Association for the Advancement of Artificial Intelligence: AAAI Publications
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Optimizing Vital Sign Monitoring in Resource-Constrained Maternal Care: An RL-Based Restless Bandit Approach
No full textMaternal mortality remains a significant global public health challenge. One promising approach to reducing maternal deaths occurring during facility-based childbirth is through early warning systems, which require the consistent monitoring of mothers' vital signs after giving birth. Wireless vital sign monitoring devices offer a labor-efficient solution for continuous monitoring, but their scarcity raises the critical question of how to allocate them most effectively. We devise an allocation algorithm for this problem by modeling it as a variant of the popular Restless Multi-Armed Bandit (RMAB) paradigm. In doing so, we identify and address novel, previously unstudied constraints unique to this domain, which render previous approaches for RMABs unsuitable and significantly increase the complexity of the learning and planning problem. To overcome these challenges, we adopt the popular Proximal Policy Optimization (PPO) algorithm from reinforcement learning to learn an allocation policy by training a policy and value function network. We demonstrate in simulations that our approach outperforms the best heuristic baseline by up to a factor of 4
Making Transparency Advocates: An Educational Approach Towards Better Algorithmic Transparency in Practice
No full textConcerns about the risks and harms posed by artificial intelligence (AI) have resulted in significant study into algorithmic transparency, giving rise to a sub-field known as Explainable AI (XAI). Unfortunately, despite a decade of development in XAI, an existential challenge remains: progress in research has not been fully translated into the actual implementation of algorithmic transparency by organizations. In this work, we test an approach for addressing the challenge by creating transparency advocates, or motivated individuals within organizations who drive a ground-up cultural shift towards improved algorithmic transparency.
Over several years, we created an open-source educational workshop on algorithmic transparency and advocacy. We delivered the workshop to professionals across two separate domains to improve their algorithmic transparency literacy and willingness to advocate for change. In the weeks following the workshop, participants applied what they learned, such as speaking up for algorithmic transparency at an organization-wide AI strategy meeting. We also make two broader observations: first, advocacy is not a monolith and can be broken down into different levels. Second, individuals' willingness for advocacy is affected by their professional field. For example, news and media professionals may be more likely to advocate for algorithmic transparency than those working at technology start-ups
AI Toolkit: Libraries and Essays for Exploring the Technology and Ethics Behind AI
No full textIn this paper we describe the development and evaluation of AITK, the Artificial Intelligence Toolkit. This open-source project contains both Python libraries and computational essays (Jupyter notebooks) that together are designed to allow a diverse audience with little or no background in AI to interact with a variety AI tools, exploring in more depth how they function, visualizing their outcomes, and gaining a better understanding of their ethical implications. These notebooks have been piloted at multiple institutions in a variety of humanities courses centered on the theme of responsible AI. In addition, we conducted usability testing of AITK. Our pilot studies and usability testing results indicate that AITK is easy to navigate and effective at helping diverse users gain a better understanding of AI and its ethical implications. Our goal, in this time of rapid innovations in AI, is for AITK to provide an accessible resource for faculty from any discipline looking to incorporate AI topics into their courses and for anyone eager to learn more about AI on their own
Understanding K-12 Teachers’ Needs for AI Education: A Survey-Based Study
No full textWith the rapid rise of AI technologies such as ChatGPT, understanding and integrating AI into K-12 education has become increasingly important. However, teachers often lack the AI literacy necessary to navigate these tools, which can lead to the perpetuation of misconceptions and biases in the classroom. This study seeks to identify K-12 teachers’ self-identified needs regarding AI education and compare them with existing research on professional development (PD) for AI integration. We surveyed 34 K-12 teachers to assess their knowledge of AI, identify areas where they require further support, and evaluate the relevance of current PD offerings. Our findings reveal a significant disconnect between the top-down assumptions of expert-driven PD initiatives and the practical needs articulated by teachers. Key themes emerged, including a diverse range of AI understanding among educators, a strong preference for hands-on, practical training, and a demand for ongoing institutional support. Additionally, teachers expressed a desire for collaborative learning environments to share strategies and experiences related to AI. This study underscores the importance of tailoring PD programs to address the unique contexts and challenges faced by educators, advocating for a more personalized approach that fosters confidence and competence in AI integration. By aligning PD offerings with teachers’ needs, we aim to enhance their ability to effectively utilize AI tools in the classroom, ultimately enriching the educational experience for students
Statistical Methodologies for Decision-Making and Uncertainty Reduction in Machine Learning
No full textWhile advances in machine learning and the expansion of massive datasets have significantly improved predictive accuracy, the translation of these predictions into actionable decisions—alongside a robust understanding of associated risks—remains underexplored. My research focuses on developing methodology and theory in data-driven decision-making and uncertainty quantification that effectively address core data challenges. This paper presents two connected pillars of my research: data-driven contextual optimization, uncertainty quantification and reduction
scMBERT: A Pre-Trained Deep Learning Model for Single-Cell Multiomic Data Representation and Prediction (Student Abstract)
No full textRecent advancements in single-cell sequencing technologies enable the measurement of multiple modalities in individual cells, offering insights into the transcriptome and regulome in various biological systems and human diseases in an unprecedented resolution. However, effectively using these ultra-high-dimensional and large-scale multiomic data to understand gene regulation remains challenging. Inspired by the success of adapting large language models into the genomics field, we develop scMBERT, a BERT framework-based pre-trained deep learning model using single-cell multiomic data. We showed that scMBERT increases model flexibility and performance in downstream tasks like cell type annotation and batch-effect correction, demonstrating the potential of leveraging multiomic data to improve single-cell genomic data analyses
Automatic Summarization of Long Documents (Student Abstract)
No full textA vast amount of textual data is added to the internet daily, making utilization and interpretation of textual data difficult and cumbersome.
