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How algorithms are augmenting the journalistic institution: In search of evidence from newsroom innovation labs Digitalization has dramatically changed newsrooms in recent years; for example, journalists increasingly use tools to gather, write, verify, and disseminate news. These tools, in the form of algorithms, are latent in the news ecosystem and take the form of recommender systems (labeling what is newsworthy), speech-to-text generators (helping with the writing of articles), or metrics systems (measuring reading behavior through audience analytics). However, little research has been done on how journalists interact with these relatively novel algorithms in the (1) news gathering,(2) news production,(3) news verification, and (4) news production and moderation. This dissertation focuses on this journalist-algorithm interaction and enhances our understanding of how these tools are reshaping rather than reinventing the journalistic institution. Based on the results of one theoretical and five empirical studies, this dissertation engages with algorithms and journalism as an institution. The first study proposes a typology that maps the various levels of automation and autonomy in the newsroom. The five-level typology of computational journalism starts from a'manual level'(level 0, where the news worker has full autonomy) and ends with a'full automation support level'(level 4, where the algorithm has full autonomy or decision-making power). In doing so, the interaction between a journalist and an algorithm becomes more granular, and the level of automation and autonomy might affect the journalists' perception of whether or not they will interact with …