's benefits in industrial environments while minimizing potential risks. [ABSTRACT FROM AUTHOR]

This paper embarks on a comprehensive and in-depth comparative analysis of two prominent artificial intelligence applications: ChatGPT and IBM Watson. Developed by OpenAI, ChatGPT excels in natural language processing, leveraging cutting-edge technology to enable robust conversational interfaces. In contrast, IBM Watson offers a multifaceted suite of AI services that encompass machine learning, advanced data analytics, and sophisticated language processing capabilities. This analysis delves into their intricate technical specifications, exploring the nuances of their respective architectures and computational frameworks. It meticulously evaluates their diverse capabilities, ranging from text generation and comprehension to complex data analysis and predictive modeling. Moreover, the paper critically examines the industry-specific use cases where ChatGPT and IBM Watson demonstrate their strengths and applications. By highlighting real-world examples across various sectors such as healthcare, finance, customer service, and education, it elucidates how each platform can be leveraged to enhance operational efficiency, decision-making processes, and user interaction experiences. Additionally, the analysis forecasts emerging trends in AI development, shedding light on how these technologies are expected to evolve and integrate into broader technological landscapes. [ABSTRACT FROM AUTHOR]

This study, conducted at Complutense Intermedia-Lab, employs a dual approach to explore university students' use of Generative AI (GenAI), combining a survey with foresight methodologies (Sci-fi prototyping). The initial survey gathers baseline data on students' experiences, attitudes, and concerns regarding GenAI, providing a comprehensive understanding of current practices among university students in Spain. This empirical foundation informs subsequent Sci-fi prototyping sessions, where students creatively envision future scenarios, fostering futurist thinking and deeper engagement. By integrating principles of Responsible Research and Innovation (RRI), this approach facilitates a nuanced exploration of GenAI's potential impacts on education. The incorporation of both quantitative data collection and qualitative foresight methods in this study serves to navigate challenges and level opportunities of promoting the ethical and inclusive incorporation of GenAI in Higher Education, ensuring that future innovations align with societal values and needs. [ABSTRACT FROM AUTHOR]

The integration of artificial intelligence (AI) in cosmetology is transforming the industry in numerous ways, including the introduction of advanced tools such as at-home skin analysis devices that can evaluate skin quality and augmented reality applications that allow users to virtually try on various makeup products. These innovations empower individuals to make well-informed decisions about their cosmetic care and enable cosmetologists to predict treatment outcomes with higher accuracy. In this way, AI enhances patient satisfaction by better aligning expectations with achievable results. A computerized database search was performed to identify articles relevant to this topic. A comprehensive search was applied to the following electronic databases: IEEE Xplore, PubMed, Google Scholar, and Research Gate. This review explores four key areas in the current literature where AI contributes to cosmetic procedures. Firstly, AI democratizes skincare by making products and services more accessible to everyone. Secondly, it bridges the gap between physicians and cosmetic suppliers by enlightening collaboration and innovation. Thirdly, it improves the assessment of cosmetic ingredients by ensuring better safety and efficacy, and lastly, AI provides an ethical alternative to animal testing by replacing the Local Lymph Node Assay (LLNA) and the Guinea Pig Maximization Test (GPMT) with in silico models. While AI offers significant benefits, it also raises concerns about data privacy, informed consent, and the potential for promoting unrealistic beauty standards. Addressing these challenges involves implementing measures such as anonymization and de-identification techniques to protect sensitive data and safeguard informed consent for data collection and processing. This article aims to highlight the responsible and ethical use of AI in cosmetology, emphasizing the importance of accuracy and customization in cosmetic care, which represents a significant advancement in the industry. [ABSTRACT FROM AUTHOR]

This paper delves into the fusion of artificial intelligence (AI) and emotional intelligence (EQ) by analyzing the frameworks of international sustainability agendas driven by UNESCO, WEF, and UNICEF. It explores the potential of AI integrated with EQ to effectively address the Sustainable Development Goals (SDGs), with a focus on education, healthcare, and environmental sustainability. The integration of EQ into AI use is pivotal in using AI to improve educational outcomes and health services, as emphasized by UNESCO and UNICEF's significant initiatives. This paper highlights the evolving role of AI in understanding and managing human emotions, particularly in personalizing education and healthcare. It proposes that the ethical use of AI, combined with EQ principles, has the power to transform societal interactions and decision-making processes, leading to a more inclusive, sustainable, and healthier global community. Furthermore, this paper considers the ethical dimensions of AI deployment, guided by UNESCO's recommendations on AI ethics, which advocate for transparency, accountability, and inclusivity in AI developments. It also examines the World Economic Forum's insights into AI's potential to revolutionize learning and healthcare in underserved populations, emphasizing the significance of fair AI advancements. By integrating perspectives from prominent global organizations, this paper offers a strategic approach to combining AI with EQ, enhancing the capacity of AI systems to meaningfully address global challenges. In conclusion, this paper advocates for the establishment of a new Sustainable Development Goal, SDG 18, focused on the ethical integration of AI and EQ across all sectors, ensuring that technology advances the well-being of humanity and global sustainability. [ABSTRACT FROM AUTHOR]

