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AAAI 2023 Workshop on Diversity in Artificial Intelligence

Artificial Intelligence - Diversity, Belonging, Equity, and Inclusion (AIDBEI)

Welcome to AAAI 2023!

The AAAI 2023 Workshop on Artificial Intelligence Diversity, Belonging, Equity, and Inclusion (AIDBEI) is a one-day virtual event at the International Conference of the Association for the Advancement of Artificial Intelligence. 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. Diverse in AI was founded with the support and participation of our allies from Black in AI, WiML, LatinX in AI, Queer in AI, {Dis}Ability in AI, Indigenous in AI, and the Black in X Network.


Place: Virtual
Start Time: 7:30 AM Pacific Time (PT)
End Time: 1:30 PM Pacific Time (PT)
Date: February 11, 2023
PMLR proceedings from last year's workshop at AAAI 2022
Submission Deadline: January 22, 2023
Notification Due: January 27, 2023
Preprint Due: February 1, 2023
Camera-Ready Copy Due for PMLR Proceedings: TBD

Call for Participation

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.

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).

List of Topics

  • 1. Demographic studies regarding AI applications and/or students underserved populations
  • 2. Reports of mentoring practice for AI students from underserved populations
  • 3. Data science and analytics on surveys, assessments, demographics, and all other data regarding diversity and inclusion in AI
  • 4. Survey work on potential underserved populations, especially undergraduate students from such populations 
  • 5. Fielded systems incorporating AI and experimental results from underserved communities
  • 6. Emerging technology and methodology for AI in underserved communities

Submission Guidelines

All papers must be original and not simultaneously submitted to another journal or conference. Submissions should use the AAAI conference Author Kit.

The following paper categories are welcome:
  • Long papers (5 - 8 pages)
  • Short papers and poster abstracts (2-4 pages)
  • Contributed talks

Committees

Program Committee

  • Bethany Chamberlain, IT University of Copenhagen
  • Dr. Jessica Elmore, Council for Advancement and Support of Education (CASE)
  • Laverne Bitsie-Baldwin, Kansas State University
  • Dr. Shiri Dori-Hacohen, University of Connecticut
  • Veronika Cheplygina, IT University of Copenhagen
  • Yihong Theis, Kansas State University
  • Huichen Yang, Kansas State University

Organizing committee

  • Dr. William H. Hsu, Kansas State University
  • Huichen Yang, Kansas State University

Panelists

  • Hetvi Jethwani - Queer in AI, Indian institute of technology, Delhi, Indian
  • Maria Skoulidarou - Disability in AI, University of Cambridge, UK
  • Laura Montoya - LXAI, Accel AI Institute and LatinX in AI (LXAI)
  • Shawn Tsosie, Jeff Doctor - Indigenous in AI

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.

Contact Us

All questions about submissions should be emailed to huichen@ksu.edu, physician@ksu.edu and bhsu@ksu.edu.