AAAI 2024 Workshop on Diversity in Artificial Intelligence
Artificial Intelligence - Diversity, Belonging, Equity, and Inclusion (AIDBEI)
Welcome to AAAI 2024!
The AAAI 2024 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 fifth 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: 9:00 AM Pacific Standard Time |
| End Time: 5:00 PM Pacific Standard Time(PDT) |
| Date: February 22, 2024 |
| PMLR proceedings from last year's workshop at AAAI 2023 |
Call for Participation
This workshop is the fifth 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). ƒMeanwhile, 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
- Dr. William H. Hsu, Kansas State University
- Yihong Theis, Kansas State University
- Lourdes Ramirez Cerna, Universidad de Lima
Organizing committee
- Dr. William H. Hsu, Kansas State University
- Yihong Theis, Kansas State University
- Lourdes Ramirez Cerna, Universidad de Lima
- Jessica Elmore, Senior Director of Cross Cultural Learnings, Council for Advancement and Support of Education (CASE)
Panelists
- Peter-Lucas Jones, Chief Executive Officer of Te Hiku Media
- Andrew Hundt, Computing Innovation Postdoctoral Fellow at the Georgia Institute of Technology
- Dr. Avijit Ghosh - Research Data Scientist at AdeptID and a Lecturer in the Khoury College of Computer Sciences at Northeastern University
- Arjun Subramonian - Machine Learning ResearcherMachine Learning Researcher UCLA-NLP
- Leilani H. Gilpin - Assistant Professor in Computer Science and Engineering and an affiliate of the Science & Justic Research Center at UC Santa Cruz
- Li, Manling - Assistant professor at the Computer Science department of Northwestern University
- Dr Olubayo Adekanmbi, CEO of Data Science Nigeria/DSNai
Workshop Schedule
- *All times are in PDT
- 9:00 - 9:20 Workshop Opening
- 9:20 - 9:50 Session 1: First Invited Talk
- 9:50 - 10:15 Affinity group intros (recorded)
- 10:45 - 11:15 Break
- 11:15 - 11:45 Session 2: Second Invited Talk
- 12:30 - 14:00 Lunch Break
- 14:00 - 14:45 Panel Discussion on Voices in AI: Ethics, Critical Perspectives, and Opportunities
The question this time include:
(i)A key takeaway from the first four workshops is that diversity, equity, and inclusion efforts need to be about more than representation and a "seat at the table", but making sure that voices are heard. What are ways of doing this that you have seen, implemented, or participated in the field of machine learning?
(ii)Data sovereignty and ownership have always loomed large in language technologies - for indigenous cultures, women, Black people and people of color across multiple diasporas, and LGBTQ people. What are specific ways in which the voices of marginalized people can be taken away or taken advantage of using AI - and what do you see as the most important consideration in redressing this? (iii)"AI of, by, and for the people" is a slogan that we have heard in various forms at conferences such as AAAI, but increasingly there is a tension between critical voices and technical contributors. As someone who holds both intersectional roles, what is your message to attendees of AAAi, NeurIPS, ICML, ICLR, IJCAI, AAMAS, etc.? (iv)AI education and access is a perennial cross-cutting topic at this workshop. Our affinity group partners such as LXAI have emphasized the costs of high-performance computing and training as barriers to entry, and some experts such as Pablo Samuel Castro have made a mission out of lowering these barriers by developing technologies such as self-supervision in RL so that the training overhead of models is greatly reduced. What are other ways to do this as LLMs, generative image and video models, XAI, and RL for autonomy dominate a large swath of the industry and academia? (v)share your message about voices of diverse participants in AI research with the AAAI community. What resources, opportunities, ethical challenges, and risks and harms do you think AI researchers do not know, care, or listen enough about in 2024? - 14:45 - 15:15 Mentoring break-outs
- 15:15 - 15:45 Afternoon coffee break
- 15:45 - 16:15 Session 4: Discussion on PMLR proceedings and future AIDBEI workshops
- 16:15 - 17:00 Affinity groups social (hybrid, in Zoom & in person at conference center)
Accepted Papers
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TBD
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.