Digital Commons Network^™

The Great Scrape: The Clash Between Scraping And Privacy, Daniel J. Solove, Woodrow Hartzog

Faculty Scholarship

Artificial intelligence (AI) systems depend on massive quantities of data, often gathered by “scraping”—the automated extraction of large amounts of data from the internet. A great deal of scraped data contains people’s personal information. This personal data provides the grist for AI tools such as facial recognition, deep fakes, and generative AI. Although scraping enables web searching, archiving of records, and meaningful scientific research, scraping for AI can also be objectionable and even harmful to individuals and society.

Organizations are scraping at an escalating pace and scale, even though many privacy laws are seemingly incongruous with the practice. In this …

A Proposed Ehrenfeucht-Fraïssé Game Model For Natural Language Processing Generative Adversarial Networks, Don Li

Anthós

Large Language Models (LLM’s) (e.g., ChatGPT) constitute both a significant research area and commercial application of AI. Current major LLM’s are built on Generative Pre-Trained Transformer (GPT) neural network architecture to perform natural language processing (NLP) tasks. Generative Adversarial Network (GAN) is another popular neural network architecture, which leverages a zero-sum game between constituent neural networks within the architecture to train the GAN, and is widely used for visual data applications. This article proposes a new GAN architecture for NLP: an EF-GAN whose underlying algorithm uses Ehrenfeucht–Fraïssé (EF) games, a game-theoretic approach from model theory to determine elementary equivalence of …

Meta’S Ai Bias To What Extent Do Meta's Algorithmic Processes For Classifying And Processing Information Related To Acts Of Racism Impact Fairness, Bias, And Social Justice Across Its Platforms?, Shanique Foster

Electronic Theses and Dissertations 2020 - Present

Algorithmic processing and classification of racism-related content have become increasingly prevalent across Meta's platforms. While these algorithms aim to create safer online environments, their effectiveness and impact on fairness remain understudied. This research examines how Meta's algorithmic processes for classifying racist content affect fairness, bias, and social justice across Facebook, Instagram, and WhatsApp.

Using qualitative research methodology, the study conducted focus groups with diverse platform users to understand their experiences with content moderation. The research also analyzed theoretical frameworks related to algorithmic bias, fairness metrics, and social justice in digital spaces.

Key findings revealed significant variations in algorithmic effectiveness across …

Ai-Based Detection Of Coronary Artery Occlusion Using Acoustic Biomarkers Before And After Stent Placement, David Anderson Lloyd, Andrei Dragomir, Bulent Ozpolat, Biykem Bozkurt, Yasemin Akay, Metin Akay

Faculty, Staff and Students Publications

No abstract provided.

The Monstrous-Digital: Data Necropolitics, Social Media, And The Assassination Of Brian Thompson, Laura Glitsos

Research outputs 2022 to 2026

This research article examines the assassination of UnitedHealthcare CEO Brian Thompson and the ensuing social media attention on the alleged shooter Luigi Mangione. I argue that the incident can be read as a cultural moment that captures the essence of an emergent sensibility that I call the “monstrous-digital” because of its layered and complex interconnections with the digital apparatus as a space of control and contestation. Specifically, I connect it to the biopolitical weaponization of computational systems, McQuillan’s (2015) algorithmic states of exception, and Pele and Mulholland’s (2023) data necropolitics. I approach this research using a qualitative case study method, …

Knowledge Diffusion In Networks Of Artificial Learners, Ehsan Beikihassan

Dissertations

The dissertation draws inspiration from the topic of peer learning in the social sciences and the study of information dissemination and knowledge diffusion in network science. In particular, it introduces and studies a setting involving a population or network of artificial learners, with the objective of optimizing aggregate performance measures under constraints on training resources. In this context, natural knowledge diffusion processes in networks of interacting artificial learners are studied. The term "natural" refers to processes that emulate human peer learning, where the internal state and learning processes of students remain largely opaque, and the main degree of freedom lies …

Multidisciplinary Collaboration For The Integration Of Ai In Slavic Languages And Literatures: The Learning Lab Model, Anna Ivanov

