AI Challenges Research Articles

Navigating ethical challenges of explainable ai in autonomous systems

The rapid integration of autonomous systems, such as vehicles, drones, and robots, into various sectors brings forth significant ethical challenges concerning their decision-making processes. This paper examines the role of Explainable AI (XAI) in addressing these challenges, particularly regarding accountability in the event of accidents and the necessity of human oversight in automated environments. We discuss the critical ethical implications of transparency, emphasizing how XAI can bridge the gap between complex algorithmic decision-making and public understanding, thereby fostering trust in these technologies. The paper also outlines the current regulatory frameworks for AI safety, analysing their effectiveness in promoting responsible innovation. Furthermore, we investigate the consequences of opaque algorithms, especially in life-critical applications where the stakes are exceptionally high. Through an analysis of case studies, we showcase how organizations have successfully implemented XAI to enhance safety measures and uphold ethical responsibility in their autonomous systems. Ultimately, this study advocates for the integration of XAI as a vital component in developing responsible autonomous technologies, ensuring accountability, and safeguarding public trust in an era increasingly defined by automation.

Leveraging AI Tools for Discourse Analysis in Early Childhood Bilingual Education: Enhancing Language Development

The implementation of AI tools in education has become increasingly popular and brought transformative potential, particularly in the field of language education because of its powerful functions on designing immersive and personalized activities and offering real-time feedback. This study explores the role of AI tools in discourse analysis and the impact on enhancing bilingual skills among early childhood students. It investigates two key questions: (1) How do AI tools impact young childrens bilingual skills in early childhood classrooms? (2) How can discourse analysis of AI tools assist educators with bilingual teaching? A qualitative case study, including an in-depth interview with a bilingual educator, reveals insights into the effectiveness of AI tools in promoting bilingual language acquisition and engaging young learners in meaningful and enjoyable learning environment. Furthermore, the discourse analysis of AI tools offers immediate, actionable insights on each students academic performance, helping educators better understand the language learning process and the specific challenges students face. However, considerations and challenges of AI in education, such as privacy risks and misuse, still need to be considered. The findings of this study bridge the gap and highlight the potential for discourse analysis of AI tools in early childhood bilingual education. Further study can focus on exploring other AI tools in bilingual education and conducting long-term research with a large example size.

Activating Our Intelligence

How can we sift through the AI challenges and create a balanced approach drawing on the library’s strengths? This column presents a reflection on how we can inspire and foster our intelligence and potential to discern useful information about AI and our use of it.

Artificial Intelligence: Opportunities and Challenges for Public Administration

AbstractArtificial intelligence (AI) could be at the heart of a fourth revolution by significantly changing professional identity, relationships between individuals and resource management. To help public administration practitioners and researchers cope with changes anticipated by AI, this research note maps existing conceptual approaches on the opportunities and challenges of AI in public administration and proposes a conceptual framework and initiatives that can inform and support effective, public interest‐oriented technology policymaking. This research note outlines a potential research agenda, with multi‐level opportunities and challenges, to accelerate this critical work.

Cross-pollination of knowledge for object detection in domain adaptation for industrial automation

AbstractArtificial Intelligence is revolutionizing industries by enhancing efficiency through real-time Object Detection (OD) applications. Utilizing advanced computer vision techniques, OD systems automate processes, analyze complex visual data, and facilitate data-driven decisions, thus increasing productivity. Domain Adaptation for OD has recently gained prominence for its ability to recognize target objects without annotations. Innovative approaches that merge traditional cross-disciplinary domain modeling with cutting-edge deep learning have become essential in addressing complex AI challenges in real-time scenarios. Unlike traditional methods, this study proposes a novel, effective Cross-Pollination of Knowledge (CPK) strategy for domain adaptation inspired by botanical processes. The CPK approach involves merging target samples with source samples at the input stage. By incorporating a random and unique selection of a few target samples, the merging process enhances object detection results efficiently in domain adaptation, supporting detectors in aligning and generalizing features with the source domain. Additionally, this work presents the new Planeat digit recognition dataset, which includes 231 images. To ensure robust comparison, we employ a self-supervised Domain Adaptation (UDA) method that simultaneously trains target and source domains using unsupervised techniques. UDA method leverages target data to identify high-confidence regions, which are then cropped and augmented, adapting UDA for effective OD. The proposed CPK approach significantly outperforms existing UDA techniques, improving mean Average Precision (mAP) by 10.9% through rigorous testing on five diverse datasets across different conditions- cross-weather, cross-camera, and synthetic-to-real. Our code is publicly available https://github.com/anwaar0/CPK-Object-Detection

