Kan AI voorspellen of een persoon homoseksueel is op basis van analyse van geschreven tekst ?

AI’s vermogen om gevoelige persoonlijke eigenschappen af te leiden uit ongestructureerde data heeft aanzienlijke privacyzorgen opgewerkt. Recente modellen beweren de seksuele geaardheid nauwkeurig te kunnen voorspellen op basis van taalkundige patronen, wat op discriminerende wijze kan worden ingezet. Deze mogelijkheid daagt noties van autonomie en toestemming in digitale ruimtes uit. Juridische bescherming tegen dergelijke afleidingen is vrijwel afwezig.

Background

Recent AI systems have attempted to infer sexual orientation from written text using linguistic analysis. Studies leveraging large language models and stylometric features—such as word choice, syntax, and semantic structures—have reported correlations between these linguistic patterns and self-identified sexual orientation, particularly in contexts where personal relationships are discussed. However, the reliability of such predictions is constrained by small sample sizes, cultural and linguistic biases within training datasets, and the risk of reinforcing stereotypes. Ethical debates focus on privacy, consent, and the potential for misuse in discriminatory applications, as legal protections for inferences drawn from unstructured data remain largely undeveloped or untested. Research in this area intersects with prior work examining the detection of sexual orientation from other data modalities, such as facial images, which has also generated significant ethical and methodological scrutiny.