airegulation3: airegulation Bayesian Networks

In bnRep: A Repository of Bayesian Networks from the Academic Literature

airegulation Bayesian Networks

Description

Understanding support for AI regulation: A Bayesian network perspective.

Format

A discrete Bayesian network to understand public perceptions towards AI (opportunity BN of the paper). The BN was learned from data. The vertices are:

Age

(14-29, 30-44, 45-59, 60+);

AI_Risk_data

(Not_mentioned, Mentioned);

AI_Risk_Jobs

(Not_mentioned, Mentioned);

AI_Risk_LossOfControl

(Not_mentioned, Mentioned);

AI_Risk_Misuse_Regulation

(Not_mentioned, Mentioned);

AI_Risk_Society

(Strongly_disagree, Somewhat_disagree, Somewhat_agree, Strongly_agree);

AIRegulations

(No, Yes);

Education

(Low, Medium, High);

EUAppropriateRegulation

(Appropriate, Too_strict, Not_strict_enough, Don_t_know);

FriendsAI

(No, Yes);

HeardEURegulation

(No, Yes);

Income

(Low, Medium, High, Not_reported);

InformedAI

(Very_poor, Rather_poor, Rather_good, Very_good);

InterestAI

(Not_at_all, Less_strongly, Strongly, Very_strongly);

MediaAI

(No, Yes);

Municipality

(<5k, 5k-19k, 20k-99k, 100k-499k, 500k+);

SearchAI

(No, Yes);

Sex

(Female, Male);

VoteIntent

(Left, Right, Other);

Value

An object of class bn.fit. Refer to the documentation of bnlearn for details.

References

Cremaschi, A., Lee, D. J., & Leonelli, M. (2025). Understanding support for AI regulation: A Bayesian network perspective. International Journal of Engineering Business Management, 17, 18479790251383310.

bnRep documentation built on Nov. 5, 2025, 6:18 p.m.