The development of computer science has raised ethical concerns regarding the potential negative impacts of machine learning tools on people and society. Some examples are pornographic deepfakes used as weapons of war against women; pattern recognition designed to uncover sexual orientation; and misuse of data and deep learning by private companies to influence democratic elections. We contend that these three examples are cases of automated evil. In this article, we defend that the concept of forbidden knowledge can help to inform (...) a coherent ethical framework in the context of machine learning research. We conclude that restricting generalised access to extensive data and limiting access to ready-to-use codes would mitigate potential harms caused by machine learning tools. In addition, the notions of intersectionality and interdisciplinarity should be systematically introduced in data and computer science research. (shrink)

Against the ideal of value-free science I argue that science is not––and cannot be––value-free and that relevant values are both cognitive and moral. I develop an argument by indicating various aspects of the value-ladenness of science. The recognition of the value-ladenness of science requires rethinking our understanding of the rationality and responsibility of science. Its rationality cannot be seen as merely instrumental––as it was seen by the ideal of value-free science––for this would result in limiting the autonomy of science and (...) reducing scientists to “minds to hire”. The scientific rationality must be seen as practical rationality which takes into account the full horizon of values. The scientific responsibility must also be broaden in scope and type. On this basis I draw three practical conclusions concerning the organization of research and training of young scientists, appealing to Plato’s claim that those most capable of healing are also those most capable of harming. (shrink)

Certain research strands can yield “forbidden knowledge”. This term refers to knowledge that is considered too sensitive, dangerous or taboo to be produced or shared. Discourses about such publication restrictions are already entrenched in scientific fields like IT security, synthetic biology or nuclear physics research. This paper makes the case for transferring this discourse to machine learning research. Some machine learning applications can very easily be misused and unfold harmful consequences, for instance, with regard to generative video or text synthesis, (...) personality analysis, behavior manipulation, software vulnerability detection and the like. Up till now, the machine learning research community embraces the idea of open access. However, this is opposed to precautionary efforts to prevent the malicious use of machine learning applications. Information about or from such applications may, if improperly disclosed, cause harm to people, organizations or whole societies. Hence, the goal of this work is to outline deliberations on how to deal with questions concerning the dissemination of such information. It proposes a tentative ethical framework for the machine learning community on how to deal with forbidden knowledge and dual-use applications. (shrink)

Advances in the life sciences are occurring with extreme rapidity and accumulating a great deal of knowledge about life’s vital processes. While this knowledge is essential for fighting disease in a more effective way, it can also be misused either intentionally or inadvertently to develop novel and more effective biological weapons. For nearly a decade civil-academic society as well as States Parties to the Biological and Toxin Weapons Convention have recognised the importance of dual-use biosecurity education for life scientists as (...) a means to foster a culture of responsibility and prevent the potential misuse of advances in the life sciences for non-peaceful purposes. Nevertheless, the implementation of dual-use biosecurity education for life scientists has made little progress in institutions of higher learning. Professional societies and academic organizations have worked from the bottom-up in developing online dual-use biosecurity education modules that can be used for instruction. However, top-down help is needed from goverments if further progress is to be made in implementing biosecurity education for life scientists. (shrink)