Applying Transparency in Artificial Intelligence based Personalization Systems

TL;DR

This paper proposes a set of best practices and a checklist to help designers evaluate and improve transparency in AI-based personalization systems, aiming to reduce manipulation and increase user trust.

Contribution

It introduces a novel checklist derived from ethics and computer science to assess transparency in personalization algorithms and demonstrates its application on real online services.

Findings

Checklist aids designers in evaluating transparency

Application reveals strengths and weaknesses of current systems

Encourages development of standardized transparency measurement tools

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.