Digital Commons Network^™

The Common Threats Of Artificial Intelligence And Privatization, Landyn Rookard

Texas A&M Law Review

Administrative agencies’ growing use of automated decisionmaking tools poses threats to core democratic values, such as agency flexibility, expertise, fairness, transparency, and accountability. But decades of privatization have wrought similar, lasting harms to the United States’ public institutions. This Article argues that the thoughtful criticisms and prescriptions from the burgeoning literature on the government’s use of artificial intelligence should be used to strengthen the scrutiny accorded to privatization.

Specifically, this Article challenges the perception that automated decisionmaking poses a greater threat to public values than privatization. Indeed, the two share several characteristics and goals. These include, for example, a fixation …

The Human Complementary Usage Of Ai And Ml For Fair And Unbiased Educational Assessments, Hayri Eren Suna, Mahmut Özer

Chinese/English Journal of Educational Measurement and Evaluation | 教育测量与评估双语期刊

In recent years, artificial intelligence (AI) and machine learning (ML) algorithms have played an influential role in advancing educational assessment. As a means of improving equal opportunities in education, assessing students' learning deficiencies and developing personalized learning suggestions is considered an important aspect. Furthermore, big data-based algorithms play an increasing role in assessing students' cognitive and social-emotional development and conducting research on school-based monitoring. A number of these developments, on the one hand, provide valuable feedback to both students and educational authorities regarding assessment of student growth, and on the other hand, they raise the issue of bias in the …

The Role Of Artificial Intelligence In Transforming Physical And Online Fashion Retail: Enhancing Experiences, Driving Sustainability, And Fostering Innovation, Andrew Burnstine

Faculty and Staff Publications & Presentations

This study explores the transformative role of artificial intelligence (AI) in revolutionizing the fashion industry, with a focus on enhancing consumer experiences, promoting sustainability, and driving innovation in retail. It examines AI applications in personalized recommendations, virtual try-ons, and supply chain optimization, while also addressing societal implications. Sustainability is a central theme, highlighting how AI minimizes overproduction, enables circular fashion, and encourages conscious consumerism. Case studies, such as Nike’s AI-powered retail stores and Lynn University’s Surreal Fashion Show, demonstrate practical applications and innovations during the COVID-19 pandemic. This research synthesizes insights from reports by The Business of Fashion and McKinsey …

The Future Of Ai: Join The Conversation, Jennifer Wojton, Cassandra Branham, Vijay Tummala, Laxima Niure Kandel, Kayla D. Taylor

Publications

Join the Conversation! The Future is AI? There is so much conflicting information about what AI is capable of, how it could/should be used, by whom and for what purpose. In this panel discussion, we hope to provide a baseline of information that will help all participants think critically and articulate thoughtful questions about the mechanics of AI, ethical use or non-use of AI in particular contexts (school, industry, business, art, etc.), and the impacts we are currently experiencing or are likely to experience. Hear from ERAU faculty of different disciplines to discuss what the current state of AI technology …

If You Were A Sesame Street Character, Which One Would You Be? Natural Language Processing And Personality With Big Bird And Friends, Joseph Uran Meyer

Doctoral Dissertations

This paper examined and compared several natural language processing and machine learning techniques in predicting self-reported Big Five personality traits from text responses. The models were validated on the open-source 2019 SIOP Machine Learning Competition dataset (N = 1,689). The techniques evaluated included bag-of-words, Empath dictionary, LSTM networks, fine-tuning Transformer models, and stacked generalization. Results indicated that the present study’s models had lower error in four of the five constructs analyzed. Limitations of the study include use of an MTurk sample and small sample size. Future research should explore similar techniques on larger applicant samples. Practical implications and contributions to …

Artificial Intelligence In Higher Education Accreditation: Advancing Quality, Accessibility, And Special Education Inclusion, Jacques D. Singleton

