Ethics of Artificial Intelligence Plays a Role in Engineering
We know that predictive models developed by artificial-intelligence (AI) and machine-learning (ML) algorithms are based on data. And, because we know how this data is used to build AI-based models, the main target of AI ethics is addressing how AI models become biased based on the quality and the quantity of the data that is used.
This second part of this two-part series discusses how AI ethics can determine and clarify how human biases of traditional engineers—assumptions, interpretations, simplifications, and preconceived notions—can be revealed in the engineering applications of AI and ML. Part 1 discussed the nonengineering applications of AI and ML and how human biases such as racism and sexism can be included in AI models through the inclusion of biased data during the training of the algorithms.
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, combining it with field measurements (actual physics-based data), and then using this combined set of data to model the physics using AI and ML algorithms. This approach often is called a “hybrid” model.
The ethics of AI is important to engineers and scientists who have adopted this technology to solve engineering-related problems. While AI ethics has become an important topic in the nonengineering application of AI and ML, it is just as important in engineering. The next section will present a specific example of AI ethics in the engineering application of AI and ML, showing how guesswork, assumptions, interpretations, and simplifications can lead traditional engineers using AI and ML algorithms to generate unrealistic and highly biased predictive models. This usually happens when they have tried to use facts and field measurements but were not successful.
The reasons for the inclusion of such biases in the engineering application of AI appear to be related to a lack of scientific understanding of how AI must be used to model physical phenomena. Currently, some individuals and companies who claim to be using an engineering application of this technology are including a large amount of human biases so that they can solve problems using AI after they failed 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.
The major contribution of AI and ML to solving engineering problems is the modeling of the physical phenomena based on actual measured data, which would be the main driver for the avoidance of biases, assumptions, interpretations, and preconceived notions about physics. Because traditional techniques for modeling physical phenomena rely on mathematical equations, these techniques usually include assumptions and sometimes biases. This is especially true when the physical phenomena being modeled cannot be observed directly. Petroleum engineering is a good example of such a situation because the target hydrocarbon is deep underground. The same is true about any other engineering discipline when the mathematical equations that include assumptions, interpretations, and simplification are used to model the physical phenomena.
Reservoir engineering, reservoir modeling, and reservoir management contribute greatly to the income of the operating and service companies in the oil and gas industry. This is why reservoir modeling is so important in the petroleum industry. Modeling fluid flow in hydrocarbon reservoirs includes many assumptions, interpretations, and simplifications because the reservoirs are hundreds or thousands of feet below the surface. This means that actually observing, touching, or realistically testing anything that takes place in a hydrocarbon reservoir is impossible.
