IAAI 2025 - Thirty-Seventh Annual Conference on Innovative Applications of Artificial Intelligence (IAAI-25)

The Thirty-Seventh Annual Conference on Innovative Applications of Artificial Intelligence (IAAI-25) is a venue for papers describing highly innovative realizations of AI technology. The objective of the conference is to showcase successful applications and novel uses of AI. The conference will use technical papers, best practice papers, invited talks, and panel discussions to explore issues, methods, and lessons learned in the development and deployment of AI applications; and to promote an interchange of ideas between basic and applied AI and the discourse on the actual deployment of AI in practice. The general goal of the conference is to teach people the challenges and solutions to accomplishing something useful in the real world, as opposed to describing a new algorithm. Papers focusing on algorithmic descriptions, rather than applications, are more appropriate for AAAI-25. All submissions must be original.
IAAI-25 will consider: (1) papers that showcase novel, deployed applications of AI; (2) applications on a trajectory to deployment; (3) papers that present deployed tools for faster AI solutions development and deployment; (4) papers that showcase original ways of integrating methodologies from different areas of AI for practical realization and future deployment; and (5) papers that analyze the factors related to AI incidents and the best practices for preventing or mitigating their recurrence.
Tracks and Topics
1. Deployed Highly Innovative Applications of AI​
Papers submitted to this track must describe *deployed* applications with measurable benefits that include an innovative use of AI technology. Applications are defined as deployed once they are in production use by their final end-users and the in-use experience can be meaningfully collected and reported. The study may evaluate either a stand-alone application or a component of a complex system.
Papers will be judged by the quality of: the task or problem description; the application description; the innovative use of AI technology; the measurable application use and payoff; and the lessons learned during application development, deployment and maintenance. In-depth descriptions of the algorithm are generally more appropriate for AAAI than for IAAI. Each accepted deployed application paper will receive the IAAI ‘Innovative Application’ Award.
Papers in this track may have up to 8 pages in the prescribed AAAI style, plus at most one more page which may only contain references.
2. Emerging Applications of AI
Emerging applications papers ‘bridge the gap’ between basic AI research and case studies of deployed AI applications, by discussing efforts to apply AI tools, techniques, or methods to real-world problems in novel ways. Emerging applications focus on aspects of AI applications that are not yet sufficiently deployed to be submitted as case studies in the Deployed track.
This track is distinguished from reports of purely scientific AI research appropriate for AAAI in that the objective of the efforts reported at IAAI should be the potential application of AI technologies, including engineering considerations. A key requirement for papers is to discuss the path forward for achieving deployment of the technology.
Papers will be judged primarily by the following criteria: significance (of the problem, and the tool or methodology); relevance of AI technology to the problem; innovation; path to deployment; content; evaluation; technical quality; and clarity. Authors are advised to bear these questions in mind while writing their papers. Authors are also advised to consider the novelty of applying AI to the particular problem domain.
Papers in this track may have up to 6 pages in the prescribed AAAI style, plus at most one more page which may only contain references.
3. Deployed Innovative Tools for Enabling AI Applications
Within this track, we solicit papers describing *deployed* tools for improving applied AI innovation and deployment of AI systems. Areas of interest include, but are not limited to:
Process organization: Tools that help manage and assure the development, evaluation or deployment of AI systems.
Data cleaning: Tools to ease the pain point of processing raw data for its use in AI systems.
Modelling: Tools that facilitate AI modelling, for example approaches to facilitate deriving models from examples or demonstrations.
Meta-algorithmics: Tools to improve AI systems, algorithm configurators, algorithm portfolios, and hyper-parameter tuners.
Platforms: Tools in the form of platforms designed for AI research, especially those with connections to real world systems and domains.
Novel computational models: Tools to exploit new computational hardware, for example neuromorphic co-processors, quantum computers, and chips approximating quantum computation.
Interaction: Tools that facilitate the interaction design of or user experience with AI systems, or improve user evaluations.
Trusted AI: Tools that generate explanations, justifications, and persuasions for data-driven models.