The Future of AI Governance: Navigating the Challenges of Generative AI

The Future of AI Governance: Navigating the Challenges of Generative AI

AI Governance in Healthcare: Best Practices, Solutions, and Unresolved Issues

What challenges are presented by AI adoption in healthcare? What role should AI governance play in regard to the opportunities and risks for generative AI (large language models like ChatGPT-4, etc.) in health? How do we responsibly evaluate new technology and ensure guardrails for its deployment, but without stifling innovation? How do we address societal fears and biases? What level of reliability and robustness should algorithms achieve before being adopted with confidence across healthcare? How can AI support health equity and expand access? We will explore these questions and more with some of the nation’s leading experts on these topics. Learning Objectives Understand key opportunities and challenges for AI governance in healthcare, particularly in regard to rapidly emerging generative AI applications Discuss various responsible AI and AI governance frameworks that seek to ensure safety and efficacy in AI adoption Explore the role of government, legislation, and regulators in supporting safe and equitable adoption of AI in healthcare Explore opportunities for government, industry, and academic partnerships to accelerate AI validation, development, and adoption of safety standards Consider the potential impact of AI on the healthcare workforce and provider and patient experiences Identify the risks we should be most concerned with and what approaches can we take to mitigate those risks? By the end of the presentation, participants should have a sense of the opportunities and challenges of adopting AI in healthcare, key risks and frameworks for mitigating them, and the potential role of government, academia, and industry in addressing these issues Generative AI application examples Patient Communication and Engagement Intelligent chatbots interacting with patients, answering their queries, providing health advice, and helping them manage their conditions Mental Health Support Provide psychological support to patients, helping them manage stress, anxiety, depression, and other mental health issues. Provide interventions based on CBT and other therapeutic techniques Automated Medical Documentation Note-taking can allowing doctors to focus more on patient care Education and Training Create educational content and personalized training for medical students and healthcare professionals, potentially simulating things like patient cases and providing guidance on diagnosis, etc. Synthesis and Summarization of Medical Research Make it easier for people to keep up with the latest developments Public Health Communication In a public health emergency, help craft health messages, FAQs, and timely and accurate updates for the public Generative AI challenges and limitations Ensuring the accuracy of AI-generated information Maintaining patient privacy Handling the ethical implications of AI and human-machine interactions in healthcare “Hallucinations” (false or inaccurate information) can occur when model has limited knowledge of a particular domain “Distraction” (losing track of earlier inputs) can occur when inputs go beyond allowable token count for model “context window” and models start to produce responses not relevant to queries and context.

The promise and challenges of generative AI in education

ABSTRACT Generative artificial intelligence (GenAI) tools, such as large language models (LLMs), generate natural language and other types of content to perform a wide range of tasks. This represents a significant technological advancement that poses opportunities and challenges to educational research and practice. This commentary brings together contributions from nine experts working in the intersection of learning and technology and presents critical reflections on the opportunities, challenges, and implications related to GenAI technologies in the context of education. In the commentary, it is acknowledged that GenAI’s capabilities can enhance some teaching and learning practices, such as learning design, regulation of learning, automated content, feedback, and assessment. Nevertheless, we also highlight its limitations, potential disruptions, ethical consequences, and potential misuses. The identified avenues for further research include the development of new insights into the roles human experts can play, strong and continuous evidence, human-centric design of technology, necessary policy, and support and competence mechanisms. Overall, we concur with the general skeptical optimism about the use of GenAI tools such as LLMs in education. Moreover, we highlight the danger of hastily adopting GenAI tools in education without deep consideration of the efficacy, ecosystem-level implications, ethics, and pedagogical soundness of such practices.

Toward an Explainable Large Language Model for the Automatic Identification of the Drug-Induced Liver Injury Literature.

