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Beyond ‘smart-only’ cities: redefining the ‘smart-everything’ paradigm

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Abstract

This paper presents different manifestations and problems of the ‘smart-everything’ paradigm, provides a critical reflection of its implications and proposes a human-centered design approach resulting in the provision of ‘people-oriented, empowering smartness’. The approach is characterized by design goals like “keeping the human in the loop and in control” and the proposal that “smart spaces make people smarter”. The critical reflection implies to ‘redefine’ the ‘smart-everything’ paradigm. One could also say this is a proposal in the spirit of humanized computing. While the approach has general applicability, the examples are mainly taken from the domain of employing information technology in current and future urban environments, where one can observe an increasing hype indicated by the label ‘smart cities’. The paper argues that a citizen-centered design approach for future cities is needed for going beyond technology-driven ubiquitous instrumentations and installations of cities. To illustrate the situation, the paper addresses several general problem sets concerning artificial intelligence and algorithmic automation as well as privacy issues. There are two trade-offs to be considered: (a) between human control and automation, and (b) between privacy and smartness. People are not asked anymore beforehand for their permission to collect and process their personal data. People do not have the choice to decide and make the trade-off decision between smartness and privacy themselves but are confronted with serious privacy infringements. To remedy the situation, a ‘privacy by design’, respectively ‘privacy by default’ approach is proposed. The combination of redefining the ‘smart-everything’ paradigm in terms of empowering people, employing privacy by design and enforcing an overall citizen-centered design approach is guided by the goal of reconciling people and technology, creating and maintaining a balance of decision-making and control entities. It should convince and incite all stakeholders “to move beyond ‘smart-only’ cities” and transform them into Humane, Sociable and Cooperative Hybrid Cities.

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Notes

  1. Progress towards autonomous driving is categorized by levels from 0 to 5, where “0” is fully manual with no automation and “5” full automation (no human driver needed for supervision) (SAE 2014).

  2. For a complete picture of AI, it must be noted that there are also rule-based systems and approaches that are deterministic in nature. Not all AI methods involve ‘black boxes’ also called ‘opaque AI’. Nevertheless, increasing complexity can result in situations, where even AI experts encounter problems with explaining why and how decisions were reached. People must be able to trust the outcome and the decisions, which is a lot easier with what is called ‘transparent AI’.

  3. For a description of the history of AmI see Aarts and Encarnacao (2006).

  4. This is not to be confused with the notion of a ‘digital footprint’ or ‘digital traces’ which refer to a set of traceable digital activities, actions, contributions (e.g., in social media networks).

  5. Here, we do not address activities of secret services or criminal investigations.

  6. Velo is the Swiss-German word for bicycle.

  7. See the developments for so called ‘product memories’, were tagging allows to monitor the complete production and retail process of a product from the producer (fair trade yes or no) to the consumer, who might also check, e.g., if the cooling chain of the fish was maintained throughout the process until delivery in the shop. These are positive aspects of tagging.

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Acknowledgements

The author gratefully acknowledges feedback and advice from Shin’ichi Konomi, Irene Mavrommati, Christine Riedmann-Streitz, Ben Shneiderman, Constantine Stephanidis on earlier versions of this paper. Nevertheless, the author has the full responsibility for the content of this paper.

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Streitz, N. Beyond ‘smart-only’ cities: redefining the ‘smart-everything’ paradigm. J Ambient Intell Human Comput 10, 791–812 (2019). https://doi.org/10.1007/s12652-018-0824-1

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