Measuring Diversity of Artificial Intelligence Conferences

Ana Freire, Lorenzo Porcaro, Emilia Gómez
Proceedings of 2nd Workshop on Diversity in Artificial Intelligence (AIDBEI), PMLR 142:39-50, 2021.

Abstract

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.

Cite this Paper

BibTeX
@InProceedings{pmlr-v142-freire21a, title = {Measuring Diversity of Artificial Intelligence Conferences}, author = {Freire, Ana and Porcaro, Lorenzo and G\'{o}mez, Emilia}, booktitle = {Proceedings of 2nd Workshop on Diversity in Artificial Intelligence (AIDBEI)}, pages = {39--50}, year = {2021}, editor = {Lamba, Deepti and Hsu, William H.}, volume = {142}, series = {Proceedings of Machine Learning Research}, month = {09 Feb}, publisher = {PMLR}, pdf = {https://proceedings.mlr.press/v142/freire21a/freire21a.pdf}, url = {https://proceedings.mlr.press/v142/freire21a.html}, abstract = {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.} }
Endnote
%0 Conference Paper %T Measuring Diversity of Artificial Intelligence Conferences %A Ana Freire %A Lorenzo Porcaro %A Emilia Gómez %B Proceedings of 2nd Workshop on Diversity in Artificial Intelligence (AIDBEI) %C Proceedings of Machine Learning Research %D 2021 %E Deepti Lamba %E William H. Hsu %F pmlr-v142-freire21a %I PMLR %P 39--50 %U https://proceedings.mlr.press/v142/freire21a.html %V 142 %X 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.
APA
Freire, A., Porcaro, L. & Gómez, E.. (2021). Measuring Diversity of Artificial Intelligence Conferences. Proceedings of 2nd Workshop on Diversity in Artificial Intelligence (AIDBEI), in Proceedings of Machine Learning Research 142:39-50 Available from https://proceedings.mlr.press/v142/freire21a.html.

Related Material