Work

Best Practices for Transparency in Machine Generated Personalization

Year: 2020

Type: conference-paper

Abstract: 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 and system designers benefit from guidance in implementing transparency in their systems.

Source: ACM Conference on User Modeling, Adaptation and Personalization (UMAP)

DOI: 10.1145/3386392.3397593

Authors: Laura Schelenz, Avi Segal, Kobi Gal

Institutions: University of Tübingen, Ben-Gurion University of the Negev, University of Edinburgh

Language: English

Topic: Ethics and Social Impacts of AI

Subfield: Safety Research

Field: Social Sciences

Domain: Social Sciences

Open Access status: green

Funders: European Commission, Horizon 2020 Framework Programme

Awards: G434337789, WeNet - The Internet of US, G8107045763