Artificial Intelligence and Robotics Commons^™

Holistic & Socially Just Admissions Procedures In An Artificially Intelligent (Ai) World, Lucinda Bratini, Ali Cunningham Abbott, Tracy N. Baker

Faculty and Staff Publications & Presentations

In our ever-evolving social context, the counseling profession continues to center social justice and decolonial praxis in training programs. Simultaneously, we remain attuned to shifts in machine learning and artificial intelligence (AI) which require us to adjust and grow. This roundtable discussion shares the development of a holistic admissions review (HAR) pilot process, which incorporates relational, diversity and social justice values alongside AI innovations and current CACREP considerations.

Ethical Responsibility In The Design Of Artificial Intelligence (Ai) Systems, David K. Mcgraw

International Journal on Responsibility

This article aims to provide an overview of the ethical questions surrounding the responsibilities of designers of artificial intelligence (AI) systems. First, the author delves into the philosophical underpinnings of this responsibility, examining various ethical theories to grasp the moral obligations individuals have towards others and society. The author contends that designers of technology bear the responsibility of considering the broader societal implications of their creations. Subsequently, the author scrutinizes the fundamental question of whether AI systems present unique ethical concerns compared to conventional technologies, pinpointing factors such as complexity, opacity, autonomy, unpredictability, uncertainty, and the potential for significant social …

Simplify Workflows: Ai As A Coding Companion, Tiffany Garrett

Library Scholarship

Artificial Intelligence is on everyone’s minds and has been the topic of the past two Matheson Lectures. But, what is the role of academic health sciences libraries? Moving the theoretical into practical, seven of our colleagues will present real-life case studies. What worked - what didn’t - what would they do differently?

This entry is from one of those case presentations on how a librarian at Roseman University of Health Sciences used AI to complete simple computer programming projects that optimized a few library workflows.

Telu Activation Function For Fast And Stable Deep Learning, Alfredo Fernandez

USF Tampa Graduate Theses and Dissertations

We propose the Hyperbolic Tangent Exponential Linear Unit (TeLU), a neural network hidden activation function defined as $TeLU(x)=x \cdot tanh(e^x)$. TeLU’s design is grounded in the core principles of key activation functions, achieving strong convergence by closely approximating the identity function in its active region while effectively mitigating the vanishing gradient problem in its saturating region. Its simple formulation enhances computational efficiency, leading to improvements in scalability and convergence speed. Unlike many modern activation functions, TeLU seamlessly combines the simplicity and effectiveness of ReLU with the smoothness and analytic properties essential for learning stability in deep neural networks. TeLU’s ability …

Ai-Enabled Methodological Frameworks To Enhance The Robustness Of Machine Learning Models In Various Domains And Dynamic Environments, Mohammad Noroozi

USF Tampa Graduate Theses and Dissertations

This dissertation explores novel approaches to address complex challenges in various domains, focusing on ensuring that machine learning (ML) models can reliably detect both the data they are trained on and unseen data. Reliable detection of unfamiliar inputs is critical for deploying models in real-world applications, where performance can degrade when faced with novel or unexpected information. Each domain addressed in this work introduces a set of contributions aimed at enhancing model performance, broadening generalization through diverse training data, and improving robustness against adversarial attacks. Three sets of contributions are presented, each encapsulated in a chapter.

One of the contributions …

Incorporating Ai Literacy Into Music Library Instruction: An Interactive Discussion, Taylor J. Greene

Library Presentations, Posters, and Audiovisual Materials

Taylor Greene gave a presentation connecting AI Literacy to his work not only as the liaison to the Hall-Musco Conservatory of Music but also more broadly in his role as Chair of Research and Instructional Services. He began by providing an overview of Chapman University’s cautious approach to embracing generative AI and highlighted the library’s role in supporting faculty, staff, librarians, and students in better understanding these technologies and their potential impact on higher education. He summarized the work of the AI Task Force and offered a general overview of the AI Literacy lectures that he and Dr. Doug Dechow …

On The Role Of Prediction In Streaming Hierarchical Learning, Ramy Mounir

USF Tampa Graduate Theses and Dissertations

In today's world, AI systems need to make sense of large amounts of data as it unfolds in real-time, whether it's a video from surveillance and monitoring cameras, streams of egocentric footage, or sequences in other domains such as text or audio. The ability to break these continuous data streams into meaningful events, discover nested structures, and predict what might happen next at different levels of abstraction is crucial for applications ranging from passive surveillance systems to sensory-motor autonomous learning. However, most existing models rely heavily on large, annotated datasets with fixed data distributions and offline epoch-based training, which makes …

From Biased Data Inputs To Your Discriminatory Diagnosis Outputs: A Review Of Legal Liability For Artificial Intelligence In Healthcare, Amber Bolden

Michigan Technology Law Review

While health disparities in America occur due to non-medical circumstances, certain protected classes experience healthcare disparities due to the biases of medical professionals. Biased diagnoses, both intentional or unintentional, have existed throughout the history of the medical profession. That those biases are becoming data for training algorithms raises concerns as the medical field increasingly incorporates and standardizes artificial and augmented intelligence in patient diagnosis and treatment. Currently unregulated but with lifedetermining potential, artificial intelligence (AI) when used in patient treatment leads to important questions: should the doctor, the provider, or the AI developers be liable, and for what? Section II …

Left, Then Right Internal Carotid Artery Dissection: A Case Report, Jeffrey M. Kalczynski, John Douds, Michael E. Silverman

SKMC Student Presentations and Publications

INTRODUCTION: We present a unique case of a patient who presented to the emergency department with stroke-like symptoms found to have a spontaneous, left-sided internal carotid artery dissection (ICAD).

