Measuring Diversity of Artificial Intelligence Conferences
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Abstract (arXiv)
The lack of diversity of the Artificial Intelligence (AI) field is nowadays a concern, and several initiatives such as funding schemes and mentoring programs have been designed to overcome it. However, there is no indication on how these initiatives actually impact AI diversity in the short and long term. This work studies the concept of diversity in this particular context and proposes a small set of diversity indicators (i.e. indexes) of AI scientific events. These indicators are designed to quantify the diversity of the AI field and monitor its evolution. We consider diversity in terms of gender, geographical location and business (understood as the presence of academia versus industry). We compute these indicators for the different communities of a conference: authors, keynote speakers and organizing committee. From these components we compute a summarized diversity indicator for each AI event. We evaluate the proposed indexes for a set of recent major AI conferences and we discuss their values and limitations.
Key facts. Cited by 5 papers in the map (data through 14 Oct 2026). First posted on arXiv 20 Jan 2020.
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Citing papers per year: 2022: 3; 2023: 1; 2024: 1.
5 papers.
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- 2022A Human Rights-Based Approach to Responsible AIcited by 20
- 2024Open Challenges on Fairness of Artificial Intelligence in Medical Imaging Applicationscited by 2
- 2022Monitoring Diversity of AI Conferences: Lessons Learnt and Future Challenges in the DivinAI Projectcited by 0
- 2024Open Challenges on Fairness of Artificial Intelligence in Medical Imaging Applications24 Jul 2024
- 2023Towards Measuring the Representation of Subjective Global Opinions in Language Models28 Jun 2023
- 2022SERENGETI: Massively Multilingual Language Models for Africa21 Dec 2022
- 2022A Human Rights-Based Approach to Responsible AI6 Oct 2022
- 2022Monitoring Diversity of AI Conferences: Lessons Learnt and Future Challenges in the DivinAI Project3 Mar 2022