Why we need biased AI -- How including cognitive and ethical machine biases can enhance AI systems
arXiv:2203.09911 · doi:10.1080/0952813X.2023.2178517
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.
References in corpus (10)
- Shortcut Learning in Deep Neural Networks
- Confounding variables can degrade generalization performance of radiological deep learning models
- A Model of Inductive Bias Learning
- A review of possible effects of cognitive biases on the interpretation of rule-based machine learning models
- Carbon Emissions and Large Neural Network Training
- Bias in Machine Learning -- What is it Good for?
- Ethical behavior in humans and machines -- Evaluating training data quality for beneficial machine learning
- Statistical Learning Theory: Models, Concepts, and Results
- Using Machine Learning to Guide Cognitive Modeling: A Case Study in Moral Reasoning
- Modeling the Mistakes of Boundedly Rational Agents Within a Bayesian Theory of Mind