Artificial Intelligence and Robotics Commons^™

Comprehensively Evaluating The Perception Systems Of Autonomous Vehicles Against Hazards, Xiaodong Zhang, Jie Bao, Jianlei Chi, Jun Sun, Zijiang Yang

Research Collection School Of Computing and Information Systems

Perception systems are vital for the safety of autonomous driving. In complex autonomous driving scenarios, autonomous vehicles must overcome various natural hazards, such as heavy rain or raindrops on the camera lens. Therefore, it is essential to conduct comprehensive testing of the perception systems in autonomous vehicles against these hazards, as demanded by the regulatory agencies of many countries for human drivers. Since there are many hazard scenarios, each of which has multiple configurable parameters, the challenges are (1) how do we systematically and adequately test an autonomous vehicle against these hazard scenarios, with measurable outcome; and (2) how do …

Artificial Intelligence-Driven Materials Science: Evolution, Framework, Dilemmas, And Breakthroughs, Yanglili Zhou, Weihua Wang, Ziwei Zhao

Bulletin of Chinese Academy of Sciences (Chinese Version)

Artificial intelligence-driven materials science (AIMS) represents a revolutionary and disruptive paradigm in materials research, promising to fundamentally break through the traditional bottlenecks of research cycles and efficiency. Historically, the evolution of materials science research paradigms from empirical trial and error, theoretical modeling, and computational simulation to the new data-driven stage has been driven by innovations in cognitive tools and methods. Currently, artificial intelligence, as a disruptive cognitive tool, is fundamentally reconstructing the core elements and interaction logic of materials science: the research process achieves intelligent iteration and full-process closed-loop; the capabilities of researchers are reshaped and teams are organized; and …

Study On Strategies And Measures And Insights For Introducing Artificial Intelligence Talents In Japan, Fangxin Hu, Ru Ma, Yujun Su

Bulletin of Chinese Academy of Sciences (Chinese Version)

In response to the aging population and the need for innovation in science and technology development, Japan regards AI as a key technology to solve social problems. In addition to accelerating the training of domestic AI talents, Japan is also vigorously introducing overseas AI talents. This study sorts out and analyzes Japan’s long-term, annual, and AI-specific strategic planning for the introduction of AI talents, including Basic Plan for Science, Technology and Innovation, Comprehensive Innovation Strategy, Strategic Plan for Artificial Intelligence Technology, and AI Strategy, and explores Japan’s specific implementation measures such as updating the national residence management system, improving the …

Typeface: Machine-Viewing Gentrification On Storefront Imagery In Bedford-Stuyvesant, Brooklyn, Alexander Mcquilkin

Dissertations, Theses, and Capstone Projects

Gentrification—broadly, the replacement of a less powerful group by a more powerful one in an urban context—is oft-discussed in the popular press, but its definition is much-debated in the urban planning literature. Furthermore, academic treatments of displacement understandably focus on measurable yet fairly abstract indicators like changes in rent or income, whereas neighborhood change is often registered by residents on the ground using visual, but difficult-to-quantify markers like retail turnover. This project uses image recognition technology on a set of storefront photos to index the visual streetscape of a neighborhood, as well as to track changes to that portrait over …

A Visualization-Supported, Hierarchical, Action-Learning Model For Driving Behavior In A V2x Environment, Xuantong Wang, Jing Li, Jecca Bowen

Geography and the Environment: Faculty Scholarship

Understanding human driving decisions is crucial for intelligent transportation research. Most existing studies focus on individual vehicles in limited contexts, which restricts broader applicability of results. Leveraging Vehicle-to-Everything (V2X) infrastructure, this study introduces a machine learning framework to model driving actions and detect outliers across diverse environments. This approach features a semantically enabled clustering method that groups similar driving behaviors based on speed and actions. It also adds a time-series learning model to identify typical driving behaviors across various contexts, thereby enabling detection of abnormal driving actions. A suite of visual tools has been developed to help interpret driving patterns, …

Ai In Society: A Regulatory Framework For Responsible Integration, Ziad Doughan, Sari Itani, Hadi Al Mubasher

BAU Journal - Science and Technology

This review paper studies the influence of Artificial Intelligence (AI) and Machine Learning (ML) on society in various categories in detail. AI and ML have developed rapidly in the past two decades, thus changing our lifestyles. These developments have various positive and negative impacts on society. This paper explores the many societal impacts of AI and ML, in economics, social aspects, ethics, and policy, shedding light on the opportunities and challenges that arise. An interdisciplinary insight is capable of understanding the challenges society faces when it uses AI and ML, locking opportunities that lie ahead and identifying promising paths towards …