Russian Language Journal

The Learning Lab at Harvard University's Derek Bok Center for Teaching and Learning employs graduate students as Media and Design Fellows (MDFs) to support innovative pedagogy in their departments. Over the last few years and in its Summer of AI, the Learning Lab has explored AI and its applications and implications in education. As such, the Lab, its staff, and its MDFs have developed and run well-structured, intellectually rigorous learning opportunities for many departments. For the Department of Slavic Languages and Literatures, the Learning Lab used AI in language and literature workshops. This paper presents a few case studies of …

Artificial Intelligence In Fetal And Pediatric Echocardiography, Alan Wang, Tam T Doan, Charitha Reddy, Pei-Ni Jone

Faculty, Staff and Students Publications

Echocardiography is the main modality in diagnosing acquired and congenital heart disease (CHD) in fetal and pediatric patients. However, operator variability, complex image interpretation, and lack of experienced sonographers and cardiologists in certain regions are the main limitations existing in fetal and pediatric echocardiography. Advances in artificial intelligence (AI), including machine learning (ML) and deep learning (DL), offer significant potential to overcome these challenges by automating image acquisition, image segmentation, CHD detection, and measurements. Despite these promising advancements, challenges such as small number of datasets, algorithm transparency, physician comfort with AI, and accessibility must be addressed to fully integrate AI …

Measurement Of Breast Artery Calcification Using An Artificial Intelligence Detection Model And Its Association With Major Adverse Cardiovascular Events, Suzanne Rose, Josette Hartnett, Zachary Estep, Daniyal Ameen, Shweta Karki, Edward Schuster, Rebecca Newman, David Hsi

Department of Medicine Faculty Papers

Breast artery calcification (BAC) obtained from standard mammographic images is currently under evaluation to stratify risk of major adverse cardiovascular events in women. Measuring BAC using artificial intelligence (AI) technology, we aimed to determine the relationship between BAC and coronary artery calcification (CAC) severity with Major Adverse Cardiac Events (MACE). This retrospective study included women who underwent chest computed tomography (CT) within one year of mammography. T-test assessed the associations between MACE and variables of interest (BAC versus MACE, CAC versus MACE). Risk differences were calculated to capture the difference in observed risk and reference groups. Chi-square tests and/or Fisher's …

Ethical Aspects Of Utilising Artificial Intelligence In Clinical Settings, Jeffrey Byrnes, Michael Robinson

Philosophy Faculty Articles and Research

In response to recent proposals to utilize artificial intelligence (AI) to automate ethics consultations in healthcare, we raise two main problems for the prospect of having healthcare professionals rely on AI-driven programs to provide ethical guidance in clinical matters. The first cause for concern is that, because these programs would effectively function like black boxes, this approach seems to preclude the kind of transparency that would allow clinical staff to explain and justify treatment decisions to patients, fellow caregivers, and those tasked with providing oversight. The other main problem is that the kind of authority that would need to be …

Transparency And Authority Concerns With Using Ai To Make Ethical Recommendations In Clinical Settings, Jeffrey Byrnes, Michael Robinson

Philosophy Faculty Articles and Research

In response to recent proposals to utilize artificial intelligence (AI) to automate ethics consultations in healthcare, we raise two main problems for the prospect of having healthcare professionals rely on AI-driven programs to provide ethical guidance in clinical matters. The first cause for concern is that, because these programs would effectively function like black boxes, this approach seems to preclude the kind of transparency that would allow clinical staff to explain and justify treatment decisions to patients, fellow caregivers, and those tasked with providing oversight. The other main problem is that the kind of authority that would need to be …

Volume 7 (2024) Artificial Intelligence And Responsibility, Arwa Alnajashi, Danielle Derise, Philip L. Frana, David Mcgraw, Amanda Sawyer, Tatjana Titareva, Raafat Zaini, Allie Zombron

International Journal on Responsibility

The seventh volume of the International Journal on Responsibility (IJR) arrives at a crucial moment in the evolution of artificial intelligence and its integration into our academic and social fabric. As we witness the rapid advancement and deployment of AI systems across various domains, this special issue examines the multifaceted dimensions of responsibility surrounding AI technology, with a particular focus on its role in higher education and broader societal implications.

