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Why we need biased AI: How including cognitive biases can enhance AI systems

Thilo Hagendorff, Sarah Fabi

2023Journal of Experimental & Theoretical Artificial Intelligence16 citationsDOI

Abstract

This paper stresses the importance of biases in the field of artificial intelligence (AI). To foster efficient algorithmic decision-making in complex, unstable, and uncertain real-world environments, we argue for the implementation of human cognitive biases in learning algorithms. We use insights from cognitive science and apply them to the AI field, combining theoretical considerations with tangible examples depicting promising bias implementation scenarios. Ultimately, this paper is the first tentative step to explicitly putting the idea forth to implement cognitive biases into machines.

Topics & Concepts

Computer scienceField (mathematics)CognitionArtificial intelligenceCognitive biasHuman intelligenceCognitive computingCognitive scienceMachine learningData sciencePsychologyMathematicsPure mathematicsNeuroscienceBayesian Modeling and Causal InferenceForecasting Techniques and ApplicationsDecision-Making and Behavioral Economics
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