Digital Redlining In The Smart City: Artificial Intelligence, Housing Law, And Structural Urban Inequality, Spurthi Nrusimhadevara

Undergraduate Scholarship and Creative Works

Artificial intelligence is increasingly used in urban housing systems, where it shapes decisions about tenant screening, rent pricing, lending, zoning, and neighborhood investment. Although these tools are often promoted as efficient and impartial, they frequently rely on historical data that reflect racial, economic, and spatial inequality. As a result, AI systems can reproduce discriminatory outcomes even when protected characteristics are not directly used. This paper examines digital redlining in the smart city and argues that algorithmic housing tools mirror long standing structural inequities that raise significant concerns under fair housing and civil rights law. It evaluates how automated screening, predictive …

Computational Clinical Judgment: Predicting Risk With Large Language Models, Hannah Laqueur, Ryan W. Copus

Faculty Works

For seventy years, research has shown actuarial methods outperform clinical judgment. Yet actuarial approaches have limitations: they generally rely on structured data; cannot exploit rare case-specific details; have limited accuracy where outcome data are scarce or incomplete; and cannot offer case-level justifications. Large language models (LLMs) offer a different approach. Like actuarial methods, they aggregate information algorithmically, but like clinicians, they bring general knowledge and can provide case-level justifications. We prompted seven LLMs to assess rearrest risk from 113 parole hearing transcripts and compared their predictions to a machine learning model trained on 4,000 cases with 91 administrative variables. GPT-5 …

Accounting For Spatial Effects And Social Norms In Making Algorithmic Law: Insights From And Applications In Urban Mobility, Jingkang Gao

Journal of Law and Mobility

This Article examines a prominent idea in the law and technology literature: that algorithms and big data can be used to make law dynamic and personalized. As currently envisioned by legal scholars, “algorithmic law” entails laws that adjust in real time to changing conditions and vary across individuals, improving welfare by tailoring legal rules and standards to personal characteristics.

This Article argues that this vision of algorithmic law is incomplete—and often counterproductive. Existing proposals treat personalization as a function of individual attributes alone, overlooking the fact that effects of individual behavior are fundamentally interactive. Individual behavior is shaped by spatial …

Simulating Social Attitudes With Llms: Accuracy, Demographic Effects, And Refusal Behavior In The Sensitive Domain Of Suicide Prevention, Cristina J. Perez, Michael P. Vasquez Jr., Philippe J. Giabbanelli, Patrick Y. Wu

VMASC Publications

Large language models (LLMs) are increasingly used to simulate public opinion, yet their validity in sensitive policy domains remains underexplored. We evaluate whether LLMs can reproduce attitudes toward suicide prevention policies using 32 questions drawn from seven nationally representative U.S. surveys (2023-2025). We systematically vary demographic conditioning (race/ethnicity, gender, age, education, income, party), prompt framing (direct elicitation, respondent embodiment, specialist embodiment), and model architecture (GPT-5 Nano, DeepSeek V3.2, Meta Llama 3.1 8B, Mistral Small 24B). Across 811,560 prompts, the mean absolute error—the average gap between predicted and human response distributions—is 23 percentage points. We also find that LLM responses to …

Interpretable Machine Learning For Bridge-Pier Scour Prediction And Flood Resilience, Adil Khan, Dalya Ismael

Engineering Technology Faculty Publications

Bridge-pier scour is a leading cause of flood-induced bridge failure, yet practice still lacks transparent, physics-informed tools that link data-driven prediction with design guidance. This study develops an interpretable, physics-aware machine-learning framework to predict equilibrium scour depth and translate those predictions into actionable strategies for flood-resilient infrastructure. Using the 2014 U.S. Geological Survey Pier-Scour Database (569 laboratory cases), five models: Gradient Boosting, AdaBoost (Tree), XGBoost, Gaussian Process (RBF kernel), and Kernel Ridge (polynomial), were trained and evaluated with K-fold cross-validation. Model performance was evaluated using R², RMSE, and MAE. Gradient Boosting performed best, achieving training and testing R² of 0.99 …

Artificial Elections: How Artificial Intelligence Can Affect Elections And Decide Our Future, Ryan D. Granger

