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Artificial Intelligence based systems increasingly use personalization toprovide users with relevant content, products, and solutions. Personalizationis intended to support users and address their respective needs andpreferences. However, users are becoming increasingly vulnerable to onlinemanipulation due to algorithmic advancements and lack of transparency. Suchmanipulation decreases users' levels of trust, autonomy, and satisfactionconcerning the systems with which they interact. Increasing transparency is animportant goal for personalization based systems. Unfortunately, systemdesigners lack guidance in assessing and implementing transparency in theirdeveloped systems.  In this work we combine insights from technology ethics and computer scienceto generate a list of transparency best practices for machine generatedpersonalization. Based on these best practices, we develop a checklist to beused by designers wishing to evaluate and increase the transparency of theiralgorithmic systems. Adopting a designer perspective, we apply the checklist toprominent online services and discuss its advantages and shortcomings. Weencourage researchers to adopt the checklist in various environments and towork towards a consensus-based tool for measuring transparency in thepersonalization community.

Applying Transparency in Artificial Intelligence based Personalization Systems