Reimagining AI Conference Mission Statements to Promote Inclusion in the Emerging Institutional Field of AI

AI conferences play a crucial role in education by providing a platform for knowledge sharing, networking, and collaboration, shaping the future of AI research and applications, and informing curricula and teaching practices. This work-in-progress, innovative practice paper presents preliminary findings from textual analysis of mission statements from select artificial intelligence (AI) conferences to uncover information gaps and opportunities that hinder inclusivity and accessibility in the emerging institutional field of AI. By examining language and focus, we identify potential barriers to entry for individuals interested in the AI domain, including educators, researchers, practitioners, and students from underrepresented groups. Our paper employs the use of the Language as Symbolic Action (LSA) framework [1] to reveal information gaps in areas such as no explicit emphasis on DEI, undefined promises of business and personal empowerment and power, and occasional elitism. These preliminary findings uncover opportunities for improvement, including the need for more inclusive language, an explicit commitment to diversity, equity, and inclusion (DEI) initiatives, clearer communications about conference goals and expectations, and emphasis on strategies to address power imbalances and promote equal opportunities for participation. The impact of our work is bi-fold: 1) we demonstrate preliminary results from using the Language as Symbolic Action framework to text-analysis of mission statements, and 2) our preliminary findings will be valuable to the education community in understanding gaps in current AI conferences and consequently, outreach. Our work is thus of practical use for conference organizers, engineering and CS educators and other AI-related domains, researchers, and the broader AI community. Our paper highlights the need for more intentional and inclusive conference design to foster a diverse and vibrant community and community of AI professionals. By addressing these information gaps and opportunities, collectively the community can foster a more inclusive and accessible environment for all individuals interested in AI, promoting innovation, collaboration, and progress in the field.

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Publication Details

Published
2024-10-13
DOI
https://doi.org/10.1109/fie61694.2024.10893566
Citations
3
Primary Topic
Big Data and Business Intelligence
Type
article
Field-Weighted Citation Impact
1.59
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article

Reimagining AI Conference Mission Statements to Promote Inclusion in the Emerging Institutional Field of AI

3 citations
Big Data and Business Intelligence
1.59
article

Reimagining AI Conference Mission Statements to Promote Inclusion in the Emerging Institutional Field of AI

article en
3 citations

Abstract

AI conferences play a crucial role in education by providing a platform for knowledge sharing, networking, and collaboration, shaping the future of AI research and applications, and informing curricula and teaching practices. This work-in-progress, innovative practice paper presents preliminary findings from textual analysis of mission statements from select artificial intelligence (AI) conferences to uncover information gaps and opportunities that hinder inclusivity and accessibility in the emerging institutional field of AI. By examining language and focus, we identify potential barriers to entry for individuals interested in the AI domain, including educators, researchers, practitioners, and students from underrepresented groups. Our paper employs the use of the Language as Symbolic Action (LSA) framework [1] to reveal information gaps in areas such as no explicit emphasis on DEI, undefined promises of business and personal empowerment and power, and occasional elitism. These preliminary findings uncover opportunities for improvement, including the need for more inclusive language, an explicit commitment to diversity, equity, and inclusion (DEI) initiatives, clearer communications about conference goals and expectations, and emphasis on strategies to address power imbalances and promote equal opportunities for participation. The impact of our work is bi-fold: 1) we demonstrate preliminary results from using the Language as Symbolic Action framework to text-analysis of mission statements, and 2) our preliminary findings will be valuable to the education community in understanding gaps in current AI conferences and consequently, outreach. Our work is thus of practical use for conference organizers, engineering and CS educators and other AI-related domains, researchers, and the broader AI community. Our paper highlights the need for more intentional and inclusive conference design to foster a diverse and vibrant community and community of AI professionals. By addressing these information gaps and opportunities, collectively the community can foster a more inclusive and accessible environment for all individuals interested in AI, promoting innovation, collaboration, and progress in the field.

Northwestern University (US), University of Georgia (US), University of Calgary (CA), University of Toronto (CA), Francophone University Association (CA), Northwestern University (PH), Kantar Health (United States) (US), Embry–Riddle Aeronautical University (US)
Reduced inequalities
Openalex Percentile: Top 12%
Big Data and Business Intelligence
1.59
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