Drug-induced liver injury (DILI) stands as a significant concern in drug safety, representing the primary cause of acute liver failure. Identifying the scientific literature related to DILI is crucial for monitoring, investigating, and conducting meta-analyses of drug safety issues. Given the intricate and often obscure nature of drug interactions, simple keyword searching can be insufficient for the exhaustive retrieval of the DILI-relevant literature. Manual curation of DILI-related publications demands pharmaceutical expertise and is susceptible to errors, severely limiting throughput. Despite numerous efforts utilizing cutting-edge natural language processing and deep learning techniques to automatically identify the DILI-related literature, their performance remains suboptimal for real-world applications in clinical research and regulatory contexts. In the past year, large language models (LLMs) such as ChatGPT and its open-source counterpart LLaMA have achieved groundbreaking progress in natural language understanding and question answering, paving the way for the automated, high-throughput identification of the DILI-related literature and subsequent analysis. Leveraging a large-scale public dataset comprising 14 203 training publications from the CAMDA 2022 literature AI challenge, we have developed what we believe to be the first LLM specialized in DILI analysis based on LLaMA-2. In comparison with other smaller language models such as BERT, GPT, and their variants, LLaMA-2 exhibits an enhanced out-of-fold accuracy of 97.19% and area under the ROC curve of 0.9947 using 3-fold cross-validation on the training set. Despite LLMs' initial design for dialogue systems, our study illustrates their successful adaptation into accurate classifiers for automated identification of the DILI-related literature from vast collections of documents. This work is a step toward unleashing the potential of LLMs in the context of regulatory science and facilitating the regulatory review process.

System Restructuring, Value Expansion, and New Challenges: AI Era Journalism Practice Transformation and Challenges

System Restructuring, Value Expansion, and New Challenges: AI Era Journalism Practice Transformation and Challenges

WILL AI REPLACE HUMAN JOBS IN THE FILM PRODUCTION?

Artificial Intelligence is utilized in multiple steps of film production such as script writing, Pre-production works, identifying locations, casting the actors, calculating the budget of the film, Box-office performance, Demographics of the audience, enhancing VFX, animations, CGI, music and Post-production works (Editing, colouring, sound design). This article explores the arrival of AI in film production, popular filmmaker’s and artists’ opinions on AI, how AI benefits and challenges human jobs in the Film industry, and AI which are used in every stage of filmmaking. This article elucidates about movies generated through AI, how popular movie production companies like 20th Century Fox, and Warner Bros. use AI as a tool to enhance their films, AI which are used to generate films, and jobs that AI is unlikely to replace. This article concludes by stating how humans should use AI in order to sustain in this era of AI.

The coevolution of technology, markets, and culture: the challenging case of AI

AbstractArtificial intelligence (AI) is at the center of economic, social, political, and ethical debate. For the first time in human history, since the appearance of writing, a new type of “intelligence” is impacting the very nature of social relationships. New technologies are not only changing the operating model of markets and value chains in an accelerated way but also in how information is generated and processed, how labor, social relations, and culture evolve, etc. In this paper, we pursue two main goals. First, to set up an analytical framework in which cognitive, technical, and cultural dynamics are intertwined with the processes of deployment of economic action. Secondly, we utilize this framework to explore some key features and challenges of AI and its impact on coevolutionary processes at the cognitive, market, and cultural levels. We devote special attention to the consequences that AI may have for the concept of (economic) rationality and the formation of action plans. We conclude that AI will massively enhance agents’ spaces of action by improving efficiency and exploring new possibilities. However, AI will not change the nature of human action and the structure of the evolution of the economic system.

Transforming higher education in Tanzania: unleashing the true potential of AI as a transformative learning tool

PurposeThis study aims to explore AI’s potential impact on the broader landscape of higher education in Tanzania. This study contributes to the ongoing discussion of AI’s potential to transform higher education and highlights the ethical considerations and challenges that must be addressed to ensure its successful implementation. This study informs future research and policy decisions in education and technology by providing a detailed understanding of AI’s perceived benefits and challenges in higher education.Design/methodology/approachA mixed-methods approach was used, which involves collecting and analyzing quantitative and qualitative data to understand the research problem comprehensively. This approach allowed data triangulation and led to a more robust and detailed understanding of this study.FindingsIn this study, it was discovered that enhanced assessment, time-saving, personalized learning, improved accessibility and detecting cheating are the perceived benefits of AI as a tool for enhancing learning in higher education, while cost and infrastructure, academic misconduct, data privacy and security, bias and ethical concerns and lack of human interaction are the perceived challenges of AI as a tool for enhancing learning in higher education. Further, it was revealed that students are more accepting of using AI tools in the classroom because they think they are more effective and engaging. On the other hand, faculty were more cautious and skeptical about employing AI tools in the classroom because they worried about how it would affect their teaching methods and job security.Research limitations/implicationsThe data collection was not conducted face-to-face. To fully capture respondents’ emotional responses, feelings, facial expressions, reactions, or body language was challenging. However, a sufficient number of individuals who participated were very cooperative, and their knowledge was very beneficial in understanding the topic.Originality/valueA unique view of this study is a clear understanding of the perceived benefits and challenges of using AI as a tool for enhancing learning in higher education, as well as the variations in these perceptions among students and faculty. By examining the perspectives of both groups, this study provides a comprehensive understanding of the complex role of AI in higher education. Understanding the broader implications of AI in higher education can inform policy decisions and ensure that AI is used responsibly and ethically.

