Trust Over Time

Summary

This project anticipates the growing risk for media outlets to publish erroneous information as they rely more and more on algorithms to produce and distribute their content. In particular, it aims to develop and evaluate a content aggregator based on artificial intelligence that combines news stories, archives and social network content, using a design approach that allows users to see in a transparent way the source of journalistic content, as well as the decisions made by the algorithms, in order to increase their perception of trust.

Keywords :
artificial intelligence, deep learning, media, trust, interaction design, user perception
Duration

12 months

People involved

Principal investigators

Nicolas Henchoz (EPFL)

Prof. Daniel Gatica-Perez (Idiap Research Institute)

 

Researchers

Delphine Ribes (EPFL)

Andreas Sonderegger (EPFL)

Lara Défayes (EPFL)

Hélène Portier (EPFL)

Lazar Stojkovic (EPFL)

Thanh-Trung Phan (Idiap Research Institute)

 

Media partners

Vincent Seriot (RTS)

Pietro Rezzonico (RTS)

Academic institutions

EPFL+ECAL Lab (EPFL)

Social Computing Group (Idiap Research Institute)

Media partners

RTS – Radio Télévision Suisse (SRG) – Data and Archives (D+A)

Status

This project started in October 2019 and has been completed

Related call for projects

Helping media fight misinformation and restore the public’s trust