Applying Transparency in Artificial Intelligence based Personalization Systems

• URL: http://arxiv.org/abs/2004.00935v2
• Date: Fri, 21 Aug 2020 13:49:54 GMT
• Title: Applying Transparency in Artificial Intelligence based Personalization Systems
• Authors: Laura Schelenz, Avi Segal, and Kobi Gal
• Abstract summary: Increasing transparency is an important goal for personalization based systems. We combine insights from technology ethics and computer science to generate a list of transparency best practices for machine generated personalization. Based on these best practices, we develop a checklist to be used by designers wishing to evaluate and increase the transparency of their algorithmic systems.
• Score: 5.671950073691286
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Artificial Intelligence based systems increasingly use personalization to provide users with relevant content, products, and solutions. Personalization is intended to support users and 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 work we combine insights from technology ethics and computer science to generate a list of transparency best practices for machine generated personalization. Based on these best practices, we develop a checklist to be used by designers wishing to evaluate and increase the transparency of their algorithmic systems. Adopting a designer perspective, we apply the checklist to prominent online services and discuss its advantages and shortcomings. We encourage researchers to adopt the checklist in various environments and to work towards a consensus-based tool for measuring transparency in the personalization community.

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