The Validation of AI Techniques

Rossella Locatelli (), Giovanni Pepe () and Fabio Salis ()
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Rossella Locatelli: University of Insubria
Giovanni Pepe: KPMG Advisory
Fabio Salis: Credito Valtellinese

Chapter Chapter 4 in Artificial Intelligence and Credit Risk, 2022, pp 65-79 from Springer

Abstract: Abstract This chapter describes the implementation of validation techniques aimed at monitoring and mitigate risks related to the development of AI models. The key trustworthy indicators are identified and detailed in coherence with the main trustworthy principles, namely accuracy, robustness, fairness, efficiency and explainability. Also, a focus on the interpretability of the AI models’ outcomes, summarising the main regulatory requirements, and describing the methodological approaches aimed at assessing the stability of the models is detailed. In order to evaluate and interpret the results of the AI models, the contribution of each risk divers is assessed by means of specific methodologies.

Keywords: Traditional models; Traditional techniques; Machine Learning—ML; Artificial Intelligence—AI; Validation; Trustworthy indicators; Trustworthy principles; Accuracy; Robustness; Fairness; Efficiency; Explainability; Credit risk; Credit risk assessment; Regulatory requirements; Interpretability; Stability; Model outcome; Results (search for similar items in EconPapers)
Date: 2022
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Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-3-031-10236-3_4

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DOI: 10.1007/978-3-031-10236-3_4

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