The ethics of artificial intelligence (AI) has become an important topic in the application of AI and machine learning in the past several years. This first part of a two-part series explains the evolution and importance of the ethics of AI. The second part will present its relevance and use in engineering applications.
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 first part of this two-part series discusses 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. Because engineering applications of AI and ML are used to model physical phenomena, Part 2 of the series will discuss 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.
Continue reading: https://jpt.spe.org/the-ethics-of-ai-evolves-with-the-technology
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 first part of this two-part series discusses 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. Because engineering applications of AI and ML are used to model physical phenomena, Part 2 of the series will discuss 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.
Continue reading: https://jpt.spe.org/the-ethics-of-ai-evolves-with-the-technology