The articles in this volume contribute to our understanding of responsibility through diverse lenses, from classroom implementation to ethical design considerations. Together, they fulfill IJR’s central mission of exploring “Who …

Computer-Mediated Communication And Artificial Intelligence Strategies In Arts Entrepreneurship, Anna Blake

Journal of Arts Entrepreneurship Education

This study analyzes the potential of artificial intelligence (AI) using computer-mediated communication (CMC) theory to prospect areas for innovation in an emerging field that is rapidly becoming integrated within our everyday lives as well as in the marketplace. In order to demonstrate how arts entrepreneurs can effectively and ethically leverage AI technologies to communicate with their audiences and stakeholders, I examine as case studies the work of individual artists including Grimes. The CMC framework is used to examine art as a form of communication, illustrate how AI changes the nature of art, challenge the extant concepts of identity and intellectual …

The Chinese Room And Creating Consciousness: How Recent Strides In Ai Technology Revitalize A Classic Debate, Thomas Held

Departmental Honors & Graduate Capstone Projects

Since 1950, when Alan Turing first posed the question of whether a machine could think, the possibility of artificial consciousness has sparked intense and ongoing debate, and strong positions have been staked out on each side of the argument. On the one hand, the historically popular functionalist school of thought claims that any system capable of producing suitably “conscious” behavior in a given environment should be considered conscious. On the other hand, John Searle’s famous “Chinese Room” argument insists that this cannot be the case, and that consciousness is in all likelihood not artificially reproducible. However, both positions have issues—the …

Redefining Reference Services In The 21st Century, Johanna Tunon

School of Information Student Research Journal

The role of reference services in libraries has evolved significantly in the 21st century, driven by technological advancements and changing user needs. This paper explores how reference services have transitioned from traditional, desk-centric interactions to encompass a wide array of platforms and tools, including digital innovations and generative AI. The decline in reference transactions has prompted librarians to adapt, shifting focus towards more complex consultations and instructional roles, while also navigating the blurred boundaries between reference assistance and formal instruction. The paper also examines the updated definitions and limitations of reference services by the Reference and User Services Association (RUSA), …

And You Don't Seem To Understand: Disanalogies Between Artificial And Biological Consciousness, Derek Freeman

The Journal of Purdue Undergraduate Research

This paper posits a new way of attempting to formulate theories of machine consciousness. At present, inquiry revolves around formulating criteria that could be applied to machines to determine if they possess human-like consciousness. The paper demonstrates that efforts so far have not been fruitful, instead finding that present methods of inquiry are destined to result in an impasse between those who support the notion of machine consciousness and those who oppose it. Consequently, the paper questions the assumption that human and machine consciousness must resemble each other. It explores a different approach, one that rejects the idea that the …

Data Intensive Method For Processing Defect Detection And Mitigation For Composites, Deepak Kumar

Doctoral Dissertations and Master's Theses

Composite manufacturing without processing defects is a crucial step in satisfying the production, performance, and quality requirements of composite materials in various industries. Autoclave processing enables manufacturing of high-quality composite parts with excellent mechanical properties, whereas additive manufacturing (AM) offers adaptability to complex designs and streamlined processes. However, each method presents unique challenges; despite the benefits, autoclave processes can still result in processing defects, and AM is particularly susceptible to processing anomalies owing to the novelty of the process. Ensuring the quality and reliability of composite manufacturing is essential to fully capitalize on the advantages offered by both techniques. Artificial …

Predicting Chaotic Systems With Quantum Echo-State Networks, Erik Connerty, Ethan N. Evans, Gerasimos Angelatos, Vignesh Narayanan

Publications

Recent advancements in artificial neural networks have enabled impressive tasks on classical computers, but they demand significant computational resources. While quantum computing offers potential beyond classical systems, the advantages of quantum neural networks (QNNs) remain largely unexplored. In this work, we present and examine a quantum circuit (QC) that implements and aims to improve upon the classical echo-state network (ESN), a type of reservoir-based recurrent neural networks (RNNs), using quantum computers. Typically, ESNs consist of an extremely large reservoir that learns high-dimensional embeddings, enabling prediction of complex system trajectories. Quantum echo-state networks (QESNs) aim to reduce this need for prohibitively …

Sd-Weat: Towards Robustly Measuring Bias In Input Embeddings For Artificial Intelligence Language Models, Magnus Gray

