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Acceleration of urbanisation is posing great challenges to sustainable development. Growing accessibility to big data and artificial intelligence (AI) technologies have revolutionised many fields and offered great potential for addressing pressing urban problems. However, using these technologies without explicitly considering responsibilities would bring new societal and environmental issues. To fully harness the potential of big data and AI without creating new problems, we envisage a conceptual framework of Responsible Urban Intelligence (RUI) and advocate an agenda for action. We first define RUI as consisting of three major components including urban problems, enabling technologies, and responsibilities; then introduce transparency, fairness, and eco-friendliness as the three dimensions of responsibilities which naturally link with the human, space, and time dimensions of cities; and further develop a four-stage implementation framework for responsibilities as consisting of solution design, data preparation, model building, and practical application; and finally present a research agenda for RUI addressing challenging issues including data and model transparency, tension between performance and fairness, and solving urban problems in an eco-friendly manner.

The selectivities to pathogenic Klebsiella strains of different isolation media were compared by known standard strains. The modified MacConkey-inositol-carbenicillin (MCIC) medium (Named MCIAC, MacConkey-inositol-adonitol-carbenicillin) supplemented with adonitol gave no false-negative colonies, and exhibited higher selectivity. MCIC and Simmons citrate agar with inositol (SCAI) media gave two false-negative colonies, respectively. These three media all gave two false-positive colonies, respectively. Salmonella Shigella medium gave four false-negative colonies and five false-positive colonies. Violet red bile glucose agar medium gave the most false-positive colonies, although it gave no false-negative colonies. One hundred samples of powdered milk were examined by MCIAC, MCIC and SCAI plates. The typical positive colonies were further identified using Vitek GNI Auto Microbic system and API 20E system. The results showed that the specificity of the MCIAC medium was higher than MCIC and SCAI media. PRACTICAL APPLICATIONS MacConkey-inositol-carbenicillin (MCIC) is the most commonly used selective medium for the detection of Klebsiella. But some inositol-nonfermenting Klebsiella strains would be missed when selected by this medium. We improved the MCIC medium by supplementing with 1% adonitol. The new modified medium (MacConkey-inositol-adonitol-carbenicillin, MCIAC) had advantages over other selective Klebsiella media in having a higher selectivity and an incubation time of only 16–24 h. MCIAC could be routinely used for pathogenic Klebsiella selection of powdered milk and other food samples.