IDEAS home Printed from https://ideas.repec.org/p/ipt/iptwpa/jrc137550.html
   My bibliography  Save this paper

Diversity in Artificial Intelligence Conferences

Author

Listed:

Abstract

This report provides an overview of the divinAI project and provides a set of diversity indicators for seven core artificial intelligence (AI) conferences from 2007 to 2023: the International Joint Conference on Artificial Intelligence (IJCAI), the Annual Association for the Advancement of Artificial Intelligence (AAAI) Conference, the International Conference on Machine Learning (ICML), Neural Information Processing Systems (NeurIPS) Conference, the Association for Computing Machinery (ACM) Recommender Systems (RecSys) Conference, the European Conference on Artificial Intelligence (ECAI) and the European Conference on Machine Learning/Practice of Knowledge Discovery in Databases (ECML/PKDD). We observe that, in general, Conference Diversity Index (CDI) values are still low for the selected conferences, although showing a slight temporal improvement thanks to diversity initiatives in the AI field. We also note slight differences between conferences, being RecSys the one with higher comparative diversity indicators, followed by general AI conferences (IJCAI, ECAI and AAAI). The selected Machine Learning conferences NeurIPS and ICML seem to provide lower values for diversity indicators. Regarding the different dimensions of diversity, gender diversity reflects a low proportion of female authors in all considered conferences, even given current gender diversity efforts in the field, which is in line with the low presence of women in technological fields. In terms of country distribution, we observe a notable presence of researchers from the EU, US and China in the selected conferences, where the presence of Chinese authors has increased in the last few years. Regarding institutions, universities and research centers or institutes play a central role in the AI scientific conferences under analysis, and the presence of industry seems to be more notable in machine learning conferences. An online dashboard that allows exploration and reproducibility complements the report.

Suggested Citation

  • GOMEZ Emilia & PORCARO Lorenzo & FRAU AMAR Pedro & VINAGRE Joao, 2024. "Diversity in Artificial Intelligence Conferences," JRC Research Reports JRC137550, Joint Research Centre.
  • Handle: RePEc:ipt:iptwpa:jrc137550
    as

    Download full text from publisher

    File URL: https://publications.jrc.ec.europa.eu/repository/handle/JRC137550
    Download Restriction: no
    ---><---

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:ipt:iptwpa:jrc137550. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Publication Officer (email available below). General contact details of provider: https://edirc.repec.org/data/ipjrces.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.