Association for the Advancement of Artificial Intelligence: AAAI Publications
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Dual-View Interaction-Aware Lane Change Prediction for Autonomous Driving
No full textAs artificial intelligence techniques evolve, we are approaching a critical moment for the widespread deployment of autonomous vehicles. Subsequently, the emergence of mixed-autonomy traffic environments presents formidable challenges to autonomous vehicles, especially for the accurate prediction of lane change intentions of their surrounding human-driven vehicles, which is crucial for ensuring the safety of autonomous vehicles. Existing lane change prediction models mainly focus on capturing the temporal variations in the movement dynamics of individual vehicles. However, the neglect to consider inter-vehicle interactions hinders their capability in complex lane change scenarios, resulting in suboptimal prediction performance. Moreover, current interaction-aware approaches for autonomous driving fail to explicitly model future interactions between vehicles, leading to unreasonable prediction results that can cause collisions between vehicles. To address the above issues, we propose to incorporate the concept of perceived safety into future interaction modeling and design a dual-view interaction-aware lane change prediction model. We evaluate the proposed model on two real-world datasets and experimental results show that the proposed model achieves average improvements of 11.7-12.4% in classification ability and 75.6-95.7% in forecast ability over the best-performing baselines across the two datasets. The ablation study and investigation into future interaction modeling demonstrate that our model has advantages in interpreting lane change scenarios from a driving safety perspective
UrbanWaste: In-the-Bin Dataset for Waste Disposal Inspection with Multi-Granularity Hierarchical Labels
No full textOur world faces the challenge of efficiently and responsibly managing the ever-growing volume of urban waste. Many countries and regions have implemented categorized trash bins and require residents to sort their waste according to specified criteria. Proper waste classification by residents significantly reduces the workload in the waste disposal process. However, due to the lack of effective supervision during classification, the quality of waste sorting is often compromised. This misclassification can lead to higher pollution risks, lower recycling rates, and increased waste management costs and difficulties. To address this issue, we propose using images captured from within trash bins to supervise garbage delivery. We introduce UrbanWaste, an image dataset specifically designed for in-the-bin waste detection and segmentation. The dataset includes 25,254 RGB images and 140,008 annotated items, featuring dense annotations and multi-granularity labels across 193 distinct waste categories. We evaluated state-of-the-art segmentation models to understand their generalization and performance on UrbanWaste. Based on this dataset, we developed a comprehensive workflow for waste classification inspection, which has been deployed in real-world districts to assess the system's effectiveness. We hope UrbanWaste will inspire new directions in AI research for environmental sustainability
Active Geospatial Search for Efficient Tenant Eviction Outreach
No full textTenant evictions threaten housing stability and are a major concern for many cities. An open question concerns whether data-driven methods enhance outreach programs that target at-risk tenants to mitigate their risk of eviction. We propose a novel active geospatial search (AGS) modeling framework for this problem. AGS integrates property-level information in a search policy that identifies a sequence of rental units to canvas to both determine their eviction risk and provide support if needed. We propose a hierarchical reinforcement learning approach to learn a search policy for AGS that scales to large urban areas containing thousands of parcels, balancing exploration and exploitation and accounting for travel costs and a budget constraint. Crucially, the search policy adapts online to newly discovered information about evictions. Evaluation using eviction data for a large urban area demonstrates that the proposed framework and algorithmic approach are considerably more effective at sequentially identifying eviction cases than baseline methods
Trustworthy AI Meets Educational Assessment: Challenges and Opportunities
No full textArtificial intelligence (AI) has made substantial impacts in numerous fields, including education. Within education, learning and assessment are two key areas. Although many AI techniques have been applied to improve teaching and learning, their potential in educational assessment remains underexplored. This paper explores the intersection of AI and educational assessment and presents a rich landscape of challenges and opportunities, especially in the context of trustworthy AI, including fairness, transparency, accountability, explainability, and robustness. We will begin by outlining the foundations of trustworthy AI and educational assessment. Next, we will delve into the application of trustworthy AI for various assessment tasks, such as test item generation, test design, and automated scoring. In addition, the talk will also discuss how insights from educational measurement theory, such as item response theory (IRT) and validity frameworks, can inform the development and evaluation of trustworthy AI models. These frameworks help ensure that AI systems in education are not only accurate, but also equitable and aligned with educational goals. Finally, we will highlight future research directions, focusing on the integration of ethical AI principles into educational technology and the need for interdisciplinary collaboration to tackle the emerging challenges in this field. The aim is to foster a new generation of AI-powered educational tools that are both innovative and trustworthy, ultimately contributing to a more equitable and more effective educational landscape