Honors Undergraduate Theses

This thesis examines the growing role of artificial intelligence (AI) in democratic elections, highlighting both its transformative potential and its associated risks. Drawing on a qualitative analysis of existing literature, the study explores how AI is increasingly integrated into political campaigns, election administration, and voter engagement. Key benefits include enhanced data analysis, personalized political messaging, and improved efficiency in campaign operations. AI also supports real-time fact-checking and more accurate vote tabulation, which can strengthen transparency and trust in electoral processes. However, the thesis emphasizes that these advantages are accompanied by significant challenges. AI technologies enable the rapid creation and dissemination …

Can An Experienced Qualitative Researcher Distinguish Ai From Human Qualitative Content Analysis?, Alexandra T. Lucas, Jianna Ramos, Maria Bajwa, Aaron Calhoun, Mark W. Scerbo, Janice C. Palaganas

Psychology Faculty Publications

Background

Artificial intelligence (AI) has become increasingly embedded in research workflows. Large language models (LLMs) are being used to code segments of text, organise codes into themes and interpret patterns within contexts. Recent comparisons between human and AI analyses demonstrate up to 80% thematic overlap, yet humans consistently exhibit deeper interpretive integration and contextual understanding. This study assesses whether experienced researchers can distinguish between entirely human-generated and AI-generated qualitative content analyses of a simulation debriefing.

Methods

We conducted a qualitative descriptive study comparing human-generated qualitative content analysis (QCA) with ChatGPT-4o-generated QCA using a single focus group transcript on emotion management …

Biosecure-Llm Framework: Protecting Llms From Cyberbiosecurity Threats And The Case For Independent Ai Safety Governance, Xavier-Lewis Palmer, Lucas Potter, Srdjan Lesaja, Sotirios Karathanasis, Mohammad Ghasemigol

Computer Science Faculty Publications

Large Language Models (LLMs) are becoming critical infrastructure in scientific, healthcare, and governmental contexts. As frontier AI laboratories increasingly partner with government agencies, a fundamental question arises: Who should control the safety and policy-enforcement layers that constrain model behavior? Current safety mechanisms (LLM guardrails) are typically designed for generic "harmlessness" and operate by detecting semantic patterns and refusing requests. However, they are inadequate governance instruments because they cannot implement auditable, domain-specific controls tied to external regulatory policy objects (e.g., control lists or rules governing personally identifying information). Even a perfectly aligned model is not able to express institution-specific policy without …

Cybersecurity Center For Offshore Wind Energy (Final Project Round), Sachin Shetty

Center for Secure and Intelligent Critical Systems (CSICS) Publications

This project establishes a Cybersecurity Center for Offshore Wind Energy with the objective of designing and operating a cyber-physical testbed for wind energy farms (WEFs) that enables comprehensive cybersecurity research. The testbed incorporates a Supervisory Control and Data Acquisition (SCADA) system connected to turbine models via industrial-grade programmable logic controllers (PLCs) and remote terminal units (RTUs). It supports side-channel data acquisition, implementation and analysis of various cyberattack scenarios, and development of attack detection, mitigation, and best-practice guidance tailored to wind energy systems. During the project, the team expanded the number and fidelity of mathematical turbine models (MTMs), integrated these models …

Swimming In Uncertainty: Filling Data Gaps And Providing An Educational Platform For Beach Water Quality At Tybee Island, Georgia, Lukas Roberson

College of Graduate Studies: Theses & Dissertations

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Swimming in beaches water contaminated with high levels of bacteria can make you sick. Current monitoring at the public beaches on Tybee Island consists of weekly monitoring and enumeration of fecal indicator bacteria that takes 24 hours for results. If the number of bacteria exceed regulatory limits, a public health advisory is issued, and affected waters are retested until …

The Artificial Intelligence (Ai) Economy In The Mountain West, 2025, Dre Boyd-Weatherly, Sean Curry, Krish Sharma, Kristian Thymianos, William E. Brown Jr.

Economic Development & Workforce

This fact sheet reports on the artificial intelligence (AI) readiness of five Mountain West metropolitan statistical areas (MSAs): Phoenix-Mesa-Chandler, AZ; Salt Lake City-Murray, UT; Denver-Aurora-Centennial, CO; Las Vegas-Henderson-North Las Vegas, NV; and Albuquerque, NM. Using data from the Brookings Institution's “Mapping the AI Economy” report, the MSAs are benchmarked based on their overall population, employment, talent, adoption, and innovation.

