Call for Papers 
The NeurIPS 2021 Workshop on AI for Credible Elections: A Call to Action 
We invite papers that describe innovative use of AI technology or techniques in election processes. The workshop is intended to provide a forum for discussing new approaches and challenges in integrating AI into running elections, and for exchanging ideas about how to move the area forward. The workshop plans to work with AI Magazine to invite select contributions for a proposed special issue on the subject" 
Description: Artificial Intelligence and machine learning have transformed modern society. It also impacts how elections are conducted in democracies, with mixed outcomes. For example, digital marketing campaigns have enabled candidates to connect with voters at scale and communicate remotely during COVID-19, but there remains widespread concern about the spread of election disinformation as the result of AI-enabled bots and aggressive strategies. 
In response, this workshop will examine the challenges of credible elections globally in an academic setting with apolitical discussion of significant issues. The speakers, panels and reviewed papers will discuss current and best practices in holding elections, tools available for candidates and the experience of voters. They will highlight gaps and experience regarding AI-based interventions and methodologies. To ground the discussion, the invited speakers and panelists are drawn from three International geographies: US -representing one of the world’s oldest democracies; India -representing the largest democracy in the world; and Estonia ­representing a country using digital technologies extensively during elections and as a facet of daily life. The workshop will welcome contributions on all technological and methodological aspects of elections and voting. 
The workshop welcomes contributions on all aspects of elections and voting, but especially focus on the use of AI in the following: 
• For election candidates 

o Organizing candidate campaigns 

o Detecting, informing and managing mis and disinformation 

• For election organizers 

o Identifying and validating voters 

o Informing people about election information 

• For voters 

o Knowing about election procedures 

o Verifying individual and community votes 

o Navigating candidates and issues 

• Cross-cutting 

o Promoting transparency in the election process 

o Technology for data management and validation 

o Case-studies of success or failure, and the reasons thereof 

The intended audience of the workshop are students, academic researchers, professionals involved in technology for election management and informed voters. 

Paper preparation instructions 
Submission Format: either extended abstracts (4 pages) or full papers (8 pages) anonymised using the NeurIPS 2021 style guidelines found here. 
Papers may contain an unlimited number of pages for references and appendices. The latter may not necessarily be read by the reviewers. We request and recommend that authors rely on the supplementary material only to include minor details (e.g., hyperparameter settings, reproducibility information, etc.) that do not fit in the page limit. The submission process is double-blind. 
All accepted papers will be presented in a virtual poster session. We welcome articles currently under review or papers planned for publication elsewhere. Submissions site: 

Program/Presentation Format: to be determined 
Publication: There will be no formal publication of workshop proceedings. However, the accepted papers will be made available online on the workshop website and will count as non-archival reports to allow submissions to future conferences/journals. 

Important Dates 
Workshop paper submissions due: September 30, 2021 Notification to authors: October 22, 2021 Camera-ready copies of authors’ papers: Nov 8, 2021 Early-bird registration to the conference Workshop date: December 13 or 14, 2021 (date to be announced) 

Workshop Organizers 
Biplav Srivastava (University of South Carolina), Anita Nikolich (University of Illinois-Urbana Champaign), Huan Liu (Arizona State University), Natwar Modani (Adobe Research), Tarmo Koppel (University of South Carolina and Tallinn University of Technology)