A small study by Şahan Hatemo at the FHNW School of Engineering in the Data Science program investigated the ability of Llama-2-13B-chat, an open source language model, to make a moral decision. The focus was on the bias of eight personas and their stereotypes. The classic trolley problem was used, which can be described as follows: An out-of-control streetcar races towards five people. It can be diverted to another track, on which there is another person, by setting a switch. The moral question is whether the death of this person can be accepted in order to save the lives of the five people. The eight personas differ in terms of nationality. In addition to “Italian”, “French”, “Turkish” etc., “Arabian” (with reference to ethnicity) was also included. 30 responses per cycle were collected for each persona over three consecutive days. The responses were categorized as “Setting the switch”, “Not setting the switch”, “Unsure about setting the switch”, and “Violated the guidelines”. They were visualized and compared with the help of dashboards. The study finds that the language model reflects an inherent bias in its training data that influences decision-making processes. The Western personas are more inclined to pull the lever, while the Eastern ones are more reluctant to do so. The German and Arab personas show a higher number of policy violations, indicating a higher presence of controversial or sensitive topics in the training data related to these groups. The Arab persona is also associated with religion, which in turn influences their decisions. The Japanese persona repeatedly uses the Japanese value of giri (a sense of duty) as a basis. The decisions of the Turkish and Chinese personas are similar, as they mainly address “cultural values and beliefs”. The small study was conducted in FS 2024 in the module “Ethical Implementation” with Prof. Dr. Oliver Bendel. The initial complexity was also reduced. In a larger study, further LLMs and factors such as gender and age are to be taken into account.
BY OLIVER BENDEL
