Why we need biased AI -- How including cognitive and ethical machine biases can enhance AI systems
It proposes a re-evaluation of biases in AI for enhancing efficiency and ethics, addressing a foundational issue in machine learning and ethics.
This paper argues for incorporating human cognitive biases into AI algorithms to improve decision-making in complex environments and using biased training stimuli to achieve ethical machine behavior, supported by theoretical considerations and seven case studies.
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.