Theses and Dissertations

Artificial intelligence (AI) is rapidly transforming industries and markets, from healthcare to entertainment, revolutionizing decision-making processes. However, as AI grow more influential, they also risk amplifying existing biases, potentially leading to harmful consequences. Recent advancements in large language models (LLMs), such as GPT-4 and Llama, have heightened concerns about bias in natural language processing (NLP) tasks, driving the need for robust methods to detect and mitigate bias. Current approaches, such as the Word Embedding Association Test (WEAT) and its sentence-level extension the Sentence Encoder Association Test (SEAT) often fall short in capturing the nuances of biases in the input embeddings …

Investigating The Efficacy Of Prompt Engineering Techniques For Research Survey Paper Generation Using Large Language Models, Innocent Obed Awidi

Theses and Dissertations

The recent advancements in large language models (LLMs), such as ChatGPT, offer promising potential for automating academic writing tasks. This study investigates the efficacy of prompt engineering techniques in guiding LLMs to generate high-quality research survey papers, specifically exploring their application in the fields of artificial intelligence (AI) in drug development and large language model customization for classification. Using a mixed-methods approach, this research assessed the impact of prompt engineering on the coherence, relevance, and overall quality of generated content through quantitative and qualitative analyses. Techniques including prompt refinement, contextual prompting, and iterative prompting were systematically applied to enhance the …

Alphafold2-Based Characterization Of Apo And Holo Protein Structures And Conformational Ensembles Using Randomized Alanine Sequence Scanning Adaptation: Capturing Shared Signature Dynamics And Ligand-Induced Conformational Changes, Nishank Raisinghani, Vedant Parikh, Brandon Foley, Gennady M. Verkhivker

Mathematics, Physics, and Computer Science Faculty Articles and Research

Proteins often exist in multiple conformational states, influenced by the binding of ligands or substrates. The study of these states, particularly the apo (unbound) and holo (ligand-bound) forms, is crucial for understanding protein function, dynamics, and interactions. In the current study, we use AlphaFold2, which combines randomized alanine sequence masking with shallow multiple sequence alignment subsampling to expand the conformational diversity of the predicted structural ensembles and capture conformational changes between apo and holo protein forms. Using several well-established datasets of structurally diverse apo-holo protein pairs, the proposed approach enables robust predictions of apo and holo structures and conformational ensembles, …

Spectrum Optimization For Advanced Air Mobility Communications Using Deep Reinforcement Learning., Rafael D. Apaza

Electronic Theses and Dissertations

As aviation operations expand and new participants enter the National Airspace System (NAS), the demand for aeronautical communications will experience a significant rise. This surge is propelled by increased air travel and the emergence of Urban Air Mobility (UAM) operations, a subset of Advanced Air Mobility (AAM). UAM aims to facilitate intra-city transportation of people and cargo utilizing remotely piloted aircraft capable of electric vertical takeoff and landing operations. The growing dependence on efficient wireless communication systems underscores the critical importance of intelligent spectrum allocation and effective airspace management to ensure safe, seamless, and technologically advanced air operations. However, the …

The Algorithm Of Fear: Unpacking Prejudice Against Ai And The Mistrust Of Technology, James Hutson, Daniel Plate

Faculty Scholarship

The mistrust of AI seen in the media, industry and education reflects deep-seated cultural anxieties, often comparable to societal prejudices like racism and sexism. Throughout history, literature and media have portrayed machines as antagonists, amplifying fears of technological obsolescence and identity loss. Despite the recent remarkable advancements in AI—particularly in creative and decision-making capacities—human resistance to its adoption persists, rooted in a combination of technophobia, algorithm aversion, and cultural narratives of dystopia. This review investigates the origins of this prejudice, focusing on the parallels between current attitudes toward AI and historical resistance to new technologies. Drawing on examples from popular …

From Concept To Creation: The Role Of Generative Artificial Intelligence In The New Age Of Digital Marketing, Andrew Smith, James Hutson

Faculty Scholarship

Artificial intelligence (AI) has been extensively used in digital marketing. Still, the recent advances in generative AI (GAI) have revolutionized social media marketing and content creation, lowering barriers that once restricted high-quality design to professionals well versed in expensive and complex software like Adobe Suite. GAI tools enable anyone, from students to marketers, to generate logos, branding, and multimedia content without extensive training. This shift has empowered more people to engage in creative expression, expanding the pool of ideas and creativity. However, the abundance of AI-generated content raises questions about the evolving definition of “art” and the emergence of a …

