# Personalisation and profiling using algorithms and not-so-popular Colombian music: goal-directed mechanisms in music emotion recognition

## TL;DR

This study explores how personalized music emotion recognition systems can reveal political biases in emotional responses to Colombian music, raising ethical concerns about AI misuse.

## Contribution

The paper introduces a novel analysis of politically charged music's emotional impact using personalized MER systems in a Latin American context.

## Key findings

- Political identity significantly influences emotional responses to music with political content.
- Emotion recognition models can detect variations in emotional responses linked to political affiliations.
- AI-driven emotion analysis poses ethical risks in politically sensitive environments.

## Abstract

This work investigates how personalised Music Emotion Recognition (MER) systems may lead to sensitive profiling when applied to musically induced emotions in politically charged contexts. We focus on traditional Colombian music with explicit political content, including (1) vallenatos and social songs aligned with the left-wing guerrilla Fuerzas Armadas Revolucionarias de Colombia (FARC), and (2) corridos linked to sympathisers of the right-wing paramilitary group Autodefensas Unidas de Colombia (AUC). Using data from 49 participants with diverse political leanings, we train personalised machine learning models to predict induced emotional responses – particularly negative emotions. Our findings reveal that political identity plays a significant role in shaping emotional experiences of music with explicit political content, and that emotion recognition models can capture this variation to a certain extent. These results raise critical concerns about the potential misuse of emotion recognition technologies. What is often framed as a tool for wellbeing and emotional regulation could, in politically sensitive contexts, be repurposed for user profiling. This work highlights the ethical risks of deploying AI-driven emotion analysis without safeguards, particularly among populations that are politically or socially vulnerable. We argue that subjective emotional responses may constitute sensitive personal data, and that failing to account for their sociopolitical context could amplify harm and exclusion.

The online version contains supplementary material available at 10.1140/epjds/s13688-025-00595-1.