This workshop is the second in the series of workshops organized by Diverse In AI, an affinity group which aims to foster links between participants from underrepresented populations, which in artificial intelligence includes but is not limited to women, LGBTQ+ persons, and people of color (e.g., Black in AI, WiML, LatinX in AI, Queer in AI). Meanwhile, many service and outreach workshops such as Grace Hopper Conference provide opportunities to technologists to understand the needs of underserved populations and in turn give back to these communities. The organizers of this workshop wish to bring together these communities to strive to achieve the intersecting goals through interdisciplinary collaborations. This shall help in the dissemination of benefits to all underserved communities in the field of AI and further help in mentoring students/future technologists belonging to isolated, underprivileged, and underrepresented communities.

Submission Guidelines
Submissions must follow the formatting guidelines for AAAI-2021 (use the AAAI 2021 Author Kit). All papers must be original and not simultaneously submitted to another journal or conference. The following paper categories are welcome:

Long papers (5-8 pages)
Short papers and poster abstracts (2-4 pages)
Contributed talks

Submission Link: https://easychair.org/conferences/?conf=aidbei2021

List of Topics
We invite original contributions that focus on best practices, challenges and opportunities for mentoring from underserved populations, education research pertinent to AI, AI for Good as applicable to underserved students’ communities. In keeping with the organizers’ affiliations with WIML, Black in AI, LatinX in AI, and Queer in AI (whose early presence and development occurred at NeurIPS and ICML), technical areas emphasized will include machine learning with emphasis on natural language processing (NLP), computer vision (CV), and reinforcement learning (RL).

Demographic studies regarding AI applications and/or students underserved populations

Reports of mentoring practice for AI students from underserved populations

Data science and analytics on surveys, assessments, demographics, and all other data regarding diversity and inclusion in AI

Survey work on potential underserved populations, especially undergraduate students from such populations

Fielded systems incorporating AI and experimental results from underserved communities

Emerging technology and methodology for AI in underserved communities
Committees

Program Committee
Dr. Omar U. Florez (Twitter)
Dr. Jessica Elmore (Kansas State University)
Laverne Bitsie-Baldwin (Kansas State University)
Lourdes Ramírez Cerna (Universidad Nacional de Trujillo)
Maria Fernanda De La Torre (Massachusetts Institute of Technology)
Emily Alfs-Votipka (Kansas State University)
Yihong Theis (Kansas State University)

Organizing committee
Deepti Lamba (Kansas State University)
Dr. William Hsu (Kansas State University)
Dr. Pablo Rivas (Baylor University)
Dr. Matias Valdenegro (German Research Center for Artificial Intelligence)
William Agnew (University of Washington)
Louvere Walker-Hannon (MathWorks)

Venue and Format
The conference is a one-day virtual workshop that will include invited talks, contributed talks, spotlight paper presentations, poster presentations, and a panel discussion.

Contact
All questions about submissions should be emailed to dlamba@ksu.edu and bhsu@ksu.edu