Teacher Education Faculty Publications

Artificial Intelligence (AI) is transforming higher education accreditation by enhancing quality assurance, accessibility, and inclusion. Traditional accreditation relies on manual evaluation, but AI- driven tools improve efficiency through realtime data analysis, predictive insights, and automation. This chapter explores AI’s role in adaptive learning, automated accessibility tools, and predictive analytics for student success, with a focus on supporting students with disabilities and ensuring compliance with accessibility standards. Ethical concerns, including bias, privacy, and transparency, are addressed to ensure AI complements human judgment. Through case studies and policy recommendations, this chapter provides a roadmap for institutions and accrediting bodies to integrate AI …

Why Ai’S Role In Advancing Sustainability Is Underestimated, Lipika Bhattacharya

CCX Research

AI has quietly, but powerfully, woven itself into the fabric of our everyday lives. Yet, AI's potential impact on creating a more sustainable world is undervalued. The author examined artificial intelligence's (AI) role in advancing sustainability. She outlined how AI can be applied in various domains such as agriculture, water management, industry, urban planning, and biodiversity conservation for transformative effects.

Leveraging Artificial Intelligence And Machine Learning To Accelerate Discovery Of Disease-Modifying Therapies In Type 1 Diabetes., Melanie R. Shapiro, Erin M. Tallon, Matthew E. Brown, Amanda L. Posgai, Mark A. Clements, Todd M. Brusko

Manuscripts, Articles, Book Chapters and Other Papers

Progress in developing therapies for the maintenance of endogenous insulin secretion in, or the prevention of, type 1 diabetes has been hindered by limited animal models, the length and cost of clinical trials, difficulties in identifying individuals who will progress faster to a clinical diagnosis of type 1 diabetes, and heterogeneous clinical responses in intervention trials. Classic placebo-controlled intervention trials often include monotherapies, broad participant populations and extended follow-up periods focused on clinical endpoints. While this approach remains the 'gold standard' of clinical research, efforts are underway to implement new approaches harnessing the power of artificial intelligence and machine learning …

Learning: Human Versus Machine, Sanjay Sarma

Asian Management Insights

Outdated education paradigms must be revamped to reclaim the all-important human quality: agency.

Sanjay Sarma, CEO, President, and Dean of the Asia School of Business, Kuala Lumpur, Malaysia and the Fred Fort Flowers (1941) and Daniel Fort Flowers (1941) Professor in Mechanical Engineering at the Massachusetts Institute of Technology (MIT), shares insights on the artificial intelligence (AI)-agency revolution and how the human brain works.

The Ethics Of Ai Nudges: How Ai Influences Decision-Making, Seema Chokshi

Asian Management Insights

And why decision-makers should care about it. Artificial intelligence (AI) systems, through mechanisms like nudges and choice architecture, actively, yet often subtly, shape human decision-making in everyday life and professional settings. AI systems can prioritise profit or efficiency at the expense of human agency, fairness, and well-being, highlighting the need to balance AI’s capabilities with ethical considerations. The EU AI Act is a landmark framework designed to curb manipulative AI practices, emphasising the protection of human autonomy and accountability in decision-making.

Exploring Ai Technology In Grammar Performance Testing For Children With Learning Disabilities, Dimitra V. Katsarou, Evangelos Mantsos, Soultana Papadopoulou, Maria Sofologi, Efthymia Efthymiou, Ilias Vasileiou, Kalliopi Megari, Maria Theodoratou, Georgios A. Kougioumtzis

All Works

The study explores the application of artificial intelligence (AI) in addressing grammar challenges among children with learning disabilities, aiming to assess the efficacy of an AI-driven tool for personalized interventions. A sample of 100 children aged 8–12, diagnosed with learning disabilities, was recruited from special education programs. Participants were divided into an experimental group (n = 50), which used an AI-based grammar assessment tool with personalized feedback, and a control group (n = 50), which completed conventional paper-based grammar tests without feedback. The AI tool administered adaptive grammar tasks, including sentence correction and verb conjugation, and performance was evaluated over …