AI Governance and Lessons Learned as an AI Policy Advisor in the United States Senate
No full textThis talk examines the intersection of artificial intelligence and policymaking, focusing on legislative and regulatory frameworks in the United States. It explores the role of key federal agencies, existing technology-agnostic laws affecting AI, and gaps in regulatory oversight that require legislative intervention. Consumer protection laws are analyzed for their relevance to AI governance, particularly in financial services. The discussion also highlights the implications for AI research, emphasizing the importance of interdisciplinary collaboration between computer scientists and policymakers to ensure responsible AI development that aligns with democratic values and societal interests
Automated, Interpretable, and Scalable Scientific Machine Learning
No full textAlthough Artificial Intelligence (AI) has transformed vision and language modeling, Scientific Machine Learning (SciML) complements data-driven AI via a knowledge-driven approach, enhancing our understanding of the physical world. My work focuses on: 1) automating scientific reasoning with language models, 2) improving geometric interpretation, 3) developing foundation models for multiphysics
Bad AI, Good AI: Rethinking the Agency of Our Artificial Teammates
No full textA prevalent assumption in human-robot and human-AI teaming is that artificial teammates should be compliant and obedient. In this talk, I will question this assumption by presenting the Guide Robot Grand Challenge and discussing the components required to design and build a service robot that can intelligently disobey. This challenge encompasses a variety of research problems, as I will exemplify via three challenges: reasoning about the goals of other agents, choosing when to interrupt, and interacting in a tightly coupled physical environment
Scalable, Sustainable, Generalizable, and Responsible AI for Public Sector
No full textAI for public sector research is about using AI to tackle the numerous challenges faced by public sector organizations when they are out there making our world a better place. AI for public sector research is use-inspired research. It differs from traditional AI research first and foremost in its key objective being measurable societal impact. AI for public sector research contributes to the computing community by proposing new problem models, raising complexities that challenge abstractions which often leads to new methodologies, and introducing new contexts for evaluation. However, fulfilling this promise is easier said than done. This talk consists of three parts about our preliminary work in our long-term quest to make AI for public sector operationally scalable, financially sustainable, technically generalizable, and socially responsible. We will cover (1) a concrete AI for public sector project from problem scoping to field trials and deployment, (2) a generalizable algorithm applicable to various public sector domains, and (3) an overview of our work in a wide variety of applications
Knowledge Tagging with Large Language Model Based Multi-Agent System
No full textKnowledge tagging for questions is vital in modern intelligent educational applications, including learning progress diagnosis, practice question recommendations, and course content organization. Traditionally, these annotations have been performed by pedagogical experts, as the task demands not only a deep semantic understanding of question stems and knowledge definitions but also a strong ability to link problem-solving logic with relevant knowledge concepts. With the advent of advanced natural language processing (NLP) algorithms, such as pre-trained language models and large language models (LLMs), pioneering studies have explored automating the knowledge tagging process using various machine learning models. In this paper, we investigate the use of a multi-agent system to address the limitations of previous algorithms, particularly in handling complex cases involving intricate knowledge definitions and strict numerical constraints. By demonstrating its superior performance on the publicly available math question knowledge tagging dataset, MathKnowCT, we highlight the significant potential of an LLM-based multi-agent system in overcoming the challenges that previous methods have encountered. Finally, through an in-depth discussion of the implications of automating knowledge tagging, we underscore the promising future of deploying LLM-based algorithms in educational contexts
IMQC: A Large Language Model Platform for Medical Quality Control
No full textMedical quality control (MQC) indicators are essential for evaluating the performance of healthcare institutions to ensure high-quality patient care. In this paper, we report the design, implementation, and deployment of the Intelligent EMR-LLM platform for Medical Quality Control (IMQC), a large language model (LLM)-empowered system for automatically computing MQC indicators for enhancing the quality of medical services in Shanghai. It consists of an LLM (i.e., EMR-LLM) for processing electronic medical records (EMRs). With EMR-LLM, IMQC translates existing MQC indicators into a standardized representation language and automatically computes them based on EMRs. Since its deployment in February 2024, IMQC has been adopted by the Shanghai Medical Quality Management Center and associated hospitals. So far, it has processed 1,245 medical quality indicators for secondary- and tertiary-level hospitals, achieving an MQC evaluation accuracy of 93.31%, which is comparable to human experts. It has significantly improved efficiency, increasing from 10 EMRs per hour per human expert to over 1,000 EMRs per hour on average using one single H800 GPU. Over the first round of deployment in Shanghai, it is estimated that IMQC saves around 3.42 million RMB per month in manpower costs compared to traditional reporting methods. The successful deployment of IMQC sets a precedence for other regions to adopt similar AI-driven solutions to enhance medical quality control