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.

Presentation of the project results at the IMI Annual Event (24.09.2020)

Watch on YouTube

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)

 

Scientists

Delphine Ribes (EPFL)

Andreas Sonderegger (EPFL)

Lara Défayes (EPFL)

Hélène Portier (EPFL)

Thanh-Trung Phan (Idiap Research Institute)

 

Student

Lazar Stojkovic (EPFL)

 

Media partners

Vincent Seriot (RTS)

Pietro Rezzonico (RTS)

Academic institutions

EPFL+ECAL Lab (EPFL)

Social Computing Group (Idiap Research Institute)

Media partner

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