Algotric: Symmetric And Asymmetric Encryption Algorithms For Cryptography – A Comparative Analysis In Ai Era, Naresh Kshetri, Mir Mehedi Rahman, Md Masud Rana, Omar Faruq Osama, James Hutson

Faculty Scholarship

The increasing integration of artificial intelligence (AI) within cybersecurity has necessitated stronger encryption methods to ensure data security. This paper presents a comparative analysis of symmetric (SE) and asymmetric encryption (AE) algorithms, focusing on their role in securing sensitive information in AI-driven environments. Through an in-depth study of various encryption algorithms such as AES, RSA, and others, this research evaluates the efficiency, complexity, and security of these algorithms within modern cybersecurity frameworks. Utilizing both qualitative and quantitative analysis, this research explores the historical evolution of encryption algorithms and their growing relevance in AI applications. The comparison of SE and AE …

Video Game Development 3.0: Ai-Driven Collaborative Co-Creation, Jay Ratican, James Hutson

Faculty Scholarship

The evolution of game development has transitioned from manual coding (Software 1.0) to data-driven Artificial Intelligence (AI) (Software 2.0), and now to a more advanced stage—video game development 3.0. This phase is characterized by AI-driven processes leveraging large language models (LLMs), neural networks, and other AI techniques that autonomously generate code, content, and narratives. This paper explores the foundational technologies underpinning this paradigm shift, including customizable AI modules, dynamic asset creation, and intelligent non player characters (NPCs) that adapt to player interactions. It also highlights the integration of AI with emerging technologies like Virtual Reality (VR), Augmented Reality (AR), and …

The Evolution Of Artificial Intelligence In Gaming, Aditi Jorapur

ART 108: Introduction to Games Studies

No abstract provided.

Artificial Intelligence, Machines, Devices, And Robots: Mobilizing Humanness And Intelligence In The American Techno-Landscape, Carlos A. Batres

Dissertations

This dissertation is an ethnographic inquiry of artificial intelligence (AI) in the techno-landscape of three major metropolitan areas in the United States. It traces the ways in which both experts and non-experts talk about their interactions with AI via machines, robots, and other devices, and how the ways in which people deal with AI-technologies have come to shape how we think about our own ways of going about doing things. The goal is to discern what the social ramifications of this technology may be as relationships and relations are formed betwixt and between people and artificially intelligent things.

Enhancing Assessment And Feedback In Game Design Programs: Leveraging Generative Ai For Efficient And Meaningful Evaluation, James Hutson, Ben Fulcher, Jay Ratican

Faculty Scholarship

The integration of generative AI tools in game design education offers promising ways to streamline the grading, assessment, and feedback processes that are typically labor-intensive. In game design programs, faculty often deal with varied file formats, including 3D models, executable prototypes, videos, and complex game design documents. Traditional methods of assessment and feedback, primarily text-based, struggle to provide timely and actionable insights for students. Furthermore, only a small percentage of top students consistently review and apply feedback, leading to inefficiencies. This article explores how generative AI tools can augment these processes by automating aspects of grading, generating more personalized and …

Addressing Ethical Issues In Healthcare Artificial Intelligence Using A Lifecycle-Informed Process, Benjamin X Collins, Jean-Christophe Bélisle-Pipon, Barbara J Evans, Kadija Ferryman, Xiaoqian Jiang, Camille Nebeker, Laurie Novak, Kirk Roberts, Martin Were, Zhijun Yin, Vardit Ravitsky, Joseph Coco, Rachele Hendricks-Sturrup, Ishan Williams, Ellen W Clayton, Bradley A Malin, Bridge2ai Ethics And Trustworthy Ai Working Group

Faculty, Staff and Student Publications

OBJECTIVES: Artificial intelligence (AI) proceeds through an iterative and evaluative process of development, use, and refinement which may be characterized as a lifecycle. Within this context, stakeholders can vary in their interests and perceptions of the ethical issues associated with this rapidly evolving technology in ways that can fail to identify and avert adverse outcomes. Identifying issues throughout the AI lifecycle in a systematic manner can facilitate better-informed ethical deliberation.

MATERIALS AND METHODS: We analyzed existing lifecycles from within the current literature for ethical issues of AI in healthcare to identify themes, which we relied upon to create a lifecycle …