As AI becomes an integral part of our daily lives, we increasingly rely on its suggestions and automated systems to guide decision-making. From healthcare to autonomous driving and even financial planning, AI plays a vital role in helping humans navigate complex environments and make important choices. However, current AI systems are largely based on pattern recognition and correlations within data, which often limits their ability to reason about cause and effect. This reliance on correlation can lead to suboptimal recommendations, particularly in critical decision-making scenarios, where understanding the underlying causal dynamics is essential.
When AI is not grounded in causality, human-AI collaboration becomes riskier. For instance, AI may recommend actions based on patterns that seem effective but overlook key causal relationships, potentially leading to poor or even dangerous decisions. Whether in healthcare, emergency response, or transportation, such decisions could result in adverse outcomes, highlighting the urgent need for AI systems that can reason about causality rather than simply finding correlations.
The primary objectives of this workshop are to:
- Enhance causal reasoning by developing frameworks that enable AI to analyze and interpret causal dynamics in everyday life scenarios, such as driving, emergency response, assisting with navigation, and managing daily tasks.
- Improve human-AI collaboration by investigating methods for AI to effectively communicate causal insights to users, facilitating better decision-making and enhancing situational awareness in complex and high-stakes situations.
- Evaluate AI impact with counterfactual analysis, using it as an evaluation mechanism to assess the effectiveness of causally-aware AI systems. This includes examining how these systems influence user decision-making processes and outcomes in various contexts by simulating alternative scenarios.
We invite contributions from various domains where causal AI could have a significant impact, such as:
- Autonomous systems: Improving safety in self-driving vehicles and robotics.
- Emergency response: Supporting critical decisions in disaster management.
- Urban planning: Developing smarter, safer cities through traffic and infrastructure management.
- Finance: Providing reliable risk assessments and economic forecasting.
- Education: Optimizing learning through adaptive systems that understand causal drivers of student success.
- Healthcare: Enhancing diagnostic accuracy and personalized treatments.
Through this workshop, we aim to foster discussions on building more trustworthy and effective AI systems that can collaborate with humans to make better decisions by reasoning causally.
The deadlines as follows, for all the PerCom workshops:
- Paper submission deadline: December 1, 2024,
November 17, 2024(extended) - Paper notification: January 08, 2025
- Camera Ready Deadline: February 2, 2025
Submission
Submitted papers will be refereed by the workshop Program Committee. You can submit your paper here: EasyChair - PerCom 2025. Authors must select the track: CARD 2025 (Workshop on Leveraging Causal AI for Robust Human-AI Collaboration in Decision-Making).
Accepted original papers will appear in the IEEE PerCom'25 Workshops proceedings published by IEEE Computer Society Press. The papers should be in the IEEE format and should be no more than 6 pages in length. Manuscripts must be formatted in accordance with the IEEE Computer Society author guidelines. The IEEE LaTeX and Microsoft Word templates, as well as related information, can be found at the IEEE Computer Society website.
Besides original papers, we also welcome already published papers and idea papers. However, only original papers will be considered for publication in the workshop proceedings.
Each accepted paper requires a full PerCom registration! No registration is available for workshops only. Workshop papers will be included and indexed in the IEEE digital libraries (Xplore).
For submission of the camera-ready paper, please follow the instructions on the PerCom website: PerCom Website.
POC: Anjon (Sunny) Basak, email: anjon.basak@stormfish.io
Organizing Committee
Anjon (Sunny) Basak, anjon.basak@stormfish.io
Adrienne Raglin, adrienne.raglin2.civ@army.mil