arXiv subjects

Brady D. Lund

Publications and source records attributed to Brady D. Lund.

7 recordsLinked to original sources

arXiv2026

NEURON: A Neuro-symbolic System for Grounded Clinical Explainability

Clinical AI adoption is hindered by the black-box/grey-box nature of high-performing models, which lack the ontological grounding and narrative transparency required for professional-level explainability. We present NEURON, a neuro-symbolic system designed to enhance both predictive reliability and clinical interpretability. NEURON integrates SNOMED CT ontology-informed structural representations with machine learning models to bridge the gap between raw data and medical nomenclature. To facilitate human-aligned interaction, the system utilizes a Retrieval-Augmented Generation (RAG) grounded Large Language Model (LLM) layer to synthesize SHAP feature attributions and patient-specific clinical notes into coherent, natural-language explanations. Validated on the MIMIC-IV dataset for Acute Heart Failure mortality prediction, NEURON improved the AUC from 0.71-0.74 to 0.74-0.77 and substantially outperformed SHAP-based explanations in human-aligned metrics (0.807 vs. 0.558). Our results demonstrate that NEURON offers a robust, scalable engineering solution for deploying trustworthy, human-centered connected health applications.

cs.AI↗

arXiv2026

AI Literacy: An Exercise in Power-Knowledge

As generative artificial intelligence becomes one of the most significant systems of knowledge production in our society today, questions relating to who can access and shape that production grow increasingly important in our discourse. This paper argues that the existing frameworks for AI literacy, which are dominated by technical competency and responsible-use principles, are insufficient because they enforce a "consumer" orientation toward AI rather than fostering genuine epistemic agency. Based upon Foucault's concept of power-knowledge, Freire's pedagogy of critical consciousness, and scholarship of digital literacy, this paper proposes a reconceptualization of AI literacy as a critical practice that equips individuals not just to use AI systems, but to critically evaluate them, resist their structuring assumptions, and participate in their governance. The paper further argues that unequal access to AI tools in society recapitulates longstanding epistemic injustices, and that a literacy framework oriented toward empowerment must account for these structural inequities. A three-part framework of AI literacy based on the notions of contextual use, critical interrogation, and participatory governance frames this literacy as a cultivation of epistemic "agents" rather than the training of competent consumers of AI-generated information.

cs.AI↗

arXiv2026

PDPL Metric: Validating a Scale to Measure Personal Data Privacy Literacy Among University Students

Personal data privacy literacy (PDPL) refers to a collection of digital literacy skills related to an individuals ability to understand, evaluate, and manage the collection, use, and protection of personal data in online and digital environments. This study introduces and validates a new psychometric scale (PDPL Metric) designed to measure data privacy literacy among university students, focusing on six key privacy constructs: perceived risk of data misuse, expectations of informed consent, general privacy concern, privacy management awareness, privacy-utility trade-off acceptance, and perceived importance of data security. A 24-item questionnaire was developed and administered to students at U.S.-based research universities. Principal components analysis confirmed the unidimensionality and internal consistency of each construct, and a second-order analysis supported the integration of all six into a unified PDPL construct. No differences in PDPL were found based on basic demographic variables like academic level and gender, although a difference was found based on domestic/international status. The findings of this study offer a validated framework for assessing personal data privacy literacy within the higher education context and support the integration of the core constructs into higher education programs, organizational policies, and digital literacy initiatives on university campuses.

cs.CY↗

arXiv2026

Measuring University Students Satisfaction with Traditional Search Engines and Generative AI Tools as Information Sources

This study examines university students levels of satisfaction with generative artificial intelligence (AI) tools and traditional search engines as academic information sources. An electronic survey was distributed to students at U.S. universities in late fall 2025, with 236 valid responses received. In addition to demographic information about respondents, frequency of use and levels of satisfaction with both generative AI and traditional search engines were measured. Principal components analysis identified distinct constructs of satisfaction for each information source, while k-means cluster analysis revealed two primary student groups: those highly satisfied with search engines but dissatisfied with AI, and those moderately to highly satisfied with both. Regression analysis showed that frequency of use strongly predicts satisfaction, with international and undergraduate students reporting significantly higher satisfaction with AI tools than domestic and graduate students. Students generally expressed higher levels of satisfaction with traditional search engines over generative AI tools. Those who did prefer AI tools appear to see them more as a complementary source of information rather than a replacement for other sources. These findings stress evolving patterns of student information seeking and use behavior and offer meaningful insights for evaluating and integrating both traditional and AI-driven information sources within higher education.

cs.CY↗

arXiv2025

What Does Information Science Offer for Data Science Research?: A Review of Data and Information Ethics Literature

This paper reviews literature pertaining to the development of data science as a discipline, current issues with data bias and ethics, and the role that the discipline of information science may play in addressing these concerns. Information science research and researchers have much to offer for data science, owing to their background as transdisciplinary scholars who apply human-centered and social-behavioral perspectives to issues within natural science disciplines. Information science researchers have already contributed to a humanistic approach to data ethics within the literature and an emphasis on data science within information schools all but ensures that this literature will continue to grow in coming decades. This review article serves as a reference for the history, current progress, and potential future directions of data ethics research within the corpus of information science literature.

cs.DL↗

arXiv2025

Understanding the Relationship Between Personal Data Privacy Literacy and Data Privacy Information Sharing by University Students

With constant threats to the safety of personal data in the United States, privacy literacy has become an increasingly important competency among university students, one that ties intimately to the information sharing behavior of these students. This survey based study examines how university students in the United States perceive personal data privacy and how their privacy literacy influences their understanding and behaviors. Students responses to a privacy literacy scale were categorized into high and low privacy literacy groups, revealing that high literacy individuals demonstrate a broader range of privacy practices, including multi factor authentication, VPN usage, and phishing awareness, whereas low literacy individuals rely on more basic security measures. Statistical analyses suggest that high literacy respondents display greater diversity in recommendations and engagement in privacy discussions. These findings suggest the need for enhanced educational initiatives to improve data privacy awareness at the university level to create a better cyber safe population.

cs.CR↗

arXiv2025

Zero Trust Cybersecurity: Procedures and Considerations in Context

In response to the increasing complexity and sophistication of cyber threats, particularly those enhanced by advancements in artificial intelligence, traditional security methods are proving insufficient. This paper explores the Zero Trust cybersecurity framework, which operates on the principle of never trust, always verify to mitigate vulnerabilities within organizations. Specifically, it examines the applicability of Zero Trust principles in environments where large volumes of information are exchanged, such as schools and libraries. The discussion highlights the importance of continuous authentication, least privilege access, and breach assumption. The findings underscore avenues for future research that may help preserve the security of these vulnerable organizations.

cs.CR↗