Studies suggest that one in three US adults use the Internet to diagnose or learn about a health concern. However, such access to health information online could exacerbate the disparities in health information availability and use. Health information seeking behavior (HISB) refers to the ways in which individuals seek information about their health, risks, illnesses, and health-protective behaviors. For patients engaging in searches for health information on digital media platforms, health literacy divides can be exacerbated both by their own lack of knowledge and by algorithmic recommendations, with results that disproportionately impact disadvantaged populations, minorities, and low health literacy users. This study reports on an exploratory investigation of the above challenges by examining whether responsible and representative recommendations can be generated using advanced analytic methods applied to a large corpus of videos and their metadata on a chronic condition (diabetes) from the YouTube social media platform. The paper focusses on biases associated with demographic characters of actors using videos on diabetes that were retrieved and curated for multiple criteria such as encoded medical content and their understandability to address patient education and population health literacy needs. This approach offers an immense opportunity for innovation in human-in-the-loop, augmented-intelligence, bias-aware and responsible algorithmic recommendations by combining the perspectives of health professionals and patients into a scalable and generalizable machine learning framework for patient empowerment and improved health outcomes.

研究探讨了数字媒体平台上慢性病（糖尿病）视频的候选人群中人口统计学特征的偏见，并通过对其元数据进行高级分析方法的应用，探讨了负责任和代表性的建议是否能够生成。

On Curating Responsible and Representative Healthcare Video
  Recommendations for Patient Education and Health Literacy: An Augmented
  Intelligence Approach

通过增强智能方法策划负责和代表性医疗视频推荐，用于患者教育和健康文化