Association for the Advancement of Artificial Intelligence: AAAI Publications
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Human-Compatible AI and AI-Powered Science: Insights from AAAI Spring Symposium and Beyond
No full textFor over a decade, we have been organizing and leading AAAI Spring Symposium sessions at the intersection of Well-being and AI, fostering discussions on AI’s ethical, societal, and scientific implications of Artificial Intelligence. This study explores the evolution of key themes, particularly Human-Compatible AI and AI-Powered Science, emphasizing their growing importance in shaping the future of AI.
Human-compatible AI ensures that it aligns with human values while enhancing both individual and societal well-being. It focuses on value alignment, explainability, and mitigation of cognitive biases. Well-being AI, an extension of this concept, focuses not only on fairness, but also active support for psychological, cognitive, and social well-being.
Meanwhile, AI-Powered Science is transforming research paradigms and raising critical questions about reliability, trust, and the role of human intuition in AI-generated knowledge. By integrating AI-Powered Science with Well-being AI, we envision a future in which AI actively contributes to human flourishing.
Drawing on insights from past symposia and personal research, this paper discusses the progression from early concerns about AI fairness to debates on AI’s role in knowledge creation and human-AI collaboration. Finally, it argues for Well-being AI as the next stage in AI’s evolution, emphasizing the necessity of AI systems designed not only for efficiency but also for human happiness and growth
Autonomous Research Assistants for Hybrid Intelligence: Landscape and Challenges
No full textWe present an overview of AI-based tools assisting the research process, analyzing them from the point of view of Hybrid (human-AI) Intelligence (HI). While Autonomous Research Assistants (RAs) are gaining new interest by the latest advancements in AI (cf. Large Language Models), limitations arise when deployed in real-world use-cases.
Starting from the hypothesis that principles from the emerging field of HI could enhance the synergy between researchers and AI tools, we explore what requirements allow to create HI RAs, using a survey of existing systems.
We performed a review of 47 relevant articles published in the last 10 years, and we analyzed them according to various capabilities and characteristics proposed in the Hybrid Intelligence literature. Finally, we identify which future research lines could be followed to develop assistive systems that better combine the capabilities of humans and artificial RAs in a synergistic way
Multiple Distribution Shift - Aerial (MDS-A): A Dataset for Test-Time Error Detection and Model Adaptation
No full textMachine learning models assume that training and test samples are drawn from the same distribution. As such, significant differences between training and test distributions often lead to degradations in performance. We introduce Multiple Distribution Shift - Aerial (MDS-A) - a collection of inter-related datasets of the same aerial domain that are perturbed in different ways to better characterize the effects of out-of-distribution performance. Specifically, MDS-A is a set of simulated aerial datasets collected under different weather conditions. We include six datasets under different simulated weather conditions along with six baseline object-detection models, as well as several test datasets that are a mix of weather conditions that we show have significant differences from the training data. In this paper, we present characterizations of MDS-A, provide performance results for the baseline machine learning models (on both their specific training datasets and the test data), as well as results of the baselines after employing recent knowledge-engineering error-detection techniques (EDR) thought to improve out-of-distribution performance. The dataset is made readily available online
BoolXAI: Explainable AI Using Expressive Boolean Formulas
No full textIn this tool paper, we design, develop, and release BoolXAI, an interpretable machine learning classification approach for Explainable AI (XAI) based on expressive Boolean formulas. The Boolean formula defines a logical rule with tunable complexity according to which input data are classified. Beyond the classical conjunction and disjunction, BoolXAI offers expressive operators such as AtLeast, AtMost, and Choose and their parameterization. This provides higher expressiveness compared to rigid rules- and tree-based approaches. We show how to train BoolXAI classifiers effectively using native local optimization to search the space of feasible formulas. We provide illustrative results on several well-known public benchmarks that demonstrate the competitive nature of our approach compared to existing methods. Our work is embodied in the open-source BoolXAI library with a high-level user interface to serve researchers and practitioners. BoolXAI can be used either as a standalone interpretable classifier or for post-hoc explanations of other black-box models or observed behavior. We highlight several desirable benefits of our tool, especially in industrial settings where rapid experimentation, reusability, reproducibility, deployment, and maintenance are of great interest. Finally, we showcase a deployed service powered by BoolXAI as an enterprise application
The Essentials of AI for Life and Society: An AI Literacy Course for the University Community
