Application of human cognitive mechanisms to Naïve Bayes text classifier

Taniguchi, Hidetaka; Satō, Hiroshi; Shirakawa, Tomohiro

doi:10.1063/1.4992545

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Area II Cognitive Biases In Ai Algorithms1

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2021

2022

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“…Taniguchi, Sato, and Shirakaw (102) work in this direction by building a modified Naive Bayes classifier that leverages the symmetry (103) and the mutual exclusion (104) biases. The proposed model is able to perform better than alternative, state-of-the-art methods on a spam classification task when the dataset is small and…”

Section: Area II Cognitive Biases In Ai Algorithmsmentioning

confidence: 99%

BIASeD: Bringing Irrationality into Automated System Design

Gulati¹,

Lozano²,

Lepri³

et al. 2022

Preprint

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Human perception, memory and decision-making are impacted by tens of cognitive biases and heuristics that influence our actions and decisions. Despite the pervasiveness of such biases, they are generally not leveraged by today's Artificial Intelligence (AI) systems that model human behavior and interact with humans. In this theoretical paper, we claim that the future of human-machine collaboration will entail the development of AI systems that model, understand and possibly replicate human cognitive biases. We propose the need for a research agenda on the interplay between human cognitive biases and Artificial Intelligence. We categorize existing cognitive biases from the perspective of AI systems, identify three broad areas of interest and outline research directions for the design of AI systems that have a better understanding of our own biases.

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Section: Area II Cognitive Biases In Ai Algorithmsmentioning

confidence: 99%