This comprehensive review of concept-supported interpretation methods in Explainable Artificial Intelligence (XAI) navigates the multifaceted landscape. As machine learning models become more complex, there is a greater need for interpretation methods that deconstruct their decision-making processes. Traditional interpretation techniques frequently emphasise lower-level attributes, resulting in a schism between complex algorithms and human cognition. To bridge this gap, our research focuses on concept-supported XAI, a new line of research in XAI that emphasises higher-level attributes or 'concepts' that are more aligned with end-user understanding and needs. We provide a thorough examination of over twenty-five seminal works, highlighting their respective strengths and weaknesses. A comprehensive list of available concept datasets, as opposed to training datasets, is presented, along with a discussion of sufficiency metrics and the importance of robust evaluation methods. In addition, we identify six key factors that influence the efficacy of concept-supported interpretation: network architecture, network settings, training protocols, concept datasets, the presence of confounding attributes, and standardised evaluation methodology. We also investigate the robustness of these concept-supported methods, emphasising their potential to significantly advance the field by addressing issues like misgeneralization, information overload, trustworthiness, effective human-AI communication, and ethical concerns. The paper concludes with an exploration of open challenges such as the development of automatic concept discovery methods, strategies for expert-AI integration, optimising primary and concept model settings, managing confounding attributes, and designing efficient evaluation processes. [ABSTRACT FROM AUTHOR]

The accounting profession is undergoing significant transformation due to advancements in Artificial Intelligence (AI). This study evaluates AI's impact on traditional accounting roles, identifies emerging career opportunities, and examines the academic challenges of integrating AI into accounting education. AI technologies such as machine learning, robotic process automation (RPA), and data analytics are revolutionizing accounting practices by automating routine tasks, improving accuracy, and enabling strategic decision-making. Emerging roles, including AI auditors and ethical AI specialists, require a blend of traditional accounting expertise and advanced AI skills. The study highlights the need for effective pedagogical approaches, continuous faculty development, comprehensive ethical education frameworks, innovative assessment methods, and seamless integration of AI coursework with the core curriculum. By addressing these challenges, the study proposes strategies to equip future accountants with the necessary skills to thrive in an AI-enhanced environment, ensuring the profession remains relevant, competitive, and ethically grounded amidst ongoing technological advancements. [ABSTRACT FROM AUTHOR]

The rapid advancement of artificial intelligence (AI) is reshaping employee engagement strategies within human resource management (HRM). This review examines how AI technologies are transforming traditional HR practices to foster a more personalized and dynamic employee experience. AI-driven tools, such as predictive analytics, chatbots, and machine learning algorithms, are increasingly utilized to enhance recruitment, onboarding, performance management, and employee feedback mechanisms. By analyzing real-time data, AI provides HR professionals with actionable insights into workforce sentiment and productivity, enabling more effective decision-making and timely interventions. AI's role in personalizing employee engagement is particularly notable, as it enables organizations to tailor communication, recognition, and professional development opportunities to individual employee preferences and behaviors. This paper highlights how AI can automate repetitive administrative tasks, freeing HR personnel to focus on strategic initiatives that drive engagement and job satisfaction. Moreover, the integration of AI with employee engagement platforms offers new possibilities for continuous feedback and sentiment analysis, creating a more agile and responsive work environment. However, while AI holds significant potential, its implementation also raises concerns regarding data privacy, ethical considerations, and potential biases embedded in algorithms. The review addresses these challenges and explores how organizations can mitigate risks while leveraging AI to enhance employee engagement effectively. By synthesizing the latest research and case studies, this paper provides a comprehensive understanding of AI's transformative impact on HRM, offering insights into future trends and best practices for optimizing employee engagement through AI technologies. [ABSTRACT FROM AUTHOR]

Artificial Intelligence (AI) holds significant potential to reshape educational systems by enabling personalized learning, automating administrative tasks, and offering real-time assessments. This paper presents a bibliometric analysis of recent studies on AI in education, capturing key trends, methodologies, challenges, and future directions within the knowledge "library" of this evolving field. Through the analysis of 51 papers, the research explores how AI enhances both teaching and learning, along with the ethical implications surrounding its adoption. The study highlights the benefits and barriers, such as the need for equitable access to AI tools, addressing algorithmic biases, and ensuring proper teacher training. Future research directions are also outlined, including the development of ethical AI frameworks, conducting longitudinal studies, and AI's role in fostering inclusive education. [ABSTRACT FROM AUTHOR]

