Machine generated personalization is increasingly used in online systems. Personalization is intended to provide users with relevant content, products, and solutions that address their respective needs and preferences. However, users are becoming increasingly vulnerable to online manipulation due to algorithmic advancements and lack of transparency. Such manipulation decreases users’ levels of trust, autonomy, and satisfaction concerning the systems with which they interact. Increasing transparency is an important goal for personalization based systems. Unfortunately, system designers lack guidance in assessing and implementing transparency in their developed systems. In this podcast, ethics researcher Laura Schelenz and data scientist Avi Segal discuss the relevance and ways to implement transparency in systems that use personalization.
The podcast is based on the research paper “Best Practices for Transparency in Machine Generated Personalization”, which is available online at: https://arxiv.org/abs/2004.00935.