AIDBEI 2024 : AIDBEI2024: Diversity Workshop on Artificial Intelligence - Diversity, Belonging, Equity, and Inclusion

7th DiverseInAI.org Workshop on Artificial Intelligence - Diversity, Belonging, Equity, and Inclusion (AIDBEI)

AAAI 2024 workshop proposal

Description of Workshop

This workshop is the seventh 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, BIPOC persons, LGBTQ+ persons, persons with disabilities (e.g., Black in AI, WiML, LatinX in AI, Queer in AI, Indigenous in AI, Disability in AI). Meanwhile, many service and outreach workshops such as the Grace Hopper Conference (GHC) 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.

Call for Papers

The purpose of the workshop is to increase the diversity, belonging, equity, and inclusiveness (DBEI) of AAAI by providing peer review, mentoring, critical feedback, and shepherding of papers relevant to the main conference. To support this mission, DiverseInAI.org welcomes papers in two modalities: technical papers and topics of specific relevance to the specialized topic of the workshop (“AI for good in DBEI”).

Technical Papers



In keeping with the organizers’ affiliations with its affinity group partners, technical areas emphasized will include machine learning with emphasis on natural language processing (NLP), computer vision (CV), and reinforcement learning (RL).

Topics of Specific Relevance to AIDBEI

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
Documentation of risks and harms, and ethical impacts of AI with respect to marginalized peoples
Workshop Logistics

The workshop will be a full-day event featuring morning and afternoon sessions. In the spirit of fostering new collaborations and meaningful exchange of ideas, care will be taken to allocate sufficient time for discussions and questions. Since, the workshop will be a virtual event, time-allocation for talks, panel discussion, poster session, and Q&A sessions will be done to accommodate maximum participation. The program committee will aim at accepting papers for long papers (5-8 pages), short papers (2-4 page abstracts) and contributed talks. The contributions should 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.

The workshop will begin with brief welcoming remarks, followed by a 3 to 4-hour session of invited talks, contributed talks, and half the oral presentations. The second session will include the second half of papers, followed by an optional poster session. This session will also include a panel discussion on a topic of current interest, which has been at every annual AAAI workshop since its inception in 2000. The session will conclude with an online social event open to everyone, using the gather town software for one-to-one and many-to-many interaction and networking through multiple communities. A mentoring sub-event will be offered to junior attendees during the online social event in order to encourage interactions.

Target Audience

The target audience consists of artificial intelligence educators, practitioners, and students (both graduate and undergraduate) who have an interest in the application of AI to serve underserved populations in the field of AI. Intersectionality of students who themselves belong to groups that are presently underrepresented in AI research, and possess a cultural heritage or ethnic origin that is related to such underserved populations, is recognized and emphasized.

Expected Participation

Participation of 20 - 50 AAAI attendees is expected.

Call for Participation

An open call for presentations, demos, and challenge talks will be posted as for a technical workshop, additional outreach to invited speakers and participants.

Expected Timeline

CFP: Tuesday 17 Oct 2023; 2-page presentation proposals due: Tuesday 28 Nov 2023; decision: Friday 01 Dec 2023; talk abstracts, short papers, slides due: Monday 11 Dec 2023.

Funding Needed / Potential Sources of Funding

No funding is needed for the workshop itself.

Relevant Past Workshops

Recent Events Related to Proposed Topic (current and last two years, reverse chronological order)

AAAI 2023 : 6th AIDBEI workshop, 11 Feb 2023 - https://kdd.cs.ksu.edu/Workshops/AAAI-2023/
IJCAI 2022: 5th AIDBEI workshop, 23 JUly, 2022 - https://kdd.cs.ksu.edu/Workshops/IJCAI-AIDBEI-2022/
AAMAS 2022: 4th AIDBEI workshop, 09 May 2022 – https://kdd.cs.ksu.edu/Workshops/AAMAS-AIDBEI-2022/
AAAI 2022: 3rd AIDBEI workshop, 01 Mar 2022 – https://kdd.cs.ksu.edu/Workshops/AAAI-2022/
AAAI 2021: 2nd AIDBEI workshop, 09 Feb 2021 – https://kdd.cs.ksu.edu/Workshops/AAAI-2021/
AAAI 2020: Artificial Intelligence - Diversity, Belonging, Equity, and Inclusion (AIDBEI): Mentoring Students from Underserved Populations, 07 Feb 2020 – https://kdd.cs.ksu.edu/Workshops/AAAI-2020/
Organizing Committee (Tentative)

Yihong Theis, Doctoral Candidate, Computer Science, Kansas State University
Phone (mobile): +1 785 317 1631; E-mail: [email protected]

Dr. William Hsu, Professor, Computer Science, Kansas State University,
Phone (work/mobile): +1 785 236 8247; E-mail: [email protected]

Dr. Pablo Rivas, Assistant Professor, Computer Science, Baylor University,
Phone (mobile): +1 (845) 867-6873; E-mail: [email protected]

Jessica Elmore, Associate director of diversity programs, Kansas State University
Laura Montoya, President of Latinx in AI
Lourdes Ramírez Cerna, Lecturer, Universidad Nacional de Trujillo
Avijit Ghosh, Research Data Scientist, AdeptID
Michael Running Wolf, PHD Student, McGill University, Founder, Indigenous in AI
Laverne Bitsie-Baldwin, Director of Multicultural Engineering Program, Kansas State University
Savannah Thais, Research Scientist, Columbia University in the City of New York
Timnit Gebru, Founder & Executive Director, The Distributed AI Research Institute (DAIR)
Sanmi Koyejo - Assistant Professor, Stanford University
Arjun Subramonian, Machine Learning Researcher, UCLA-NLP
Ushnish Sengupta, Senior Artificial Intelligence Researcher, MediaTek Research
William Agnew, Postdoctoral Researcher, Computer Science, University of Washington
Kalika Bali, Principal Researcher, Microsoft Research India
Deb Raji, Fellow, Mozilla
Margaret Mitchell, Researcher and Chief Ethics Scientist, Hugging Face
Joy Buolamwini, Artist-in-Chief and President, The Algorithmic Justice League
Program Committee (Tentative)

Dr. Jessica Elmore, Associate director of diversity programs, Kansas State University
Laverne Bitsie-Baldwin, Director of Multicultural Engineering Program, Kansas State University
Lourdes Ramírez Cerna, Lecturer, Universidad Nacional de Trujillo
Yihong Theis, Doctoral Student, Computer Science, Kansas State University
Dr. Matias Valdenegro, Researcher, German Research Center for Artificial Intelligence
Arjun Subramonian, Machine Learning Researcher, UCLA-NLP
Ushnish Sengupta, Senior Artificial Intelligence Researcher, MediaTek Research
Hetvi Jethwani, Queer in AI
Michael Running Wolf from Indigenous in AI
Louvere Walker-Hannon, Black in AI
Anoush Najarian, Software Engineering Manager, MATLAB Performance Team, MathWorks
Kalika Bali, Principal Researcher, Microsoft Research India