My Gai Study Partner For Odes, Milena C. Cuellar

Open Educational Resources

My GAI Study Partner for ODEs is a scaffolded sequence of learning activities designed to support students in an Ordinary Differential Equations (ODEs) course by integrating the use of Generative AI tools—specifically Copilot, a platform accessible to all CUNY students and faculty. The activities are rooted in experiential learning, flipped classroom strategies, and self-directed learning principles. The sequence begins with two preparatory activities that introduce students to the ethical use and foundational concepts of Generative AI, fostering digital literacy and critical reflection. These are followed by a series of main activities—initially structured (Activity 3) and later more flexible …

Smart Highway Construction Site Monitoring Using Artificial Intelligence, Mehran Mazari, Yahaira Nava-Gonzalez, Ly Jacky N. Nhiayi, Mohamad H. Saleh

Mineta Transportation Institute

Construction is a large sector of the economy and plays a significant role in creating economic growth and national development,and construction of transportation infrastructure is critical. This project developed a method to detect, classify, monitor, and track objects during the construction, maintenance, and rehabilitation of transportation infrastructure by using artificial intelligence and a deep learning approach. This study evaluated the performance of AI and deep learning algorithms to compare their performance in detecting and classifying the equipment in various construction scenes. Our goal was to find the optimized balance between the model capabilities in object detection and memory processing requirements. …

Gender Biases Within Artificial Intelligence And Chatgpt: Evidence, Sources Of Biases, And Solutions, Qi Hui Jerlyn Ho, Andree Hartanto, Andrew Koh, Nadyanna M. Majeed

Research Collection School of Social Sciences

The growing adoption of Artificial Intelligence (AI) in various sectors has introduced significant benefits, but also raised concerns over biases, particularly in relation to gender. Despite AI's potential to enhance sectors like healthcare, education, and business, it often mirrors reality and its societal prejudices and can manifest itself through unequal treatment in hiring decisions, academic recommendations, or healthcare diagnostics, systematically disadvantaging women. This paper explores how AI systems and chatbots, notably ChatGPT, can perpetuate gender biases due to inherent flaws in training data, algorithms, and user feedback loops. This problem stems from several sources, including biased training datasets, algorithmic design …

Patient Consent And The Right To Notice And Explanation Of Ai Systems Used In Health Care, Meghan E Hurley, Benjamin H Lang, Kristin Marie Kostick-Quenet, Jared N Smith, Jennifer Blumenthal-Barby

Center for Medical Ethics and Health Policy Staff Publications

Given the need for enforceable guardrails for artificial intelligence (AI) that protect the public and allow for innovation, the U.S. Government recently issued a Blueprint for an AI Bill of Rights which outlines five principles of safe AI design, use, and implementation. One in particular, the right to notice and explanation, requires accurately informing the public about the use of AI that impacts them in ways that are easy to understand. Yet, in the healthcare setting, it is unclear what goal the right to notice and explanation serves, and the moral importance of patient-level disclosure. We propose three normative functions …

Methodology Of Automated Control Of Situations In Structurally Complex Systems With Recycled Flows, Khusan Zokirovich Igamberdiev Academician, Madina Mirxalilovma Fozilova

Chemical Technology, Control and Management

This article discusses the methodology of automated management of situations in structurally complex systems with recycled flows. The need to develop a correct model for effectively controlling such systems based on scientific principles of analysis and decision-making is substantiated. Particular attention is paid to modeling, optimizing, and using digital technologies, including artificial intelligence, to improve the accuracy and efficiency of management decisions. The principles of decomposition, multi-criteria optimization, and linguistic models of fuzzy correspondence are described, which can be used in conditions of uncertainty and complexity of industrial facilities.

