About: Machine Learning – The Results Are Not the only Thing that Matters! What About Security, Explainability and Fairness?

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An Entity of Type : schema:ScholarlyArticle, within Data Space : wasabi.inria.fr associated with source document(s)

Attributes	Values
type	Academic Article research paper schema:ScholarlyArticle
isDefinedBy	Covid-on-the-Web dataset
has title	Machine Learning – The Results Are Not the only Thing that Matters! What About Security, Explainability and Fairness?
Creator	Pawlicki, Marek Puchalski, Damian Choraś, Micha Kozik, Rafa
Source	PMC
abstract	Recent advances in machine learning (ML) and the surge in computational power have opened the way to the proliferation of ML and Artificial Intelligence (AI) in many domains and applications. Still, apart from achieving good accuracy and results, there are many challenges that need to be discussed in order to effectively apply ML algorithms in critical applications for the good of societies. The aspects that can hinder practical and trustful ML and AI are: lack of security of ML algorithms as well as lack of fairness and explainability. In this paper we discuss those aspects and provide current state of the art analysis of the relevant works in the mentioned domains.
has issue date	2020-05-23(xsd:dateTime)
bibo:doi	10.1007/978-3-030-50423-6_46
has license	no-cc
sha1sum (hex)	792b41a56d79f1f76762f48d4e6e714e5c04aff3
schema:url	https://doi.org/10.1007/978-3-030-50423-6_46
resource representing a document's title	Machine Learning – The Results Are Not the only Thing that Matters! What About Security, Explainability and Fairness?
has PubMed Central identifier	PMC7303684
schema:publication	Computational Science - ICCS 2020
resource representing a document's body	covid:792b41a56d79f1f76762f48d4e6e714e5c04aff3#body_text
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