The BESTBOT has two direct predecessor projects, the GOODBOT and the LIEBOT. In 2013, the GOODBOT was created as a closed moral chatbot system which uses textual analysis to determine the user’s emotions and is able to react appropriately. Around three years later, the LIEBOT project was initiated; an open chatbot system which systematically generates untruths using several lie strategies. Each project, including the BESTBOT project, focuses specifically on the morality of machines and belongs to the research field of machine ethics. Both predecessor projects have room for improvement and advancement; thus, the BESTBOT project uses their findings as a basis for its development and realization.

The goal of the BESTBOT project is to critically analyze and use the findings of the mentioned predecessor projects, to conduct further research in the field of machine ethics and to use the developed research findings to implement a chatbot system able to behave morally correct and in accordance with the user’s sentiments. The focus of the research lays on the integration and utilization of facial recognition, especially in combination with textual recognition. Therefore, specific research questions have been worked out and answered within the scope of the project. The theoretical research part has been conducted parallel to the practical programming part. Therefore, a flexible and iterative project method has been chosen with the Rational Unified Process (RUP). This method is specifically constructed for software projects and seamlessly fits this project. For the literature research during the theoretical part, an iterative literature review process has been applied.

The paper shows the importance for a moral machine to have a predefined code of ethics in form of written rules. It is indispensable that the rules do not conflict each other and do not leave room for interpretations or abstract conclusions. The project has adopted seven meta rules from the GOODBOT project with small adjustments as well as five additional moral rules specifically developed for the BESTBOT system. This base of rules helped to answer several behavioral questions in the course of the project. Another emphasis has been laid on facial recognition and the answering of the respective research questions. The integration and utilization of facial recognition has been beneficial to the overall system’s capabilities. The detection of human’s problems has been a crucial part of this project. Facial recognition is able to even better detect human emotions than textual recognition, due to the fact that people are more honest with their facial emotions and have less reservations to express themselves. Moreover, the addition of a second emotion recognition technology leads to further opportunities. The dealing with discrepancies between the two technologies has been another emphasis of this project. Different circumstances leading to such discrepancies have been identified which led to a common behavior strategy. People are sensitive when it comes to their feelings or visual appearance. Thus, a strategy has to be carefully chosen to cope with certain situations. The paper points out how important it is to show transparency to the user and to let him or her have the control over the process by confirming the system’s determinations. On the other hand, the integration of facial recognition also bears some risks. The problem of physiognomy as a pseudoscience and its questionable practices and results is discussed in great detail. Furthermore, security concerns in regard to informational autonomy have to be addressed and handled accordingly. By and large, it can be said that the integration of facial recognition provides opportunities and benefits that outweigh the additional risks and challenges.