Why we need biased AI -- How including cognitive and ethical machine biases can enhance AI systems

TL;DR

This paper advocates for intentionally incorporating cognitive and ethical biases into AI systems to improve decision-making efficiency and ethical behavior, supported by theoretical insights and case studies.

Contribution

It introduces the novel idea of deliberately embedding cognitive and ethical biases into AI to enhance performance and moral alignment, challenging traditional bias mitigation approaches.

Findings

Bias implementation improves decision-making in complex environments

Ethical bias filtering aligns AI behavior with social values

Case studies demonstrate practical bias integration scenarios

Abstract

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.