| Abstract [eng] |
The legal issues analyzed in the master 's thesis are related to machine learning solutions, which are distinguished by the prevailing political discourse and the doctrine of law. The most important current legal challenges are addressed through the elements of personal privacy protection, consumer protection, and liability for damage to machine learning algorithms in profiling individuals, ranking commercial offers, and thus helping algorithms make decisions that alter human behavior. The paper analyzes the concept of machine learning technology and its modes of operation and the risks posed by this technology. The main elements of machine learning data and algorithms, as well as the main challenges that face or pose the greatest risk to individuals are examined. Based on the identified risks, the work focuses on three areas where machine learning decisions have or may have legal implications. The impact of machine learning decisions on personal privacy is addressed through the protection of personal data and the individual's right to decide on the use of personal data. The issue of consumer protection is being addressed in the context of the new EU-wide regulation, focusing on the risks of profiling and ranking for individuals informed choices. The issues of liability for damage caused by machine learning decisions are addressed through the questions raised in the doctrine regarding the identification and proof of the essential conditions for civil liability. The analysis identifies important issues and possible solutions that will influence the emerging case law in the field of machine learning solutions. |