CASE REPORT: The patient was treated successfully with thrombectomy and subsequently developed contralateral symptoms caused by a right-sided ICAD. This was managed with a second contra-lateral thrombectomy. The patient's course was complicated by persistent and mild hypotension, postulated to be secondary to bilateral carotid baroreceptor trauma from the dissections.

CONCLUSION: This case highlights the importance of close neurological monitoring for patients, preferably in a neurologic critical care setting, during and after …

Dc-Instruct : An Effective Framework For Generative Multi-Intent Spoken Language Understanding, Bowen Xing, Lizi Liao, Minlie Huang

Research Collection School Of Computing and Information Systems

In the realm of multi-intent spoken language understanding, recent advancements have leveraged the potential of prompt learning frameworks. However, critical gaps exist in these frameworks: the lack of explicit modeling of dual-task dependencies and the oversight of task-specific semantic differences among utterances. To address these shortcomings, we propose DC-Instruct, a novel generative framework based on Dual-task Inter-dependent Instructions (DII) and Supervised Contrastive Instructions (SCI). Specifically, DII guides large language models (LLMs) to generate labels for one task based on the other task’s labels, thereby explicitly capturing dual-task inter-dependencies. Moreover, SCI leverages utterance semantics differences by guiding LLMs to determine whether …

The Digital Renaissance In Education: Adapting Generative Ai In Pre-Service Teacher And Provider Strategies, Jennifer J. Lesh, Jévaughn J. Lancaster

Faculty and Staff Publications & Presentations

Dr. Lesh's second presentation, "The Digital Renaissance in Education: Adapting Generative AI in Pre-Service Teacher and Provider Strategies," offered insights into the transformative role of generative AI in teacher education. Collaborating with Dr. JeVaughn Lancaster virtually, Lesh and Lancaster shared data from a recent study examining teachers' perceptions of AI in academic research. Findings underscored the potential for AI to enhance educational efficiency while also identifying ethical considerations that must be addressed. Lesh and Lancaster advocated for responsible AI training, stressing that generative AI should augment, not replace, educators' expertise and critical thinking.

Ai And Creativity: Effects Of Culture And Task Emotiveness In Human-Ai Collaboration, Choon Ngee Tan

Dissertations and Theses Collection (Open Access)

Creativity is the driving force behind innovation, propelling individuals and societies toward progress by generating novel ideas and groundbreaking solutions. The emergence of generative AI models, exemplified by GPT-3, offers opportunities to enhance human creativity. This paper explores the potential for unprecedented breakthroughs through the synergy between human intuition and AI-driven creativity, providing practical guidance on leveraging AI to amplify creative capacities. Study 1 finds that AI models trained on data from the U.S. and Chinese cultures exhibit cultural norms, values and cognition of those cultures. Study 2 finds that when humans and AI models of the same culture collaborate …

The Epistemic Role Of Ai Decision Support Systems: Neither Superiors, Nor Inferiors, Nor Peers, Rand Hirmiz

Research Collection School of Social Sciences

Despite the importance of discussions over the epistemic role that artificially intelligent decision support systems ought to play, there is currently a lack of these discussions in both the AI literature and the epistemology literature. My goal in this paper is to rectify this by proposing an account of the epistemic role of AI decision support systems in medicine and discussing what this epistemic role means with regard to how these systems ought to be utilized. In particular, I argue that AI decision support systems are not epistemic superiors, inferiors, or peers. Instead, I recommend that they be classified in …

Collectively Advancing Deep Learning For Animal Detection In Drone Imagery: Successes, Challenges, And Research Gaps, Daniel Axford, Ferdous Sohel, Mathew A. Vanderklift, Amanda J. Hodgson

Research outputs 2022 to 2026

Drones have emerged as a powerful tool in animal detection, significantly advancing wildlife monitoring, conservation, and management by capturing high-resolution, real-time imagery over areas often inaccessible or challenging for human observers to reach. However, manual analysis of drone imagery for animal detection is labour-intensive and time-consuming. The application of deep learning methods, particularly convolutional neural networks, in automating animal detection from drone imagery has the potential to revolutionise wildlife monitoring, conservation, and management protocols. This review provides a comprehensive overview of the increasing use and prospects of deep learning in animal detection using drone imagery. It explores successful applications of …

