  This work presents an initial proof of concept of how Music Emotion
Recognition (MER) systems could be intentionally biased with respect to
annotations of musically induced emotions in a political context. In specific,
we analyze traditional Colombian music containing politically charged lyrics of
two types: (1) vallenatos and social songs from the "left-wing" guerrilla
Fuerzas Armadas Revolucionarias de Colombia (FARC) and (2) corridos from the
"right-wing" paramilitaries Autodefensas Unidas de Colombia (AUC). We train
personalized machine learning models to predict induced emotions for three
users with diverse political views - we aim at identifying the songs that may
induce negative emotions for a particular user, such as anger and fear. To this
extent, a user's emotion judgements could be interpreted as problematizing data
- subjective emotional judgments could in turn be used to influence the user in
a human-centered machine learning environment. In short, highly desired
"emotion regulation" applications could potentially deviate to "emotion
manipulation" - the recent discredit of emotion recognition technologies might
transcend ethical issues of diversity and inclusion.
