Ethical Artificial Intelligence in Smart Mobility Technologies: Autonomous Driving Algorithms, Geospatial Data Mining Tools, and Ambient Sound Recognition Software
Ethical Artificial Intelligence in Smart Mobility Technologies: Autonomous Driving Algorithms, Geospatial Data Mining Tools, and Ambient Sound Recognition Software

Summary/Abstract: In this article, we cumulate previous research findings indicating that smart transportation networks deploy route planning and visual perception algorithms, predictive urban analytics, and cognitive data fusion techniques. We contribute to the literature on data modeling and mobility simulation tools, machine learning and spatial cognition algorithms, and vehicular communication technologies by showing that cognitive wireless sensor networks, computer vision and predictive modeling algorithms, and data mining and behavior tracking tools shape data-driven smart sustainable urbanism. Throughout June 2022, we performed a quantitative literature review of the Web of Science, Scopus, and ProQuest databases, with search terms including “ethical artificial intelligence” + “smart mobility technologies” + “autonomous driving algorithms,” “geospatial data mining tools,” and “ambient sound recognition software.” As we inspected research published in 2022, only 179 articles satisfied the eligibility criteria. By eliminating controversial findings, outcomes unsubstantiated by replication, too imprecise material, or having similar titles, we decided upon 36, generally empirical, sources. Data visualization tools: Dimensions (bibliometric mapping) and VOSviewer (layout algorithms). Reporting quality assessment tool: PRISMA. Methodological quality assessment tools include: AXIS, Dedoose, ROBIS, and SRDR.

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