As a result, automatic text summarization is crucial for extracting relevant information, saving precious time.
Although many transformer models excel in summarization, they are constrained by their input size, preventing them from processing texts longer than their context size.
This study introduces several novel algorithms that allow any LLM to efficiently overcome its input size limitation, effectively utilizing its full potential without any architectural modifications.
We test our algorithms on texts with more than 70,000 words, and our experiments show a significant increase in BERTScore with competitive ROUGE scores
A Nature-Inspired Colony of Artificial Intelligence System with Fast, Detailed, and Organized Learner Agents for Enhancing Diversity and Quality
No full textThe concepts of convolutional neural networks (CNNs) and multi-agent systems are two important areas of research in artificial intelligence (AI). In this paper, we present an approach that builds a CNN-based colony of AI agents to serve as a single system and perform multiple tasks (e.g., predictions or classifications) in an environment. The proposed system impersonates the natural environment of a biological system, like an ant colony or a human colony. The proposed colony of AI that is defined as a role-based system uniquely contributes to accomplish tasks in an environment by incorporating AI agents that are fast learners, detailed learners, and organized learners. These learners can enhance their localized learning and their collective decisions as a single system of colony of AI agents. This approach also enhances the diversity and quality of the colony of AI with the help of Genetic Algorithms and their crossover and mutation mechanisms. The evolution of fast, detailed, and organized learners in the colony of AI is achieved by introducing a unique one-to-one mapping between these learners and the pretrained VGG16, VGG19, and ResNet50 models, respectively. This role-based approach creates two parent-AI agents using the AI models through the processes, called the intra- and inter-marriage of AI, so that they can share their learned knowledge (weights and biases) based on a probabilistic rule and produce diversified child-AI agents to perform new tasks. This process will form a colony of AI that consists of families of multi-model and mixture-model AI agents to improve diversity and quality. Simulations show that the colony of AI, built using the VGG16, VGG19, and ResNet50 models, can provide a single system that generates child-AI agents of excellent predictive performance, ranging between 82% and 95% of F1-scores, to make diversified collective and quality decisions on a task
A Narrative Agent for the “Family Story Hoard”: An Information-Theoretic Framework for Interactive Storytelling
No full textWe introduce a narrative agent designed to facilitate the creation and preservation of personal stories within the "Family Story Hoard." The agent engages elderly users in interactive dialogues to elicit life stories, stores narrative elements, and evaluates the completeness of the story using information-theoretic metrics alongside Todorov’s five-stage narrative structure. The system dynamically guides users to fill narrative gaps through tailored prompts, ensures privacy by anonymizing sensitive data during storytelling, and automates archive with generated titles and chronological organization. By integrating conversational AI, information theory, and narrative analysis, this framework supports memory preservation while addressing challenges in coherence, completeness, and user privacy
Progressive Distribution Matching for Federated Semi-Supervised Learning
No full textFederated Learning (FL) enables collaborative learning from distributed data while preserving the privacy of participating clients. While supervised federated learning with labeled data has made notable strides and achieved success, federated semi-supervised learning (FSSL) lags in its progress. Existing works for FSSL heavily rely on fully-labeled clients, while ignoring the distribution of pseudo-labels generated from skewed unlabeled data. In this work, we offer empirical and theoretical insights into the challenges encountered when applying conventional semi-supervised algorithms in the federated regime. Specifically, we highlight how the inherent data heterogeneity in FSSL can exacerbate issues within the pseudo-labeling process. Motivated by these observations, we propose federated learning with progressive distribution matching (FedPDM) to regularize the distribution of pseudo-labels, aiming to progressively reshape it to align with the ground-truth distribution. The matching problem could be formulated as an optimal transport (OT) problem and efficiently solved by Sinkhorn-Knopp iteration. Through extensive experiments, we demonstrate the superiority of FedPDM on a variety of models and datasets compared with prior arts for FSSL