A previous ChE in Context column (CEP, July 2024, pp. 24鈥�25) and 颁贰笔鈥檚 August 2024 special section on AI and Digitalization described how the field of artificial intelligence (AI) and machine learning (ML) is quickly evolving. The speed of development and AI/ML鈥檚 wide range of applications lead to many practical issues, and this column briefly considers some of them. Data is key to successful AI/ML modeling, so it is no wonder that data is central to many of these issues.
Regulatory and safety compliance. 鈥淏lack-box鈥� ML models that are employed in applications where regulations and safety are key can be difficult to trust because they do not provide the requisite level of transparency required for compliance. 鈥淕rey鈥� or 鈥渨hite box鈥� ML models such as physics-informed and explainable-AI models are potential aids to building trustworthiness.
Ethical concerns. Model bias can lead to potentially significant ethical issues. AI/ML models learn from the data they are being 鈥渇ed.鈥� As such, if the dataset is outdated or incomplete, the models will likely predict biased results. If the ML model developer is not aware of the potentially dangerous bias problem, that is an issue; if they are aware but do not account for it, that is an ethical issue. For example, in...
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