Fairness biases in AI systems are a severe problem (as shown in my penultimate paper on “speciesist bias”). However, biases are not bad in and of itself. In our new paper, Sarah Fabi and I stress the actual importance of biases in the field of 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.