Metacognition of trust: artificial agents, science, and Bayes

Metacognition of trust: artificial agents, science, and Bayes. June 2021. Full paper, about 3000 words long. Available on demand. Presented at CEPE in May 2019.
abstract bibtex

What do we need to trust non-humans? Is our trust, in this case, rational? This paper analyses different dimensions of trust in artificial agents (especially AI algorithms based on Machine Learning). We focus on some metacognitive requirements that a human trustor H can impose on an artificial agent (AA) as a trustee. The analysis is inspired by an analogy with the acceptance of scientific theories in the absence of direct evidence. We show that similar to the case in which a human H trusts a scientific field (collections of theories, models, disciplines, etc.), we can demand from AA several metacognitive capacities that will make this relationship of trust rational for H. We apply a version of the Bayesian cognitive science to some Machine Learning algorithms and infer that a series of intrinsic features can grant rationality and trust to these special AA agents

@unpublished{MetacognitionTrustArtificial2021,
	title = {Metacognition of trust: artificial agents, science, and {Bayes}},
	copyright = {All rights reserved},
	abstract = {What do we need to trust non-humans? Is our trust, in this case, rational? This paper analyses different dimensions of trust in artificial agents (especially AI algorithms based on Machine Learning). We focus on some metacognitive requirements that a human trustor H can impose on an artificial agent (AA) as a trustee. The analysis is inspired by an analogy with the acceptance of scientific theories in the absence of direct evidence. We show that similar to the case in which a human H trusts a scientific field (collections of theories, models, disciplines, etc.), we can demand from AA several metacognitive capacities that will make this relationship of trust rational for H. We apply a version of the Bayesian cognitive science to some Machine Learning algorithms and infer that a series of intrinsic features can grant rationality and trust to these special AA agents},
	language = {3. Philosophy of cognitive science},
	month = jun,
	year = {2021},
	note = {Full paper, about 3000 words long. Available on demand. Presented at CEPE in May 2019.},
	keywords = {Artificial Intelligence, Cognitive Science, Statistical learning theory},
}

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