With an increasing degree of automation in the systems responsible for content delivery, advertisement platforms and content recommender systems alike are filtering, weighting, and ranking a continuous feed of potential items to provide a tailored experience to each individual based on their personal preferences and past behaviour. The complexity of such systems introduces a sophisticated (and almost totally opaque) new layer to peoples’ ability to access information. Automated decisions drastically impact our access to information and relationship with content serving and journalistic platforms. In many cases, the definition of success for such systems is not based on individual or societal well-being, but rather on some variation of engagement or revenue. A common belief motivating the design and optimization of these algorithms is that more (private) information about an individual equates to a better experience and more valuable advertisement via increasingly specific programmatic micro-targeting. This panel will present a multidisciplinary investigation of the interaction between data collection, the algorithmic nature of content recommendation systems, the commercial forces at play for such platforms and the individual and societal consequences of their prevalence.