The goal of this bridge will be to bring together researchers, practitioners from industry and policymakers to share technical advances and insights on applications and challenges for the application of AI in energy and critical infrastructure systems. The target audience includes AI researchers that are actively working on the use of AI and XAI in energy and critical infrastructure systems, as well as researchers in energy and critical infrastructure systems that would like to explore the potential benefits of applying AI and XAI to these domains. Other stakeholders, including policymakers and regulators interested in XAI, energy and critical infrastructure systems are welcome to join. 
[**Read more...**](https://aaai.org/conference/aaai/aaai-25/aaai-25-bridge-list/#bp09) [**Detailed Program**](https://www.doc.ic.ac.uk/~fleofant/aaai25-xai-eci/index.html#programme)

B9: Explainable AI in Energy and Critical Infrastructure Systems

Over the past couple of years, GenAI models with billions of parameters have become readily available to the general public. In turn, a mixture of tangible results and hype has led to eagerness from the public to use GenAI in many different ways. At the same time, there are various concerns surrounding these models, leading to burgeoning efforts to document and classify their negative impacts. Red-teaming, which typically takes the form of interactive probing, is commonly used as part of these efforts. In order to most effectively uncover potential risks via red-teaming, we strongly believe that a participatory effort is paramount. In particular, with this lab, we seek to leverage the diverse set of skills and background experiences of AAAI conference attendees in order to discover GenAI failures. By providing attendees with varying familiarity with GenAI models and issues with an opportunity to actively red-team generative AI models, we hope to affirm the notion that effective red-teaming requires broad participation and effort. We are confident that our lab will encourage attendees to continue to provide invaluable feedback on the failure modes of these pervasive GenAI models. 
**[Read more...](https://aaai.org/conference/aaai/aaai-25/tutorial-and-lab-list/#LH01)**

LH01: DAMAGeR: Deploying Automatic and Manual Approaches to GenAI Red-teaming

[**Read more...**](https://aaai.org/conference/aaai/aaai-25/tutorial-and-lab-list/#TH01)

TH01: Bridging Inverse Reinforcement Learning and Large Language Model Alignment: Toward Safe and Human-Centric AI Systems

The goal of this tutorial is two-fold. First, we aim to highlight the challenges of training machine learning (ML) models that must be trustworthy in addition to being accurate, especially in the context of limited and low-quality data. Second, we will demonstrate through various examples how data-efficient and label-efficient learning algorithms—such as active learning, semi-supervised learning, and synthetic data generation—can overcome these challenges to produce trustworthy ML models. These methods are pivotal in improving adversarial robustness, handling distribution shifts, ensuring fairness, and preserving privacy. 
[**Read more...**](https://aaai.org/conference/aaai/aaai-25/tutorial-and-lab-list/#TH02)

TH02: Building trustworthy ML: The role of label quality and availability

Today, machine learning and AI are becoming important tools for automating decision-making at an unprecedented rate. This has naturally raised questions regarding whether these decision-making tools treat individuals or groups fairly. While fairness is a nascent subject within the AI/ML literature, it has a long history in economics, specifically in social choice theory (and more recently, in computational social choice), where compelling mathematical notions of fairness, such as envy-freeness and the core, have played an important role in the design of fair collective decision-making algorithms. The tutorial will spotlight a recent emerging literature on adopting fairness notions from social choice to design provably fair AI/ML tools. The advantages of such notions over fairness notions proposed in the ML literature will be discussed, and applications such as classification, clustering, federated learning, multi-armed bandits, and rankings will be covered. 
**[Read more...](https://aaai.org/conference/aaai/aaai-25/tutorial-and-lab-list/#TH03)**

TH03: Fairness in AI/ML via Social Choice

An embodied agent is a generalist agent that can take natural language instructions from humans and perform a wide range of tasks in diverse environments. Recent years have witnessed the emergence of Large Language Models as powerful tools for building Large Agent Models, which have shown remarkable success in supporting embodied agents for different abilities such as goal interpretation, subgoal decomposition, action sequencing, and transition modeling (causal transitions from preconditions to post-effects).

