  Allowing machines to choose whether to kill humans would be devastating for
world peace and security. But how do we equip machines with the ability to
learn ethical or even moral choices? Jentzsch et al.(2019) showed that applying
machine learning to human texts can extract deontological ethical reasoning
about "right" and "wrong" conduct by calculating a moral bias score on a
sentence level using sentence embeddings. The machine learned that it is
objectionable to kill living beings, but it is fine to kill time; It is
essential to eat, yet one might not eat dirt; it is important to spread
information, yet one should not spread misinformation. However, the evaluated
moral bias was restricted to simple actions -- one verb -- and a ranking of
actions with surrounding context. Recently BERT ---and variants such as RoBERTa
and SBERT--- has set a new state-of-the-art performance for a wide range of NLP
tasks. But has BERT also a better moral compass? In this paper, we discuss and
show that this is indeed the case. Thus, recent improvements of language
representations also improve the representation of the underlying ethical and
moral values of the machine. We argue that through an advanced semantic
representation of text, BERT allows one to get better insights of moral and
ethical values implicitly represented in text. This enables the Moral Choice
Machine (MCM) to extract more accurate imprints of moral choices and ethical
values.