Preparing For The Artificial Intelligence (Ai) Economy In The Mountain West, 2025, Kian Parikh, Taylor Volk, Maisoon Faris, Kristian Thymianos, William E. Brown Jr.

Economic Development & Workforce

This fact sheet presents data from the Brainly report, “Here Are the States Most (and Least) Prepared to Win the AI Race in 2025” for the five Mountain West states of Arizona, Colorado, New Mexico, Nevada, and Utah. This fact sheet highlights the national and individual rankings of four key metrics for each Mountain West state: the fixed percentage of businesses using artificial intelligence (AI); the number of AI jobs per 1,000 workers; the number of AI-related degrees per 10,000 people ages 20-24; and federal funding for small business technology innovation per $1 million of gross domestic product (GDP).

Artificial Intelligence (Ai) And The Anthropic Economic Index In The Mountain West, 2025, Cason Noll, Olivia K. Cheche, William E. Brown Jr.

Economic Development & Workforce

This fact sheet presents 2025 data on the state of artificial intelligence (AI) adoption among the five Mountain West states of Arizona, Colorado, Nevada, New Mexico, and Utah. The data are sourced from the “Anthropic Economic Index,” which provides data on Claude.ai (an AI large language model) and its adoption across all 50 U.S. states and Washington, D.C. This fact sheet focuses on Claude.ai usage, the most common topic Claude.ai has been used for, and augmentation and automation shares for each Mountain West state.

Review Of Ai Needs You: How We Can Change Ai’S Future And Save Our Own, Tracy A. Fernandez Rysavy

Feminist Pedagogy

AI Needs You: How We Can Change AI’s Future and Save Our Own urges citizens to band together now, while A.I. is still in its nascent stages, to head off its potentially destructive repercussions and ensure that the technology serves more than just a wealthy few. While such efforts might seem out of reach in our polarized society, author Verity Harding points to three cases from history where policy was heavily influenced by multistakeholder collaborations. This review encourages educators to use the book as a way to study business ethics; out-of-the-box thinking; and intersectional, inclusive consensus-building over a top-down approach.

Hlcg: A Hierarchical Lane-Changing Gaming Decision Model For Heterogeneous Traffic Flow On Two-Lane Highways, Tianyi Wang, Chong He, Hao Li, Yixuan Li, Yiming Xu, Yangyang Wang, Junfeng Jiao

Research Collection College of Integrative Studies

Discretionary lane-changing behavior is one of the most common highway operations, which seriously affects traffic efficiency and safety. Nowadays, connected and automated vehicles (CAVs) are advancing rapidly, though not yet fully widespread. As a result, a mixed traffic environment with traditional human-driven vehicles (HDVs) and CAVs will persist for the foreseeable future. To achieve effective automatic lane-changing maneuvers, it’s necessary to propose a lane-changing decision model for heterogeneous traffic flow on two-lane highways. This paper firstly extends longitudinal car-following models based on the intelligent driver model and lateral lane-changing models using quintic polynomial curves to accommodate heterogeneous traffic flow, and …

Juxtaposing Approaches To Risk-Based Ai Governance In Different ‘Rights’ Contexts: A Comparative Analysis Between Singapore And The Eu, Jane Loo, Mark Findlay

Research Collection Yong Pung How School Of Law

Comparative analysis of European and certain Asian approaches to governance often degenerates into simplistic dichotomies based on universal human rights assumptions. This chapter rejects such dualities, ill-informed by theory and historical reflection. The emerging argument is founded on a historical realist approach to theorising difference. Assisted by Polanyi’s double movement, the detailed substantive comparison is preceded by considerations of how recent trends in governing AI have uniformly adopted a countermovement against the dis-embedding of data and technology from the social leading to a risk/responsibility paradigm. From here, a more nuanced reflection of AI governance approaches in the EU and Singapore …

A Difficult Act To Maintain, J.G. Allen

Research Collection Yong Pung How School Of Law

In "Where is Singapore's AI regulation headed?" (Issues, Summer 2025), Manoj Harjani starts by recounting its delicate balancing act in governing artificial intelligence. The city-state is able to maintain credibility on the global stage while remaining pragmatically grounded in technical experimentation, maintaining policy realism without overcommitting to rigid legal frameworks. The choice to adopt a regulatory “light touch” is often framed as a way to maintain flexibility, avoid overregulation, and enable innovation. But this obscures the extent to which this posture is itself a political and economic settlement—one that reinforces Singapore’s position in the global digital economy by facilitating capital …

