Ø Application of AI-Ethics in Engineering
Ø Example of AI-Ethics in Production from Shale Wells
Application of AI-Ethics in Engineering
Bias (including major assumptions, interpretations, and simplifications) from traditional engineers can be included in the engineering application of AI. This is usually done through the generation of data from mathematical equations and combining it with actual field measurements (actual physics-based data) and then using this combined set of data to model the physics using AI and Machine Learning algorithms. In many cases, such an approach is called “Hybrid Models”. In the context of engineering application of Artificial Intelligence and Machine Learning such models is the determination of lack of realistic and scientific understanding of AI and Machine Learning.
The Ethics of Artificial Intelligence is important to engineers and scientists that have become enthusiasts to use this technology for solving engineering-related problems. While in the past several years AI-Ethics has become an important topic in the non-engineering application of Artificial Intelligence and Machine Learning, now it is just as important in the engineering application of this technology. A specific example of AI-Ethics in the engineering application of AI and Machine Learning is presented in the next section showing how guesswork, assumptions, interpretations, and simplifications can help traditional engineers to use AI and Machine Learning algorithms to generate an unrealistic and highly biased predictive model. This usually happens when they have tried but have not been successful to use facts and field measurements.
It seems that the reasons behind the inclusion of such biases in the engineering application of AI have much to do with the lack of scientific understanding of how Artificial Intelligence must be used to model physical phenomena. Currently, some individuals and companies that claim the use engineering application of this technology are including a large amount of human biases so that they can solve problems using AI after they fail to build an AI-based model that does not include human biases. Human biases in engineering have much to do with how mathematical equations are built to solve physics-based problems.
Continue reading: https://towardsdatascience.com/ai-ethics-in-engineering-437ec07046a6?source=rss—-7f60cf5620c9—4