Abstract
Nowadays, intelligent systems play an important role in a wide range of applications, including financial ones, smart cities, healthcare, and transportation. Most of the intelligent systems are composed of prefabricated components. Inappropriate composition of components may lead to unsafe, power-consuming, and vulnerable intelligent systems. Although artificial intelligence-based systems can provide various advantages for humanity, they have several dark sides that can affect our lives. Some terms, such as security, trust, privacy, safety, and fairness, relate to the dark sides of artificial intelligence, which may be inherent to the intelligent systems. Existing solutions either focus on solving a specific problem or consider the some other challenge without addressing the fundamental issues of artificial intelligence. In other words, there is no general framework to conduct a component selection process while considering the dark sides in the literature. Hence, in this paper, we proposed a new framework for the component selection of intelligent systems while considering the dark sides of artificial intelligence. This framework consists of four phases, namely, component analyzing, extracting criteria and weighting, formulating the problem as multiple knapsacks, and finding components. To the best of our knowledge, this is the first component selection framework to deal with the dark sides of artificial intelligence. We also developed a case study for the component selection issue in autonomous vehicles to demonstrate the application of the proposed framework. Six components along with four criteria (i.e., energy consumption, security, privacy, and complexity) were analyzed and weighted by experts via analytic hierarchy process (AHP) method. The results clearly show that the appropriate composition of components was selected through the proposed framework for the desired functions.
Highlights
At present, there are many studies on component-based software architecture, integration and selection; architecture mismatch analysis; and off-the-shell (OTS) based development in the literature
intelligent systems (ISs) design is represented as a component selection (CS) problem
Since there is a correlation between the criteria, the analytic hierarchy process (AHP) method is utilized as a weighting mechanism
Summary
There are many studies on component-based software architecture, integration and selection; architecture mismatch analysis; and off-the-shell (OTS) based development in the literature. Despite the existence of a few studies, such as [16], on the dark sides of AI in designing ISs, this problem has not been considered in designing component-based software. Some of the above challenges were used in the literature to define new versions of CS in different contexts, such as electric vehicles (EVs) [32], smart buildings [33], and renewable energy systems [34] These studies only focused on challenges without considering information about AI-based components. All of the available algorithms suffer from a critical problem, which is the lack of a general framework to develop AI-based systems considering dark sides of AI. We developed a new framework to overcome the component selection problem of ISs considering the dark sides of AI.
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