Human-Ai Collaboration In Writing: A Multidimensional Framework For Creative And Intellectual Authorship, James Hutson

Faculty Scholarship

The integration of AI technologies into the writing process has significantly altered traditional notions of authorship, creativity, and intellectual labor. Historically, writing was seen as a human-driven cognitive and creative exercise, but with the rise of generative AI tools such as ChatGPT and Claude, the line between human and AI contributions has become increasingly ambiguous. This paper addresses the limitations of the current sliding scale model, which views AI involvement as ranging from “none” to “complete”. In its place, we propose a new multidimensional framework that more accurately reflects the complexity of human-AI collaboration in writing. The model includes axes …

Governing Generative Ai, Daryl Lim

Akron Law Review

This Article paper explores the evolving landscape of copyright law in the age of generative artificial intelligence, dissecting the multifaceted challenges and opportunities it presents for creators, legal practitioners, and policymakers. Part I, “Governing Authorship,” delves into the existential questions AI poses to traditional notions of creativity and authorship, illustrating the tension between innovation and the preservation of human creators’ rights within existing legal frameworks. Part II, “Governing Infringement,” examines the intricate issues of copyright infringement in the digital era, especially the unauthorized use of copyrighted content by AI for training purposes and the exploration of private ordering mechanisms as …

Using Ai To Make Accessible Accessible Content, Melinda Turner

Faculty Other Scholarly Works

This professional development session will explore how to leverage AI tools, specifically Google’s NotebookLM, to create accessible learning materials for college students. Participants will learn how to use NotebookLM to transform existing content into more accessible formats. The session will cover practical strategies for implementing AI to improve readability and comprehension for all learners including students with disabilities, non-native English speakers, and students with varying learning preferences. We will discuss how to create transcripts for audio/video files and ensure content is well-organized and easy to navigate. This session will also highlight the importance of evidence-based practices in content creation and …

A Review Of Racial Differences And Disparities In Ecg, Jianwei Zheng, Chizobam Ani, Islam Abudayyeh, Yunfan Zheng, Cyril Rakovski, Ehsan Yaghmaei, Omolola Ogunyemi

Mathematics, Physics, and Computer Science Faculty Articles and Research

The electrocardiogram (ECG) is a widely used, non-invasive tool for diagnosing a range of cardiovascular conditions, including arrhythmia and heart disease-related structural changes. Despite its critical role in clinical care, racial and ethnic differences in ECG readings are often underexplored or inadequately addressed in research. Variations in key ECG parameters, such as PR interval, QRS duration, QT interval, and T-wave morphology, have been noted across different racial groups. However, the limited research in this area has hindered the development of diagnostic criteria that account for these differences, potentially contributing to healthcare disparities, as ECG interpretation algorithms largely developed from major …

Triswinunetr Lobe Segmentation Model For Computing Dir-Free Ct-Ventilation, Gabriela Roque Oliveira Nomura, Aarom T. Luong, Ananya Prakash, Annabelle Alemand, Tanish Bhowmick, Alisa Ali, Jaimie Ren, Basil Rehani, Girish Nair, Richard Castillo, Yevgeniy Vinogradskiy, Edward Castillo

Department of Radiation Oncology Faculty Papers

Purpose: Functional radiotherapy avoids the delivery of high-radiation dosages to high-ventilated lung areas. Methods to determine CT-ventilation imaging (CTVI) typically rely on deformable image registration (DIR) to calculate volume changes within inhale/exhale CT image pairs. Since DIR is a non-trivial task that can bias CTVI, we hypothesize that lung volume changes needed to calculate CTVI can be computed from AI-driven lobe segmentations in inhale/exhale phases, without DIR. We utilize a novel lobe segmentation pipeline (TriSwinUNETR), and the resulting inhale/exhale lobe volumes are used to calculate CTVI. Methods: Our pipeline involves three SwinUNETR networks, each trained on 6,501 CT image pairs …

Looking Beyond Numbers In Qualitative Research: From Data Saturation To Data Analysis, Prokopis Christou Phd