However, moving from Foundation Models to Embodied Agents poses significant challenges in understanding lower-level visual details, and long-horizon reasoning for reliable embodied decision-making. We will cover the advances of the foundation models into Large Language Models Vision-Language Models, and Vision-Language-Action Models. In this tutorial, we will comprehensively review existing paradigms for foundations for embodied agents, and focus on their different formulations based on the fundamental mathematical framework of robot learning, Markov Decision Process (MDP), and present a structured view to investigate the robot’s decision-making process. More info about the tutorial is at https://foundation-models-meet-embodied-agents.github.io. [**Read more...**](https://aaai.org/conference/aaai/aaai-25/tutorial-and-lab-list/#TH04)

TH04: Foundation Models meet Embodied Agents

The rise of foundation models has revolutionized many vision-language tasks, but their application to scientific problems is still in its infancy, presenting vast opportunities for exploration. These models hold immense potential due to their versatile capabilities and the complex, multi-scale nature of real-world scientific challenges. This intersection highlights the transformative possibilities of leveraging foundation models for advancing science. However, significant challenges remain: Where do we draw the boundary for applying foundation models to scientific problems? What scientific tasks are both feasible and equipped with measurable evaluation metrics? How can we effectively utilize the heterogeneous, multi-modal data characteristic of scientific domains? This tutorial will delve into these critical questions, offering in-depth insights and practical guidance. 
[**Read more...**](https://aaai.org/conference/aaai/aaai-25/tutorial-and-lab-list/#TH05)

TH05: Multi-modal Foundation Model for Scientific Discovery: With Applications in Chemistry, Material, and Biology

**[Read more...](https://aaai.org/conference/aaai/aaai-25/tutorial-and-lab-list/#TH06)**

TH06: Pre-trained Language Model with Limited Resources

This tutorial will equip empirical reinforcement learning (RL) researchers, including graduate students, early-career researchers and industry practitioners, with a deep theoretical understanding of offline RL. By explaining the necessary and sufficient conditions for theoretical guarantees, participants will gain insights into the challenges of offline RL compared to supervised learning and online RL, including reliance on bootstrapping targets, partial state-action space coverage, and spurious data. 
**[Read more...](https://aaai.org/conference/aaai/aaai-25/tutorial-and-lab-list/#TQ01)** [**Detailed Program**](https://fengdic.github.io/offlineRL/)

TQ01: Advancing Offline Reinforcement Learning: Essential Theories and Techniques for Algorithm Developers

This tutorial explores the intersection of Semi-Supervised Learning (SSL) and Foundation Models, presenting a unified perspective on leveraging these models for learning with uncertain labels, weak supervision, and noisy data. 
**[Read more...](https://aaai.org/conference/aaai/aaai-25/tutorial-and-lab-list/#TQ02)**

TQ02: Unified Semi-Supervised Learning with Foundation Models

We are pleased to announce the Thirty-Ninth AAAI Conference on Artificial Intelligence (AAAI-25), which will be held in Philadelphia, Pennsylvania at the Pennsylvania Convention Center from February 25 to March 4, 2025.

The purpose of the AAAI conference series is to promote research in Artificial Intelligence (AI) and foster scientific exchange between researchers, practitioners, scientists, students, and engineers across the entirety of AI and its affiliated disciplines. AAAI-25 will feature technical paper presentations, special tracks, invited speakers, workshops, tutorials, poster sessions, senior member presentations, competitions, and exhibit programs, and a range of other activities to be announced.

### [Invited Speakers](https://aaai.org/conference/aaai/aaai-25/aaai-25-invited-speakers/)

Register [here](https://aaai.org/conference/aaai/aaai-25/registration/)

AAAI 2025

The purpose of the AAAI conference series is to promote research in Artificial Intelligence (AI) and foster scientific exchange between researchers, practitioners, scientists, students, and engineers across the entirety of AI and its affiliated disciplines. AAAI-25 will feature technical paper presentations, special tracks, invited speakers, workshops, tutorials, poster sessions, senior member presentations, competitions, and exhibit programs, and a range of other activities to be announced.

