Towards Responsible Media Recommendation

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

Trustworthy journalism through AI

Quality journalism has become more important than ever due to the need for quality and trustworthy media outlets that can provide accurate information to the public and help to address and counterbalance the wide and rapid spread of disinformation. At the same time, quality journalism is under pressure due to loss of revenue and competition from alternative information providers. This vision paper discusses how recent advances in Artificial Intelligence (AI), and in Machine Learning (ML) in particular, can be harnessed to support efficient production of high-quality journalism. From a news consumer perspective, the key parameter here concerns the degree of trust that is engendered by quality news production. For this reason, the paper will discuss how AI techniques can be applied to all aspects of news, at all stages of its production cycle, to increase trust

Towards Responsible Media Recommendation

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

Towards Responsible Media Recommendation

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