No full textWe describe the development of a one-credit course to promote AI literacy at the University of Texas at Austin. In response to a call for the rapid deployment of class that would serve a broad audience in Fall of 2023, we designed a 14-week seminar-style course that incorporated an interdisciplinary group of speakers who lectured on topics ranging from the fundamentals of AI to societal concerns including disinformation and employment. University students, faculty, and staff, and even community members outside of the University were invited to enroll in this online offering: The Essentials of AI for Life and Society. We collected feedback from course participants through weekly reflections and a final survey. Satisfyingly, we found that attendees reported gains in their AI literacy. We sought critical feedback through quantitative and qualitative analysis, which uncovered challenges in designing a course for this general audience. We utilized the course feedback to design a three-credit version of the course that is being offered in Fall of 2024. The lessons we learned and our plans for this new iteration may serve as a guide to instructors designing AI courses for a broad audience
“From Unseen Needs to Classroom Solutions”: Exploring AI Literacy Challenges & Opportunities with Project-Based Learning Toolkit in K-12 Education
No full textAs artificial intelligence (AI) becomes increasingly central to various fields, there is a growing need to equip K-12 students with AI literacy skills that extend beyond computer science. This paper explores the integration of a Project-Based Learning (PBL) AI toolkit into diverse subject areas, aimed at helping educators teach AI concepts more effectively. Through interviews and co-design sessions with K-12 teachers, we examined current AI literacy levels and how teachers adapt AI tools like the AI Art Lab, AI Music Studio, and AI Chatbot into their course designs. While teachers appreciated the potential of AI tools to foster creativity and critical thinking, they also expressed concerns about the accuracy, trustworthiness, and ethical implications of AI-generated content. Our findings reveal the challenges teachers face, including limited resources, varying student and instructor skill levels, and the need for scalable, adaptable AI tools. This research contributes insights that can inform the development of AI curricula tailored to diverse educational contexts
An XAI Social Media Platform for Teaching K-12 Students AI-Driven Profiling, Clustering, and Engagement-Based Recommending
No full textThis paper presents an explainable AI (XAI) education tool designed for K-12 classrooms, particularly for students aged 11-16. The tool was designed for interventions on the fundamental processes behind social media platforms, focusing on four AI- and data-driven core concepts: data collection, user profiling, engagement metrics, and recommendation algorithms. An Instagram-like interface and a monitoring tool for explaining the data-driven processes make these complex ideas accessible and engaging for young learners. The tool provides hands-on experiments and real-time visualizations, illustrating how user actions influence their personal experience on the platform as well as the experience of others. This approach seeks to enhance learners' data agency, AI literacy, and sensitivity to AI ethics. The paper includes a case example from 12 two-hour test sessions involving 209 children, using learning analytics to demonstrate how they navigated their social media feeds and the browsing patterns that emerged
Toward Causal Generative Modeling: From Representation to Generation
No full textDeep learning has given rise to the field of representation learning, which aims to automatically extract rich semantics from data. However, there have been several challenges in the generalization capabilities of deep learning models. Recent works have highlighted beneficial properties of causal models that are desirable for learning robust models under distribution shifts. Thus, there has been a growing interest in causal representation learning for achieving generalizability in tasks involving reasoning and planning. The goal of my dissertation is to develop theoretical intuitions and practical algorithms that uncover the nature of causal representations and their applications. In my work, I focus on causal generative modeling with an emphasis on either representation or generation. For representation learning, I investigate the disentanglement of causal representations through the lens of independent causal mechanisms. For generation tasks, I develop algorithms for counterfactual generation under weak supervision settings by leveraging recent advances in generative modeling. The proposed approaches have been empirically shown to be effective in achieving disentanglement and generating counterfactuals
Towards Trustworthy, Efficient, and Scalable Machine Learning
No full textThroughout the development of machine learning, researchers have increasingly focused on the challenges of trustworthiness, efficiency, and scalability. Our research specifically addresses these critical aspects
Delete-Free Planning with Object Creation is Undecidable
No full textIn planning with object creation, actions might introduce new objects as part of their effect. While this makes the formalism more expressive, it also renders the plan existence problem undecidable. A natural next step is to ask whether simpler fragments and relaxations are still undecidable when powered with object creation. Probably the most popular fragment is delete-free planning, where actions can only add but never delete atoms. In this work, we show that delete-free planning with object creation is still undecidable. We do so by reducing the problem of deciding whether a given atom is reached by the chase procedure to the plan existence problem. Our result implies that heuristics based on the delete relaxation may not be immediately useful for the object creation setting. We then highlight which restrictions we can apply to make delete-free planning with object creation practical