Benefits of Artificial Intelligence and its Innovation in Organizations

This article reviews the advances, benefits and contributions of artificial intelligence to business organizations, without ignoring the need for skilled human labor and talent that companies have always required and which is essential for the economic development of a company. The implementation of legal frameworks and regulations that will in turn address issues such as organizational privacy, cybersecurity and liability are important aspects to be addressed in this article. Thus, this paper focuses on the compilation of scientific articles, books and other texts in which important topics were evidenced such as (i). - Innovation and invention, (ii). - Big Data, (iii). -Implementation of artificial intelligence in organizations, and (iv). -legality and responsibility of decisions, likewise, it examines the legal challenges of AI and highlights that smart contracts are a technological innovation that allows secure, fast and low-risk transactions using blockchain technology, this is why the literature review aims to explore how technology, artificial intelligence and communication tools can facilitate the work of the population, decreasing the time spent on some activities

Generative AI And New Challenges For Public Leaders: The Implications For Citizen Communications And Foundations Of Government Leadership

This brief commentary describes some of the less discussed implications of generative artificial intelligence (GenAI) for changes in government communications, citizen experiences and the very foundations of government. It focuses on the current and developing capabilities of artificial intelligence to handle and generate text and images in a way that seems virtually human.

Smart Media or Biased Media: The Impacts and Challenges of AI and Big Data on the Media Industry

Smart Media or Biased Media: The Impacts and Challenges of AI and Big Data on the Media Industry

New technologies in the face of AI challenges

New technologies in the face of AI challenges

Navigating Ethical and Governance Challenges in AI: Finance

Navigating Ethical and Governance Challenges in AI: Finance

Navigating the Digital Battlefield: AI's Impact and Challenges in Cybersecurity

Navigating the Digital Battlefield: AI's Impact and Challenges in Cybersecurity

Scan-GPT: Bridging Visual Input and Textual Output for Question Solution

The ScanGPT project represents an innovative approach to leveraging the power of advanced language models, specifically OpenAI's GPT-3.5, in conjunction with OCR technology, to provide users with a comprehensive platform for obtaining information. In a rapidly advancing landscape of artificial intelligence, ScanGPT serves as a bridge between textual and non-textual content, allowing users to interact with multiple sources and receive clear and accurate responses. Traditional search methods often fall short when dealing with non-textual content such as images containing valuable information. ScanGPT addresses these limitations by combining OCR technology with advanced language models to deliver precise answers based on both text and image inputs. This paper presents the architecture, functionality, and methodology of ScanGPT, highlighting its role in meeting the diverse needs of users seeking information. The proposed system architecture seamlessly integrates text and image processing capabilities, leveraging existing technologies such as ChatGPT, Microsoft Azure OCR, and the OpenAI API. Through a modular design and rigorous security and privacy measures, ScanGPT ensures scalability, flexibility, and user confidentiality. The role of HTML, CSS, and JavaScript in the user interface design is explored, emphasizing the importance of intuitive interfaces and dynamic capabilities in enhancing user experience. Additionally, existing solutions and challenges in conversational AI are reviewed, providing insights into the evolving landscape of AI-powered interactions. The proposed system architecture of ScanGPT offers a robust, scalable, and flexible solution for conversational AI, enabling users to interact with AI systems using both text and image inputs. By seamlessly integrating text and image processing capabilities, ScanGPT aims to redefine the boundaries of conversational AI platforms, providing users with a comprehensive and user-friendly experience. Future scope and potential advancements in conversational AI are also discussed, highlighting opportunities for integrating additional sensory inputs, personalization, and scalability. Through ongoing improvements in ethical AI considerations and linguistic capabilities, ScanGPT aims to remain a trustworthy and globally accessible technology, fostering wider adoption and cultural inclusivity in AI-driven interactions. Overall, ScanGPT represents a significant step forward in harnessing the power of advanced language models and OCR technology to provide users with accurate, contextually relevant information from diverse sources, paving the way for innovative solutions to everyday problems in the era of artificial intelligence.