The amount and dissemination rate of media content accessible online is nowadays overwhelming. Recommender Systems filter this information into manageable streams or feeds, adapted to our personal needs or preferences. It is of utter importance that algorithms employed to filter information do not distort or cut out important elements from our perspectives of the world. Under this principle, it is essential to involve diverse views and teams from the earliest stages of their design and development. This has been highlighted, for instance, in recent European Union regulations such as the Digital Services Act, via the requirement of risk monitoring, including the risk of discrimination, and the AI Act, through the requirement to involve people with diverse backgrounds in the development of AI systems. We look into the geographic diversity of the recommender systems research community, specifically by analyzing the affiliation countries of the authors who contributed to the ACM Conference on Recommender Systems (RecSys) during the last 15 years. This study has been carried out in the framework of the Diversity in AI - DivinAI project, whose main objective is the long-term monitoring of diversity in AI forums through a set of indexes.

翻译：如今，在线可获取的媒体内容数量与传播速度令人应接不暇。推荐系统将这些信息过滤为适应个人需求或偏好的可管理信息流。至关重要的是，用于信息过滤的算法不应扭曲或剔除我们认知世界视角中的重要元素。基于这一原则，在设计开发的最初阶段就需纳入多元观点和团队。这一点已在近期欧盟法规中得以强调，例如《数字服务法》要求进行包括歧视风险在内的风险监测，以及《人工智能法案》要求在AI系统开发中吸纳不同背景人员。本研究聚焦推荐系统研究社区的地理多样性，具体通过分析过去15年间向ACM推荐系统会议（RecSys）投稿作者所属国家展开。此项研究属于"人工智能多样性（DivinAI）"项目框架，该项目核心目标是通过一系列指标对AI论坛的多样性进行长期监测。