@article{(Open Science Index):https://publications.waset.org/pdf/10013629,
	  title     = {Metrology-Inspired Methods to Assess the Biases of Artificial Intelligence Systems},
	  author    = {Belkacem Laimouche},
	  country	= {},
	  institution	= {},
	  abstract     = {With the field of Artificial Intelligence (AI) experiencing exponential growth, fueled by technological advancements that pave the way for increasingly innovative and promising applications, there is an escalating need to develop rigorous methods for assessing their performance in pursuit of transparency and equity. This article proposes a metrology-inspired statistical framework for evaluating bias and explainability in AI systems. Drawing from the principles of metrology, we propose a pioneering approach, using a concrete example, to evaluate the accuracy and precision of AI models, as well as to quantify the sources of measurement uncertainty that can lead to bias in their predictions. Furthermore, we explore a statistical approach for evaluating the explainability of AI systems based on their ability to provide interpretable and transparent explanations of their predictions.},
	    journal   = {International Journal of Mechanical and Industrial Engineering},
	  volume    = {18},
	  number    = {5},
	  year      = {2024},
	  pages     = {170 - 178},
	  ee        = {https://publications.waset.org/pdf/10013629},
	  url   	= {https://publications.waset.org/vol/209},
	  bibsource = {https://publications.waset.org/},
	  issn  	= {eISSN: 1307-6892},
	  publisher = {World Academy of Science, Engineering and Technology},
	  index 	= {Open Science Index 209, 2024},
	}