Large Lithium-Ion Battery Model For Secure Shared E-Bike Battery In Smart Cities, Donghui Ding, Zhao Li, Linhao Luo, Ming Jin, Bin Zhu, Yichen Zhong, Junhao Hu, Peng Cai, Huiqi Hu

Research Collection School Of Computing and Information Systems

Electric bikes powered by lithium-ion batteries are increasingly used in smart cities to promote sustainable mobility and efficient delivery services. However, limited battery range and slow plug-in charging remain key challenges. Shared electric bike battery systems, facilitated by battery swapping stations, offer a promising solution by enabling quick and efficient battery replacements. However, their success hinges on accurate anomaly detection, battery health estimation and remain range prediction. These tasks remain challenging due to data scarcity, battery diversity and environmental variability. Here we show that a large-scale lithium-ion battery model trained on over ten million battery time series data enables robust …

Roadside Asset Extraction From Mobile Lidar Point Cloud, Yushin Ahn, Riadh Munjy, Stephen Choi

Mineta Transportation Institute

Mobile LiDAR systems are powerful tools that help us map roads and their surroundings in 3D with great speed and precision. The data provided by these systems support urban planning efforts, digital mapping, transportation infrastructure maintenance, and more. This report presents a comprehensive workflow for roadside asset extraction using Mobile Terrestrial Laser Scanning (MTLS) data, focusing on road lane detection, cross-section slope analysis, and point cloud classification. Roadside asset extraction is the identification and classification of roadside features like signs and poles. The dataset, acquired using a high-resolution mobile LiDAR system, contains over 5.7 billion points (pieces of data) across …

Analysis Of Extended Producer Responsibility In E-Waste Management: Policy Drivers And Challenges In Singapore, Aldy Gunawan, Aidan Marc Wong, Tasaporn Visawameteekul, Minh Phuong Huynh, Linh Chi Tran

Research Collection School Of Computing and Information Systems

This paper examines the role of the Extended Producer Responsibility (EPR) scheme in electronic waste (e-waste) management in Singapore. It investigates the policy drivers and challenges of e-waste management, using data from an online survey to explore the attitudes and behaviors of young consumers, with a particular focus on youth. We employ the Theory of Reasoned Action (TRA) and the Theory of Planned Behavior (TPB) frameworks to develop a model that examines the relationships among attitudes, perceived norms, awareness, and perceived convenience in relation to EPR awareness and perception. The findings highlight the need for customized policies tailored to different …

Integrating Artificial Intelligence And Machine Learning Technologies Into Common Operating Picture And Course Of Action Development, C. Anthony Pfaff, Christopher John Hickey

Books, Monographs & Collaborative Studies

C. Anthony Pfaff and Christopher John Hickey, Principal Investigators

©2025 C. Anthony Pfaff. All rights reserved.

Integrating Artificial Intelligence and Machine Learning Technologies into Common Operating Picture and Course of Action Development explores the potential of artificial intelligence (AI) and machine learning to revolutionize military planning processes by enhancing situational awareness and expediting course of action development within the Joint planning process. The study delves into technical, organizational, and resource considerations that are critical for AI integration. In addition, the study highlights the importance of clean, structured data in training AI systems, addresses challenges in data collection across varying formats …

