AISI Timetable for Authors

Note: all deadlines are “anywhere on earth” (UTC-12)

Paper submission is now open!

July 21, 2026
Abstracts due at 11:59 PM UTC-12

July 28, 2026
Full papers due at 11:59 PM UTC-12

July 31, 2026
Supplementary material and code due by 11:59 PM UTC-12

October 19-25, 2026
Author feedback window

November 30, 2026
Notification of final acceptance or rejection

December 14, 2026
Submission of camera-ready files

February 16-23, 2027
AAAI-26 Conference

Note: Deadlines are track-specific and may differ from those listed above. Track-specific deadlines are listed on their respective CFP.

Call for the Special Track on AI for Social Impact

AAAI-27 is pleased to continue a special track focused on Artificial Intelligence for Social Impact (AISI). This track recognizes that high quality research conducted in social impact domains often leads to papers that differ from traditional AAAI submissions in multiple dimensions. We invite authors to submit papers that prioritize and delve deeper into one or more of the following key aspects:

• Data collection: Addressing the challenges associated with gathering data in social impact domains, such as innovative methods, validation techniques, and strategies to mitigate biases and ensure fairness.
• Problem modeling: Recognizing the intricate nature of problem formulation in social impact contexts, which requires close collaborations with domain experts and balancing various trade-offs  in decision-making.
• Field tests and evaluation: Highlighting the significance of rigorous experimentation in real-world settings to assess social impact, encompassing well-designed experimental designs, complex evaluation methodologies, and comprehensive analysis.

The aim of this track at AAAI-27 is to emphasize these technical challenges and opportunities, and to showcase the social benefits of artificial intelligence. 

This page outlines the specific track focus of the Special Track on AI for Social Impact (AISI), as well as review criteria unique to this track. For complete information about the following topics pertaining to all technical tracks and focus areas please refer to the main AAAI-27 website.

Submissions to this special track will follow the regular AAAI technical paper submission procedure but the authors need to select the AISI special track. There will be no transfer of papers between the AAAI-27 main track and the AISI special track; therefore, authors will need to decide to which track they want to submit their paper (note that only this special track offers a set of AISI keywords). Papers submitted to this track will be evaluated using the following criteria which are different from the criteria for the main track. For acceptance into this track, typically we would expect papers to have a high score on some (but not necessarily all) of these criteria. As a reference, papers accepted for AAAI-26 AISI special track can be found here.

Significance of the problem

• Excellent: The social impact problem considered by this paper is significant and has not been adequately addressed by the AI community.
• Good: This paper represents a new take on a significant social impact problem that has been considered in the AI community before.
• Fair: The social impact problem considered by this paper has some significance and this paper represents a new take on the problem.
• Poor: The problem considered by limited immediate potential for social impact.

Engagement with literature

• Excellent: Shows an excellent understanding of other literature on the problem, including that within and outside computer science.
• Good: Shows a strong but less than comprehensive understanding of other literature on the problem, including relevant work both within and outside computer science. 
• Fair: shows a moderate understanding of other literature on the topic, but does not engage in depth or misses some relevant areas entirely (e.g., entirely failing to engage with relevant work outside of computer science).
• Poor: Does not engage sufficiently with other literature on the topic.

Significance to the AI community

Is the work likely to impact what AI researchers do in other application areas? This may be accomplished through traditional technical novelty (examples: new algorithms, models, data gathering techniques, etc), or through improved scientific insight into designing AI in societal settings (examples: improved understanding of problem formulations, empirical evidence on how AI interacts with human decision makers and organizations, evidence on when and why AI improves outcomes in practice).    

• Excellent: Introduces highly novel technical ideas or scientific insights that are likely to significantly impact AI research in other application areas.
• Good: Substantially improves upon existing techniques or contributes scientific understanding with the potential to significantly impact AI research in at least related applications. 
• Fair: Moderately improves on existing techniques or understanding, with some impact on AI research in related application areas.  
• Poor: This paper is unlikely to impact AI research outside its own specific application setting.

Soundness

• Excellent: Claims are well supported by appropriate methods; strengths and weaknesses are carefully evaluated by the authors.   
• Good: The key claims of the paper are well supported but some other aspects are not. 
• Fair: Some important claims are not well supported by the evidence provided
• Poor: Significant portions of the paper not supported with appropriate evidence and methods.

Facilitation of follow-up work

• Excellent: Excellent facilitation of follow-up work: open-source code; public datasets; and a very clear description of how to use these elements in practice.
• Good: Strong facilitation of follow-up work: some elements are shared publicly (data, code, or a running system) and little effort would be required to replicate the results or apply them to a new domain.
• Fair: Adequate facilitation of follow-up work: moderate effort would be required to replicate the results or apply them to a new domain.
• Poor: Weak facilitation of follow-up work: considerable effort would be required to replicate the results or apply them to a new domain.

Scope and promise for social impact:

Is the paper’s research likely to impact practice, for example through real-world deployment of a system, impact on policy, or changing how practitioners use AI in a specific application area?   

• Excellent: Likelihood of social impact is extremely high: the paper’s ideas are already being used in practice or could be immediately.
• Good: Likelihood of social impact is high: the paper’s ideas have a clear path to impact practice.
• Fair: Likelihood of social impact is moderate: this paper gets us closer to its goal, but considerably more work would be required before the paper’s ideas could impact practice.
• Poor: Likelihood of social impact is low: the ideas proposed in this paper are unlikely to make a significant impact on the proposed problem.

Please refer to AAAI-27 Author policies for additional information.

Questions and Suggestions

Concerning author instructions, OpenReview issues, write to workflowchairs@aaai.zendesk.com.

Concerning conference registration, write to aaai27@aaai.org.

Concerning suggestions for the program and other inquiries, write to the AAAI-27 AISI Program Cochairs: aisi27chairs@aaai.org.

Organizers

Andrew Perrault (The Ohio State University, USA)

Bryan Wilder (Carnegie Mellon University, USA)

AI for Social Impact Keywords

AISI: Other Social Impact

AISI: Accessibility & Disability

AISI: Agriculture and Food

AISI: Climate

AISI: Computational Social Science and Humanities

AISI: Disaster Mitigation and Response

AISI: Education

AISI: Energy

AISI: Environmental Sustainability

AISI: Healthcare

AISI: Low and Middle-Income Countries

AISI: Mobility / Transportation

AISI: Natural Sciences

AISI: Philosophical and Ethical Issues

AISI: Policy and Social Development

AISI: Public Health 

AISI: Security and Privacy

AISI: Social Networks and Social Media

AISI: Social Welfare, Justice, Fairness and Equality

AISI: Urban Planning

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