Within the most recent two decades, machine translation (MT) has been advanced from a research area to a mature, deployed technology for prose text and most language pairs. However, language has different dimensions of meaning, and the alluded progress has primarily been demonstrated for propositional meaning.

In this small exploratory study, we investigate whether one type of affective meaning, namely different types of linguistically expressed media bias, are retained under automatic translation for two language pairs, English and German as the source languages, and Bulgarian as the target language. We evaluate four state-of-the-art translation methods to answer the question whether and to what degree a loss of non-neutral language (specifically, 27 types of media bias) can occur as a result of applying MT.

Our empirical findings are consistent with the claim that MT does not fully retain existing news bias of its input source text, but also may add extra bias of its own. To the best of our knowledge, this is the first experimental study of MT media bias retention.

In this short experience report, we present our attempt to integrate output from an ongoing research project with a traditional, mostly literature-based, course on media manipulation, bias and fake news, to turn an existing undergraduate course into a more engaging experience for attending students. To this end, we successfully utilized our system BiasScanner for news bias detection and classification (BiasScanner.org).

Die Rolle des Denkens im KI-Zeitalter