  This paper stresses the importance of biases in the field of artificial
intelligence (AI) in two regards. First, in order to foster efficient
algorithmic decision-making in complex, unstable, and uncertain real-world
environments, we argue for the structurewise implementation of human cognitive
biases in learning algorithms. Secondly, we argue that in order to achieve
ethical machine behavior, filter mechanisms have to be applied for selecting
biased training stimuli that represent social or behavioral traits that are
ethically desirable. We use insights from cognitive science as well as ethics
and apply them to the AI field, combining theoretical considerations with seven
case studies depicting tangible bias implementation scenarios. Ultimately, this
paper is the first tentative step to explicitly pursue the idea of a
re-evaluation of the ethical significance of machine biases, as well as putting
the idea forth to implement cognitive biases into machines.
