论文标题
衡量人工智能会议的多样性
Measuring Diversity of Artificial Intelligence Conferences
论文作者
论文摘要
如今，人工智能（AI）领域的多样性缺乏多样性，这是一个关注的问题，诸如资助计划和指导计划之类的几项举措旨在克服它。但是，尚无迹象表明这些计划在短期和长期内如何实际影响AI的多样性。这项工作研究了这种特定情况下的多样性概念，并提出了AI科学事件的一小部分多样性指标（即索引）。这些指标旨在量化AI场的多样性并监视其演变。我们认为在性别，地理位置和业务方面（被理解为学术界与行业的存在）方面的多样性。我们为会议的不同社区计算这些指标：作者，主旨演讲者和组织委员会。从这些组件中，我们为每个AI事件计算一个汇总的多样性指标。我们评估了一组最近的主要AI会议的拟议索引，并讨论了它们的价值和局限性。
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.
