Call for Papers for the Special Track on AI for Social Impact

AAAI 2021 will feature a special track on artificial intelligence for social impact (AISI). The track recognizes that high-quality research on social impact domains often leads to papers that differ from traditional AAAI submissions along multiple dimensions. These are motivated by the following issues:

 

• Data collection may be difficult and may require innovative methods and validations, for instance, to address large-scale data gathering in the field, eliminate bias and ensure fairness;
• Problem modeling is a time-intensive activity that requires significant collaborations with domain experts and needs to balance a variety of tradeoffs in decision making;
• Social impact may be realized through time-consuming field studies that typically compare a baseline with the application of novel algorithms in the real world, and the experimental design can be challenging and the evaluation may be multifaceted.

 

The goal of this track at AAAI 2021 is to highlight these technical challenges and opportunities and to showcase the social benefits of artificial intelligence.

 

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 2021 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 all) of these criteria.

 

4. The social impact problem considered by this paper is significant and has not been adequately addressed by the AI community.
3. This paper represents a new take on a significant social impact problem that has been considered in the AI community before
2. The social impact problem considered by this paper has some significance and this paper represents a new take on the problem
1. This paper’s contribution was elsewhere: it follows up on an existing problem formulation or introduces a new problem with limited immediate potential for social impact

 

4. Shows an excellent understanding of other literature on the problem, including that outside computer science
3. Shows a strong understanding of other literature on the problem, perhaps focusing on various subtopics or on the CS literature
2. Shows a moderate understanding of other literature on the topic, but does not engage in depth
1. Does not engage sufficiently with other literature on the topic

 

4. Introduces a new model, data gathering technique, algorithm, and/or data analysis technique
3. Substantially improves upon an existing model, data gathering technique, algorithm, and/or data analysis technique
2. Makes a moderate improvement to an existing model, data gathering technique, algorithm, and/or data analysis technique
1. This paper’s contribution was elsewhere: it employs existing models, data gathering techniques, algorithms, and/or data analysis technique (e.g., the paper presents a new experimental design and evaluation procedure).

4. Thoroughly and convincingly justifies the approach taken, explaining strengths and weaknesses as compared to other alternatives
3. The justification of the approach is convincing overall, but could have been more thorough and/or alternatives could have been considered in more detail
2. The justification of the approach is relatively convincing, but has weaknesses
1. The justification of the approach is flawed and/or not convincing

4. Evaluation was exemplary: data described the real world and was analyzed thoroughly
3. Evaluation was convincing: datasets were realistic; analysis was solid
2. Evaluation was adequate, but had significant flaws: datasets were unrealistic and/or analysis was insufficient
1. Evaluation was unconvincing

 

4. Excellent facilitation of follow-up work: open-source code; public datasets; and a very clear description of how to use these elements in practice
3. 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
2. Adequate facilitation of follow-up work: moderate effort would be required to replicate the results or apply them to a new domain
1. Weak facilitation of follow-up work: considerable effort would be required to replicate the results or apply them to a new domain

 

4. Likelihood of social impact is extremely high: the paper’s ideas are already being used in practice or could be immediately
3. Likelihood of social impact is high: relatively little effort would be required to put this paper’s ideas into practice, at least for a pilot study
2. 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 be implemented in practice
1. Likelihood of social impact is low: the ideas proposed in this paper are unlikely to make a significant impact on the proposed problem

 

This year AAAI-21 is enforcing a strict submission limit. Each individual author is limited to no more than a combined limit of 15 submissions to the AAAI-21 main track and AISI track, and authors may not be added to papers following submission (see the main AAAI-21 Call for Papers for policies about author changes).

 

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, including AISI, especially with regard to submission and deadline information, please refer to the main AAAI-21 Call for Papers at https://aaai.org/Conferences/AAAI-21/aaai21call/.

 

 

 

Concerning author instructions and conference registration, write to aaai21@aaai.org. Concerning suggestions for the program and other inquiries, write to the AAAI-21 AISI Program Cochairs:

 

 

P. Anandan (Wadhwani Institute for Artificial Intelligence, India)
Fei Fang (Carnegie Mellon University, USA)
Eric Sodomka (Facebook Research, USA)