Metacognition of trust: artificial agents, science, and Bayes

Metacognition of trust: artificial agents, science, and Bayes. February 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 are focusing here 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 a number of metacognitive capacities that will make this relation 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@unpublished{noauthor_metacognition_2019,
	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 are focusing here 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 a number of metacognitive capacities that will make this relation 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.},
	month = feb,
	year = {2019},
	keywords = {Artificial Intelligence, Cognitive Science, Statistical learning theory},
}

Downloads: 0

{"_id":"icQp8r4HqzZPXqTYQ","bibbaseid":"anonymous-metacognitionoftrustartificialagentsscienceandbayes-2019","bibdata":{"bibtype":"unpublished","type":"unpublished","title":"Metacognition of trust: artificial agents, science, and Bayes","copyright":"All rights reserved","abstract":"What do we need to trust non-humans?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 are focusing here 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 a number of metacognitive capacities that will make this relation 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.","month":"February","year":"2019","keywords":"Artificial Intelligence, Cognitive Science, Statistical learning theory","bibtex":"@unpublished{noauthor_metacognition_2019,\n\ttitle = {Metacognition of trust: artificial agents, science, and {Bayes}},\n\tcopyright = {All rights reserved},\n\tabstract = {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 are focusing here 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 a number of metacognitive capacities that will make this relation 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.},\n\tmonth = feb,\n\tyear = {2019},\n\tkeywords = {Artificial Intelligence, Cognitive Science, Statistical learning theory},\n}\n\n","key":"noauthor_metacognition_2019","id":"noauthor_metacognition_2019","bibbaseid":"anonymous-metacognitionoftrustartificialagentsscienceandbayes-2019","role":"","urls":{},"keyword":["Artificial Intelligence","Cognitive Science","Statistical learning theory"],"metadata":{"authorlinks":{}}},"bibtype":"unpublished","biburl":"https://api.zotero.org/groups/4717622/items?key=DKfiMuw6WavRAED8KMX86iG5&format=bibtex&limit=100","dataSources":["387WCqHy3dhc6wxAM"],"keywords":["artificial intelligence","cognitive science","statistical learning theory"],"search_terms":["metacognition","trust","artificial","agents","science","bayes"],"title":"Metacognition of trust: artificial agents, science, and Bayes","year":2019}