The Qualitative Report

In the realm of qualitative research, numbers have often been viewed with skepticism, and their role remains controversial. Despite this ambivalence, the importance of numerical data in qualitative research cannot be disregarded. This paper critically examines the increasing integration of numbers and algorithms, particularly through Artificial Intelligence (AI) technologies, within qualitative inquiry. It explores the role and implications of numerical data, emphasizing key principles such as data saturation. The paper highlights concerns associated with AI-assisted data analysis, including the risks of oversimplification, reductionist approaches, and detachment from context. It also addresses the potential dangers of technological over-reliance during the analysis …

Artificial Intelligence (Ai) In Pharmacy, Giang Nguyen, Elizabeth Sartschev, John Reyes, Allie Honigford, Marisa Petrunich, Kiley Devoll, Brianna Lu, Joshua Honaker, T'Bony M. Jewell

Pharmacy and Wellness Review

Artificial Intelligence (AI) has transformed the pharmaceutical field by enabling computer software systems to learn and perform human behavior. Specifically, AI has revolutionized chronic diabetes management through continuous glucose monitoring, showcasing its immense potential in healthcare. However, alongside its transformative impact, AI’s increasing role in healthcare has prompted concerns over privacy and its premature integration. Despite these challenges, AI offers limitless opportunities to improve medication management and treatment regimens, driving advancements across various domains. From improving CT imaging to enhancing adenoma detection in colonoscopies and facilitating medication adherence, AI’s impact on healthcare is profound. Furthermore, AI plays a pivotal role …

Plc-Controlled Intelligent Conveyor System With Ai-Enhanced Vision Of Efficient Waste Sorting, Natheer Almtireen, Nathir Rawashdeh, Viraj Reddy, Max Sutton, Alexander Nedvidek, Caden Karn, Et. Al.

Michigan Tech Publications

Current waste sorting mechanisms, particularly those relying on manual processes, semi-automated systems, or technologies without Artificial Intelligence (AI) integration, are hindered by inefficiencies, inaccuracies, and limited scalability, reducing their effectiveness in meeting growing waste management demands. This study introduces a prototype waste sorting machine that integrates an AI-driven vision system with a Programmable Logic Controller (PLC) for high-accuracy automated waste sorting. The system, powered by the YOLOv8 deep learning model, achieved sorting accuracies of 88% for metal cans, 75% for paper, and 91% for plastic bottles, with an overall precision of 90%, a recall of 80%, and a mean average …

Constructing A Parameterized Stimuli Space Suitable To Induce Controlled Changes In Human And Machine Perceptual Systems, Andrew Frankel

Dissertations, Theses, and Capstone Projects

Traditional computer vision applications focus on objective tasks, such as object detection, classification, or segmentation. Much of human experience is inherently subjective, such as our personal response to artwork. Individualized experiences are challenging to replicate in a controlled laboratory environment, as those experiences depend on the unique history and internal models of the observer. This work presents a technique to construct a 2-dimensional parameterized stimuli space suitable to induce controlled changes in the perceptual systems of both human and machine observers. Our contributions are threefold:

1. Using a balanced dataset of 8,800 fine art images spanning 11 art movements and 88 …

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Evaluating The Generalisability Of Region-Naïve Machine Learning Algorithms For The Identification Of Epilepsy In Low-Resource Settings, Ioana Duta, Symon M. Kariuki, Anthony Ngugi, Angelina Kakooza Mwesige, Honorati Masanja, Daniel M. Mwanga, Seth Owusu-Agyei, Ryan Wagner, J Helen Cross, Josemir W. Sander W. Sander