The rapid development of computer vision technologies and applications has brought forth a range of social and ethical challenges. Due to the unique characteristics of visual technology in terms of data modalities and application scenarios, computer vision poses specific ethical issues. However, the majority of existing literature either addresses artificial intelligence as a whole or pays particular attention to natural language processing, leaving a gap in specialized research on ethical issues and systematic solutions in the field of computer vision. This paper utilizes bibliometrics and text-mining techniques to quantitatively analyze papers from prominent academic conferences in computer vision over the past decade. It first reveals the developing trends and specific distribution of attention regarding trustworthy aspects in the computer vision field, as well as the inherent connections between ethical dimensions and different stages of visual model development. A life-cycle framework regarding trustworthy computer vision is then presented by making the relevant trustworthy issues, the operation pipeline of AI models, and viable technical solutions interconnected, providing researchers and policymakers with references and guidance for achieving trustworthy CV. Finally, it discusses particular motivations for conducting trustworthy practices and underscores the consistency and ambivalence among various trustworthy principles and technical attributes. [ABSTRACT FROM AUTHOR]

Artificial intelligence-enabled business models aim to improve decision-making, operational efficiency, innovation, and productivity. The presented systematic literature review is conducted to highlight elucidating the utilization of artificial intelligence (AI) methods and techniques within AI-enabled businesses, the significance and functions of AI-enabled organizational models and frameworks, and the design parameters employed in academic research studies within the AI-enabled business domain. We reviewed 39 empirical studies that were published between 2010 and 2023. The studies that were chosen are classified based on the artificial intelligence business technique, empirical research design, and SLR search protocol criteria. According to the findings, machine learning and artificial intelligence were reported as popular methods used for business process modelling in 19% of the studies. Healthcare was the most experimented business domain used for empirical evaluation in 28% of the primary research. The most common reason for using artificial intelligence in businesses was to improve business intelligence. 51% of main studies claimed to have been carried out as experiments. 53% of the research followed experimental guidelines and were repeatable. For the design of business process modelling, eighteen AI mythology were discovered, as well as seven types of AI modelling goals and principles for organisations. For AI-enabled business models, safety, security, and privacy are key concerns in society. The growth of AI is influencing novel forms of business. [ABSTRACT FROM AUTHOR]

The article focuses on the development of an Artificial intelligence (AI) policy framework for Africa through the publication of a draft White Paper and the AI Continental Roadmap by the African Union's Development Agency. Topics include the identification of six strategic pillars for AI development, the need for substantial investment in human capital and infrastructure, and the importance of fostering collaboration among diverse stakeholders.

IntroductionThe 2024 Voice AI Symposium, presented by the Bridge2AI-Voice Consortium, featured deep-dive educational workshops conducted by experts from diverse fields to explore the latest advancements in voice biomarkers and artificial intelligence (AI) applications in healthcare. Through five workshops, attendees learned about topics including international standardization of vocal biomarker data, real-world deployment of AI solutions, assistive technologies for voice disorders, best practices for voice data collection, and deep learning applications in voice analysis. These workshops aimed to foster collaboration between academia, industry, and healthcare to advance the development and implementation of voice-based AI tools.MethodsEach workshop featured a combination of lectures, case studies, and interactive discussions. Transcripts of audio recordings were generated using Whisper (Version 7.13.1) and summarized by ChatGPT (Version 4.0), then reviewed by the authors. The workshops covered various methodologies, from signal processing and machine learning operations (MLOps) to ethical concerns surrounding AI-powered voice data collection. Practical demonstrations of AI-driven tools for voice disorder management and technical discussions on implementing voice AI models in clinical and non-clinical settings provided attendees with hands-on experience.ResultsKey outcomes included the discussion of international standards to unify stakeholders in vocal biomarker research, practical challenges in deploying AI solutions outside the laboratory, review of Bridge2AI-Voice data collection processes, and the potential of AI to empower individuals with voice disorders. Additionally, presenters shared innovations in ethical AI practices, scalable machine learning frameworks, and advanced data collection techniques using diverse voice datasets. The symposium highlighted the successful integration of AI in detecting and analyzing voice signals for various health applications, with significant advancements in standardization, privacy, and clinical validation processes.DiscussionThe symposium underscored the importance of interdisciplinary collaboration to address the technical, ethical, and clinical challenges in the field of voice biomarkers. While AI models have shown promise in analyzing voice data, challenges such as data variability, security, and scalability remain. Future efforts must focus on refining data collection standards, advancing ethical AI practices, and ensuring diverse dataset inclusion to improve model robustness. By fostering collaboration among researchers, clinicians, and technologists, the symposium laid a foundation for future innovations in AI-driven voice analysis for healthcare diagnostics and treatment.