Toward A Responsible Future: Recommendations For Ai-Enabled Clinical Decision Support, Russell Leftwich, Eileen Koski, Amanda L Joseph, Monica Lopez-Gonzalez, Lee A Fleisher, Kimberly Nolen, Sayon Dutta, Deborah R Levy, Amy Price, Paul J Barr, Jonathan D Hron, Baihan Lin, Gyana Srivastava, Nuria Pastor, Unai Sanchez Luque, Tien Thi Thuy Bui, Reva Singh, Tayler Williams, Mark G Weiner, Tristan Naumann, Dean F Sittig, Gretchen Purcell Jackson, Yuri Quintana

Faculty, Staff and Student Publications

BACKGROUND: Integrating artificial intelligence (AI) in healthcare settings has the potential to benefit clinical decision-making. Addressing challenges such as ensuring trustworthiness, mitigating bias, and maintaining safety is paramount. The lack of established methodologies for pre- and post-deployment evaluation of AI tools regarding crucial attributes such as transparency, performance monitoring, and adverse event reporting makes this situation challenging.

OBJECTIVES: This paper aims to make practical suggestions for creating methods, rules, and guidelines to ensure that the development, testing, supervision, and use of AI in clinical decision support (CDS) systems are done well and safely for patients.

MATERIALS AND METHODS: In May …

Ai And Data Science For Public Policy, Kenneth Benoit

Research Collection School of Social Sciences

Artificial intelligence (AI) and data science are reshaping public policy by enabling more data-driven, predictive, and responsive governance, while at the same time producing profound changes in knowledge production and education in the social and policy sciences. These advancements come with ethical and epistemological challenges surrounding issues of bias, transparency, privacy, and accountability. This special issue explores the opportunities and risks of integrating AI into public policy, offering theoretical frameworks and empirical analyses to help policymakers navigate these complexities. The contributions explore how AI can enhance decision-making in areas such as healthcare, justice, and public services, while emphasising the need …

Food Computing: Domain Adaptation And Causal Inference, Qing Wang

Dissertations and Theses Collection (Open Access)

This dissertation addresses two challenges in food computing: food recognition and food image-to-recipe retrieval. The main research ideas are: (1) leveraging Large Language Models (LLMs) to augment food image representations to mitigate the combined challenges of domain gaps and data imbalance in fine-grained food recognition; (2) proposing a causal-theory inspired cross-modal representation learning formulation for reducing the bias caused by the emphasis on certain ingredients for cross-modal recipe retrieval; and (3) extending the framework to incorporate multiple confounding factors, particularly ingredients and cooking actions, allows for more comprehensive modeling of the food image-torecipe retrieval problem.

We first explore the challenges …

Against The Substitutive Approach To Ai In Healthcare, Rand Hirmiz

Research Collection School of Social Sciences

Paul Bloom has famously argued against the need for empathy in clinicians, while Sally Dalton-Brown has argued that AI need not be capable of empathy to be a good carer. In this paper, the capacity for AI to substitute for human clinicians is assessed from a bioethical perspective, primarily through the evaluation of the arguments put forth by Dalton-Brown and Bloom concerning empathy in healthcare. In opposition to both Bloom and Dalton-Brown, this paper argues that (1) empathy is essential to providing good care or deep care (that is, care that goes beyond the mere fulfilment of medical tasks), (2) …

Don’T Just Say “I Don’T Know”! Self-Aligning Large Language Models For Responding To Unknown Questions With Explanations, Yang Deng, Yong Zhao, Moxin Li, See-Kiong Ng, Tat-Seng Chua

Research Collection School Of Computing and Information Systems

Despite the remarkable abilities of Large Language Models (LLMs) to answer questions, they often display a considerable level of overconfidence even when the question does not have a definitive answer. To avoid providing hallucinated answers to these unknown questions, existing studies typically investigate approaches to refusing to answer these questions. In this work, we propose a novel and scalable self-alignment method to utilize the LLM itself to enhance its response-ability to different types of unknown questions, being capable of not only refusing to answer but also providing explanation to the unanswerability of unknown questions. Specifically, the Self-Align method first employ …

A Survey Of Ontology Expansion For Conversational Understanding, Jinggui Liang, Yuxia Wu, Yuan Fang, Hao Fei, Lizi Liao

Research Collection School Of Computing and Information Systems

In the rapidly evolving field of conversational AI, Ontology Expansion (OnExp) is crucial for enhancing the adaptability and robustness of conversational agents. Traditional models rely on static, predefined ontologies, limiting their ability to handle new and unforeseen user needs. This survey paper provides a comprehensive review of the state-of-the-art techniques in OnExp for conversational understanding. It categorizes the existing literature into three main areas: (1) New Intent Discovery, (2) New Slot-Value Discovery, and (3) Joint OnExp. By examining the methodologies, benchmarks, and challenges associated with these areas, we highlight several emerging frontiers in OnExp to improve agent performance in real-world …