Measuring Diversity of Artificial Intelligence Conferences

TL;DR

This paper introduces a set of diversity indicators to quantify and monitor the diversity of AI conferences across gender, geography, and industry, providing a tool to assess the impact of diversity initiatives.

Contribution

It proposes novel diversity indexes for AI conferences and evaluates their effectiveness in measuring diversity among different community groups.

Findings

Diversity indicators reveal variations across conferences and community groups.

The indexes highlight limitations and potential biases in current diversity assessments.

The approach offers a quantitative method to track diversity trends over time.

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.