Population Health, East Africa

Objectives: Approximately 80% of people with epilepsy live in low- and middle-income countries (LMICs), where limited resources and stigma hinder accurate diagnosis and treatment. Clinical machine learning models have demonstrated substantial promise in supporting the diagnostic process in LMICs by aiding in preliminary screening and detection of possible epilepsy cases without relying on specialised or trained personnel. How well these models generalise to naïve regions is, however, underexplored. Here, we use a novel approach to assess the suitability and applicability of such clinical tools to aid screening and diagnosis of active convulsive epilepsy in settings beyond their original training contexts. …

Improving The Prediction Accuracy Of A Model Using Svr With Genetic Algorithm, Rand Haitham Abdul Hussein, Suhail Najm Abdulla

Journal of Economics and Administrative Sciences

The enhancement of predictive accuracy of the first-order integer autoregressive (INAR 1) model is the focus of the study through the effective amalgam of machine learning techniques and classical statistics. INAR (1) model structures are key elements for the prediction of nonnegative integer-valued time series processes. Conventional estimation methods include Classical Least Squares (CLS), Conditional Maximum Likelihood (CML), and Yule-Walker (YW) as compared with advanced methods like Support Vector Regression (SVR) and Genetic Algorithms (GA). A new clustering algorithm was created (with SVR to GA) to determine optimal INAR (1) model parameters. Computer simulation studies confirmed the effectiveness of the …

The Future Of Stealth Military Doctrine, Theodoros G. Kostis

Joint Force Quarterly

This article explores the future of stealth military doctrine and argues that conventional stealth, centered on reducing radar cross-section and other detectable signatures, faces growing limitations against advanced integrated air defense systems. The author proposes shifting from a model based primarily on high speed and low observability toward one that emphasizes surprise through obfuscation. Central to this approach is the stand-in variable speed multiple-domain (SiVSMD) offensive concept, which combines artificial intelligence, autonomous unmanned systems, swarm tactics, miniaturization, electronic warfare, cyber capabilities, deception, and mosaic warfare. Small, distributed platforms would operate across land, sea, undersea, and air domains, varying their speed …

Unveiling The Potential Of Generative Artificial Intelligence: A Multidimensional Journey Into The Future, Keng Boon Ooi, Alex Koohang, Eugene Cheng Xi Aw, Tat Huei Cham, Cihan Cobanoglu, Charles Dennis, Yogesh K. Dwivedi, Jun Jie Hew, Heather Linton Kelly, Laurie Hughes, Chieh Yu Lin, Anubhav Mishra, Ian Phau, Ramakrishnan Raman, Marianna Sigala, Yun Chia Tang, Lai Wan Wong, Garry Wei Han Tan

Research outputs 2022 to 2026

Purpose: The launch of ChatGPT has brought the large language model (LLM)-based generative artificial intelligence (GAI) into the spotlight, triggering the interests of various stakeholders to seize the possible opportunities implicated by it. Nevertheless, there are also challenges that the stakeholders should observe when they are considering the potential of GAI. Given this backdrop, this study presents the viewpoints gathered from various subject experts on six identified areas. Design/methodology/approach: Through an expert-based approach, this paper gathers the viewpoints of various subject experts on the identified areas of tourism and hospitality, marketing, retailing, service operations, manufacturing and healthcare. Findings: The subject …

Deep Learning-Based Superconductivity Prediction And Experimental Tests, Daniel Kaplan, Adam Zheng, Joanna Blawat, Rongying Jin, Robert J. Cava, Viktor Oudovenko, Gabriel Kotliar, Anirvan M. Sengupta, Weiwei Xie

Faculty Publications

The discovery of novel superconducting materials is a long-standing challenge in materials science, with a wealth of potential for applications in energy, transportation and computing. Recent advances in artificial intelligence (AI) have enabled expediting the search for new materials by efficiently utilizing vast materials databases. In this study, we developed an approach based on deep learning (DL) to predict new superconducting materials. We have synthesized a compound derived from our DL network and confirmed its superconducting properties in agreement with our prediction. Our approach is also compared to previous work based on random forests (RFs). In particular, RFs require knowledge …