Towards responsible media recommendation

Abstract Reading or viewing recommendations are a common feature on modern media sites. What is shown to consumers as nowadays often automatically determined by AI algorithms, typically with the goal of helping discover relevant content more easily. However, highlighting filtering information that comes such may lead undesired effects even society, for example, when an algorithm leads creation filter bubbles amplifies spread misinformation. These well-documented phenomena create need improved mechanisms responsible recommendation, which avoid negative recommender systems. In this research note, we review threats and challenges result from use automated recommendation technology, outline possible steps mitigate societal in future.