@article{MAKHILLIBM202014727516,
    title = {Artificial Intelligence: Risks and Opportunities},
    journal = {International Business Management},
    volume = {14},
    number = {7},
    pages = {236-243},
    year = {2020},
    issn = {1993-5250},
    doi = {ibm.2020.236.243},
    url = {https://makhillpublications.co/view-article.php?issn=1993-5250&doi=ibm.2020.236.243},
    author = {Alisson Paulo and},
    keywords = {Artificial neural nets,hot-rolled structural sections,prediction,autonomous direction,theory},
    abstract = {This study aims to discuss the risks and
opportunities involved in building predictive models
based on artificial intelligence. Countermeasures are also
proposed to minimize the risks involved in their adoption
where reliability is a critical factor for user safety such as
autonomous driving. For this, it is explored a real
development of a predictive mathematical model, using
industrial data in the steel industry. This development
aimed to construct an empirical mathematical model to
predict the mechanical properties (Yield Strength, YS) of
hot rolled steel structural beams. Such model was based
on rolling process variables and the chemical composition
of steel. As a result of this research it was observed that
the obtained data agreed with the expected metallurgical
theory. The errors obtained between the estimated and the
real values were greater for process conditions with lack
of enough data. These results are associated with the risk
of using artificial intelligence technology in critical
applications and actions aiming at its improvement are
proposed.}
    }