Measuring Diversity of Artificial Intelligence Conferences
- URL: http://arxiv.org/abs/2001.07038v4
- Date: Mon, 22 Mar 2021 17:08:41 GMT
- Title: Measuring Diversity of Artificial Intelligence Conferences
- Authors: Ana Freire, Lorenzo Porcaro and Emilia G\'omez
- Abstract summary: We propose 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 evaluate the proposed indexes for a set of recent major AI conferences and we discuss their values and limitations.
- Score: 0.0
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- 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.
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