AI conferences are pivotal spaces for knowledge exchange, collaboration, and shaping the trajectory of research, practice, and education. This paper presents preliminary findings from an analysis of AI conference mission statements, investigating how their stated goals affect who is welcomed into AI conversations. We find that many mission statements reflect assumptions that may unintentionally narrow participation and reinforce disciplinary and institutional silos. This limits engagement from a broad range of contributors—including educators, students, working professionals, and even younger users —who are essential to a thriving AI ecosystem. We advocate for clearer framing that supports democratizing and demystifying AI. By broadening participation and intentionally fostering cross-sector and interdisciplinary connections, AI conferences can help unlock more innovation.
Category: Sector: Inclusion
Hard Questions: Inclusion
-
Developing the Permanent Symposium on AI (poster): Presented at Engineering and Public Policy Division (EPP) Poster Session
A multidisciplinary, reflective autoethnography by some of the people who are building the Permanent Symposium on AI. Includes the history of the project.
RQ 1: Challenges that unite AI policy & tech
RQ 2: How to design the PSAI?
RQ 3: What factors influence the adoption and scalability of the PSAI?
This is the Flagship project of the Aula Fellowship.
-
Saptarishi Futures: An Indian Intergenerational Wayfinding Framework
An Intergenerational Future Study model contextualized within Indian mythology, folklore, and generational value systems. This fusion explores ancient cultural wisdom and modern anticipatory governance to imagine just, inclusive, and regenerative futures across generations.
-
Towards Real Diversity and Gender Equality in Artificial Intelligence
This is an Advancement Report for the Global Partnership on Artificial Intelligence (GPAI) project “Towards Real Diversity and Gender Equality in Artificial Intelligence: Evidence-Based Promising Practices and Recommendations.” It describes, at a high level, the strategy, approach, and progress of the project thus far in its efforts to provide governments and other stakeholders of the artificial intelligence (AI) ecosystem with recommendations, tools, and promising practices to integrate Diversity and Gender Equality (DGE) considerations into the AI life cycle and related policy-making. The report starts with an overview of the human rights perspective, which serves as the framework upon which this project is building. By acknowledging domains where AI systems can pose risks and harms to global populations, and further, where they pose disproportionate risks and harms to women and other marginalized populations due to a lack of consideration for these groups throughout the AI life cycle, the need to address such inequalities becomes clear.
-
Presenting to the United Nations
Our Director, Tammy Mackenzie, was honoured to present our recommendations to the United Nations Committee on the Formation of a Scientific Panel on AI. We recommended that the committee include civil society in these works and that meetings should be held in countries where safe travel can be guaranteed for delegates. You can consult our recommendations here.
See the PDF of the Consultation here: Google Drive
-
World AI: Women in AI
The Aula Fellowship were present, to discuss social and environmental concerns to do with the marketing of AI as a panacea. The event brings together technology companies, civil society, and decision-makers. We were able to connect with other non-profits and universities in this sector, and to build collaborations with several attendees and presenters.
-
‘Mind the gap’: artificial intelligence and journalism training in Southern African journalism schools
This article examines journalism schools (J-schools) responses to the Artificial Intelligence (AI) ‘disruption’. It critically provides an exploratory examination of how J-Schools in Southern Africa are responding to the AI wave in their journalism curriculums. We answer the question: How are Southern African J-Schools responding to AI in their curriculums? Using a disruptive innovation theoretical lens and through documentary review of university teaching initiatives and accredited journalism curriculums, augmented by in-depth interviews, we demonstrate that AI has opened up new horizons for journalism training in multi-dimensional ways. However, this has brought challenges, including covert forms of resistance to AI integration by some Journalism educators. Furthermore, resource constraints and the obduracy of J-schools’ curriculums also contribute to the slow introduction of AI in J-schools.
-
Les leviers du pouvoir dans l’IA
La camaraderie Aula est fièr de commanditer la conférence annuel la faculté du campus Saint Jean de l’Université de l’Alberta. Notre directrice, Tammy Mackenzie, y présente les leviers du pouvoir dans l’IA dans la société albertaine et de la francopĥonie. Le tout dans le but de remmettre le pouvoir décisionnel dans les mains des gens qui sont impliqués: nous tous.
Pour en savoir plus, voir: https://www.ualberta.ca/en/campus-saint-jean/congress/index.html
-
Options and Motivations for International AI Benefit Sharing
Advanced AI systems could generate substantial economic and other societal benefits, but these benefits may not be widely shared by default. For a range of reasons, a number of prominent actors and institutions have called for efforts to expand access to AI’s benefits. In this report, we define the concept of international AI benefit sharing (“benefit sharing”) as efforts to support and accelerate international access to AI’s economic or broader societal benefits. Calls for benefit sharing typically invoke at least one of three motivations: 1) supporting inclusive economic growth and sustainable development, 2) fostering technological self-determination in low- and middle-income countries, and 3) advancing geopolitical objectives, including strengthening international partnerships on AI governance. Notably, as a subset of the third motive, some powerful actors – like the US government – may support benefit sharing as a tool to further their economic and national security interests. Benefit sharing could be implemented by (1) sharing AI resources (e.g., computing power or data), (2) expanding access to AI systems, or (3) transferring a portion of the financial proceeds from AI commercialisation or AI-driven economic growth. Depending on the objective that benefit sharing is intended to achieve, each of these approaches offers distinct opportunities and implementation challenges. These challenges include the potential for some benefit-sharing options to raise security concerns and increase certain global risks. Actors interested in benefit sharing may consider implementing low-risk forms of benefit sharing immediately, while launching cooperative international discussions to develop more comprehensive, mutually-beneficial initiatives.
-
IndicMMLU-Pro: Benchmarking Indic Large Language Models on Multi-Task Language Understanding
Known by more than 1.5 billion people in the Indian subcontinent, Indic languages present unique challenges and opportunities for natural language processing (NLP) research due to their rich cultural heritage, linguistic diversity, and complex structures. IndicMMLU-Pro is a comprehensive benchmark designed to evaluate Large Language Models (LLMs) across Indic languages, building upon the MMLU Pro (Massive Multitask Language Understanding) framework. Covering major languages such as Hindi, Bengali, Gujarati, Marathi, Kannada, Punjabi, Tamil, Telugu, and Urdu, our benchmark addresses the unique challenges and opportunities presented by the linguistic diversity of the Indian subcontinent. This benchmark encompasses a wide range of tasks in language comprehension, reasoning, and generation, meticulously crafted to capture the intricacies of Indian languages. IndicMMLU-Pro provides a standardized evaluation framework to push the research boundaries in Indic language AI, facilitating the development of more accurate, efficient, and culturally sensitive models. This paper outlines the benchmarks’ design principles, task taxonomy, and data collection methodology, and presents baseline results from state-of-the-art multilingual models.