AIDBEI 2022: AIDBEI 2022: AAAI 2022 Diversity Workshop on Artificial Intelligence - Diversity, Belonging, Equity, and Inclusion Virtual March 1, 2022

This workshop is the third 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-2022 (use the AAAI 2022 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
• * All times are in PST (+3 EST, +8 UK, +9 CET)

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 potentially 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

• Laverne Bitsie-Baldwin (Kansas State University)
• Dr. Jessica Elmore, Council for Advancement and Support of Education (CASE)
• Yihong Theis (Kansas State University)
• Huichen Yang (Kansas State University)

Organizing committee

• Dr. William Hsu (Kansas State University)
• Huichen Yang (Kansas State University)

Panelists

• Louvere Walker-Hannon (MathWorks)
• Dr. Matias Valdenegro-Toro (German Research Center for Artificial Intelligence)
• William Agnew (University of Washington)
• Hetvi Jethwani (Indian Institute of Technology)

Publication

Workshop papers will be published with the Proceedings of Machine Learning Research (PMLR). Submissions must follow the PMLR standards, and PDF versions should be submitted via EasyChair.

Venue

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 huichen@ksu.edu and bhsu@ksu.edu