Measuring Diversity of Artificial Intelligence Conferences
This work addresses the lack of measurable diversity metrics in AI, which is a concern for the field's inclusivity and long-term impact.
The paper tackles the problem of measuring diversity in AI conferences by proposing a set of indicators for gender, geography, and business sectors, and applies them to recent major conferences to quantify and monitor diversity.
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.