Introduction
The advent of systems that read, interpret, and respond to emotional signals raises specific questions about emotions , artificial empathy , and accountability. This text describes definitions, technologies, applications, and limitations, and presents open questions without making value judgments.
What is emotional artificial intelligence?
Emotional artificial intelligence seeks to identify and process emotional states from data such as voice, facial expressions, text, or biometrics. The technology combines sensors, machine learning models, and rules to recognize patterns associated with states such as sadness, anger, or happiness.
How it works in practical terms
The systems use multimodal signals: audio for intonation, video for microexpressions, and text for semantic analysis. Supervised learning models correlate these signals with emotional labels in databases. Additionally, personalization algorithms adjust responses based on history and context.
Current applications
- Customer Service: Detecting frustration in calls to prioritize responses.
- Mental health: tools that monitor emotional changes to alert professionals.
- Education: Virtual tutors who adapt the pace to the student's motivation.
In daily work, these applications seek to improve interaction, without replacing professionals.
Technical and philosophical limits
There is a key distinction between simulation and subjective experience . Models can reproduce affective signals and adapt responses, but there is no objective evidence that they experience consciousness or internal experiences. Furthermore, accuracy depends on data quality, sample diversity, and cultural context.
“Machines don’t feel like humans do,” say those who emphasize the difference between observable behavior and subjective experience.
Risks, biases and privacy
Models can reflect biases present in training data, impacting automated decisions. Furthermore, the collection of affective information poses privacy and informed consent challenges, especially when its use is persistent or opaque.
Legal and ethical framework
The need for regulations governing the collection of emotional data, algorithmic transparency, and accountability is being discussed in several countries. Proposals include audit obligations, limits on use in surveillance, and user rights over their emotional data.
Evaluation and metrics
Performance measurement uses classic indicators such as precision, recall, and specific measures for affective detection. However, external validity depends on the representativeness of the samples and the context of use. In comparative studies, it is recommended to report biases based on gender, age, and origin.
Open questions
- To what extent is simulating emotion equivalent to having an affective experience?
- What ethical limits should be imposed on the use of emotional data in public and private services?
- How to ensure transparency and fairness in models trained with diverse cultural data?
