AAAI 2021 Workshop on Diversity in Artificial Intelligence
Artificial Intelligence - Diversity, Belonging, Equity, and Inclusion (AIDBEI)
Welcome to AIDBEI 2021!
The AAAI 2021 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. AIDBEI 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).
| Place: Virtual |
| Start Time: 7:30 AM Pacific Time (PT) |
| End Time: 1:30 PM Pacific Time (PT) |
| Date: February 9, 2021 |
| Submission Deadline: December 29, 2020 (Extended) |
| Notification Due: January 21, 2021 |
| Final Version Due: January 31, 2021 |
Call for Participation
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.
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. Omar U. Florez, Twitter
- Lourdes Ramírez Cerna, Universidad Nacional de Trujillo
- Dr. Jessica Elmore, Kansas State University
- Laverne Bitsie-Baldwin, Kansas State University
- Maria Fernanda De La Torre, Massachusetts Institute of Technology
- Emily Alfs-Votipka, Kansas State University
- Yihong Theis, Kansas State University
- Huichen Yang, Kansas State University
Organizing committee
- Deepti Lamba, Kansas State University
- Dr. William H. Hsu, Kansas State University
- William Agnew, University of Washington
- Dr. Matias Valdenegro-Toro, German Research Center for Artificial Intelligence
- Dr. Pablo Rivas, Baylor University
- Louvere Walker-Hannon, MathWorks
- Huichen Yang, Kansas State University
Panelists
- Louvere Walker-Hannon
- Dr. Matias Valdenegro-Toro
- William Agnew
Workshop Schedule
- *All times are in PST (+3 EST, +8 UK, +9 CET)
- 07:30 - 07:45 Workshop Opening
- 07:45 - 08:00 TBD
- 08:00 - 09:40 Session 1
- 08:00 - 08:25 Waking up to Marginalization: Public Value Failures in Artificial Intelligence and Data Science - Thema Monroe-White, Brandeis Marshall and Hugo Contreras-Palacios
- 08:25 - 08:50 Socioeconomic factors impacting students' learning during COVID-19 in India - Chahat Tandon, Arpita Telkar, Sanjana R R, Pratiksha Jayesh Bongale, Hemant Palivela and Nirmala C R.
- 08:50 - 09:15 DeBiasLy: Detecting Bias in Lyrics of Hinglish Item Songs - Vishal Bhalla
- 09:15 - 09:40 Measuring Diversity of Artificial Intelligence Conferences - Ana Freire, Lorenzo Porcaro and Emilia Gomez.
- 09:40 - 10:10 Panel Discussion on Mentoring: Higher Education, Industry, Allyship, Impact, and Resources
- 10:15 - 10:30 Invited talk
- 10:30 - 10:45 Break
- 10:45 - 12:25 Session 2
- 10:45 - 11:10 Analyzing Toxicity in Online Gaming Communities - Ayushi Ghosh
- 11:10 - 11:35 PreDefense: Defending Underserved AI Students and Researchers from Predatory Conferences - Thomas Chen
- 11:35 - 12:00 Characterizing Intersectional Group Fairness with Worst-Case Comparisons - Avijit Ghosh, Lea Genuit and Mary Reagan.
- 12:00 - 12:25 Working Set Selection to Accelerate SVR Training - Pablo Rivas
- 12:25 - 1:00 Mentoring Session
- 1:00 - 1:30 Online Social
Accepted Papers
- Waking up to Marginalization: Public Value Failures in Artificial Intelligence and Data Science - Thema Monroe-White, Brandeis Marshall and Hugo Contreras-Palacios
- Socioeconomic factors impacting students' learning during COVID-19 in India - Chahat Tandon, Arpita Telkar, Sanjana R R, Pratiksha Jayesh Bongale, Hemant Palivela and Nirmala C R.
- Analyzing Toxicity in Online Gaming Communities - Ayushi Ghosh
- Characterizing Intersectional Group Fairness with Worst-Case Comparisons - Avijit Ghosh , Lea Genuit and Mary Reagan.
- Working Set Selection to Accelerate SVR Training - Pablo Rivas
- Measuring Diversity of Artificial Intelligence Conferences - Ana Freire, Lorenzo Porcaro and Emilia Gomez.
- DeBiasLy: Detecting Bias in Lyrics of Hinglish Item Songs - Vishal Bhalla.
- PreDefense: Defending Underserved AI Students and Researchers from Predatory Conferences - Thomas Chen.
Publication
Workshop papers will be published with the Proceeedings 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 dlamba@ksu.edu and bhsu@ksu.edu.