Du Undergraduate Showcase Abstracts: Research, Scholarship, And Creative Works, Kabe Aberle, Nadia Kako, Kateri Mcrae, Brooke Agulnek, Sky Palmon, Yasmine Ramirez, Francisca Aguirre Beltran, Ashley Juarez, Bridget Kim, Tessa Appel, Sterling Kerr, Spencer Ingley, Gabe Meyer, Robin Tinghitella, Dale Broder, Lily Baeza, Chloe Beers, Julia Coakley, Whitney Kelsey, Sydney Gainforth, Gabi Wing, Audrey Martin, Aaliyah Amore Berry, Brooke Watley, Kiruthika Venkatesan, Rachel Bienstock, Annabella Brotherston, Madison Bryant, Mia Burgener, Emma P. Lieb, Rachel A. Johnson, Jennifer L. Hoffman, Kania Campbell, Kiena Campbell, Courtney Cassidy, Sage Krzyzkowski, Maddox Jones, Skylar Abookire, Luke Hawkins, Sunnah Yoon, Andrea Chu, Yan Qin, Nyah Cubbison, Brian Gearity, Daniel Mcintosh, Mariely Cruz, Edward Garrido, Grady Dionne, Nicole Doris, Lyndsie Salvagio, Ann-Charlotte Granholm-Bentley, Anna Dymov, Hannah Eckert, Gabrielle Welsh, Erica Larson, Charlie Ernst, Anna Zhou, Sarah Watamura, Larissa Fedorovich-Klein, Georgie Fields, Kimberly A. Guevara, Aven Mccall, Ben Peltier, Feruz Yahia, Patrick Flores, Jadyn Floyd, Sophia Forcier, J. Von R. Monteza, Peter Sokol-Hessner, Gwendolyn Geiger, Scott Nichols, Camryn Gunter, Kendal Hengst, Charlie Bednarz, Issy Garside, Addison Baker, Rachel Mina, Brooke Hermanson, Amanda Klingler, William Highfill, Sydney Jaques, Kerstin Lewey, Allison Grossery, Daniel Linseman, Ethan Lim, Jagger Livengood, Owen Mantelli, Gabby Pappas, Abby Mcdonald, Madeleine Dierking, Eve Miller, Emma Loeber, Anna Marlow, Michael Kerwin, Ella Mathews, Hillary Hamann, Khadija Mohamed, Vivian Nguyen, Gabri Notov, Ifunayachi Ogbonna-Ukuku, Sunil Kumar, Charles Baysah, Sarah Olson, Don Sullivan, Anna Paradiso, Jay Parrish, Mira Pronobis, Alisha Pravasi, Kerstin Haring, Diego Ramirez, Christopher Reardon, Juliana Ramirez, Casey Doherty, Ella Kestner, Teagan Weindel, Cate Billings, Pablo Torre-Walter, Lucy Rand, Samantha Reynolds, Mark Siemens, Khadeeja Rashid, Laine Satterlee, Piper Heilbronner, Lily Pound, Ben Whitehurst, Anna Respet, Lizzie Lesoing, Sydney Hertel, Aya Saad-Masri, Brooke Ballenger, Max Proske, Hannah Rosenberg, Ellia Nakahara, Sophia Espinoza, Ivan Woolhouse, Simon Ruland, Gorkem Er, Timothy Sweeny, Melaku Saketa, Michela Schenk, Maren Lynch, Madi Hamm, Grace Schroeder, Michelle Rozenman, Rana Seif, Jackson Hall, Marisela Simental, Daniel Paredes, Aaron Mena, Preston Spaan, Evelyn Stovin, David Andrew Swartz, Anh Tran, Daniel Pittman, Luke Farchione, Emily Boyer, Ukari Verner, Lacey Conrad, Jonathan Velotta, James Weiner, Jagger Gossett, Noah Sherry, Sam Proud, Ben Block, Avi Narayana, Zoey Weiss, Alyssa Wilson, Gabrielle Walsh, David Zonana, Keely Wright, Kena Riveria, Lillybelle Deer, Jena Doom, Elysia Davis, Isabelle Yaremenko, Caitlyn Young

DU Undergraduate Research Journal Archive

Abstracts from the DU Undergraduate Research Showcase.

Thematic Hotspots And Strategy Analysis Of International Ai Regulatory Texts Based On Lda Models, Taitian Mao, Yihe Peng

Journal of Scientific Information Research

[Purpose/significance]This article conducts an in-depth exploration of international artificial intelligence (AI) regulatory policies and gains insights into the regulatory focuses and trends of various countries, with the aim of providing valuable references for global AI governance strategies.

[Method/process] This paper applies the LDA topic clustering analysis method to conduct an in-depth study of twenty-seven international policy documents. The aim is to accurately identify the topics, analyze the key theme words, and further reveal the regulatory hotspots in the field of artificial intelligence.

[Results/conclusion] The study reveals six core regulatory themes: systemic risk assessment, ethical and legal regulation, social impact governance, …

Selected Artificial Intelligence Provisions In U.S. Fiscal Year 2025 National Defense Authorization Act, Bert Chapman

Libraries Faculty and Staff Presentations

The 2025 Fiscal Year National Defense Authorization Act contains multiple provisions relating to artificial intelligence (AI). These congressionally mandated provisions direct various sections of the Department of Defense (DOD) and individual U.S. armed service branches to execute congressional intent for AI policymaking. Examples of such intent include identifying and planning DOD's AI workforce, demonstrating AI biotechnology applications for national security, improving the human usability of AI systems, and establishing an AI security center. This presentation will note that reports on these initiatives must be prepared for relevant congressional oversight committees, and, in many cases, are in many cases, publicly released …