Evaluation of a well-being recommendation app by Telefónica
February 7th, 2020
This paper presents the evaluation (algorithmic audit) of REM!X, a personalized well-being recommendation app developed by Telefónica Innovación Alpha. The main goal of the evaluation was to identify and mitigate algorithmic biases in the recommendation system that could lead to the discrimination of protected groups.
The audit was conducted through a qualitative methodology that included five focus groups with developers and a digital ethnography relying on users comments reported in the Google Play Store. To minimize the collection of personal information, as required by best practice and the GDPR, the REM!X app did not collect gender, age, race, religion, or other protected attributes from its users. This limited the algorithmic assessment and the ability to control for different algorithmic biases. Indirect evidence was thus used as a partial mitigation for the lack of data on protected attributes..