How Data is used in Smart Cities

The Case / Scenario

By 2030, the population living in cities will increase by an additional 1.5 billion people, straining resources, infrastructure, jobs and healthcare. The use of Smart Information Systems (SIS, AI techniques based on big data analytics) may help to tackle this by ensuring the sustainable growth of cities. This use case unpacks the ethical challenges of AI by looking at four European cities and some of their SIS applications: citizens’ complaints AI (Amsterdam), parking permit chat-bot (Helsinki), platform for data exchange (Copenhagen), and a project with an open-source algorithm (Hamburg). Smart cities are in their infancy, which means that availability and accuracy of data, and as a result the accuracy of recommendations, is an issue. Consent, transparency and data ownership are also prominent ethical considerations, with a focus on citizens exercising control over data which refer to them. Collaboration needs to be at the heart of a smart city. A public-private model facilitates both the business development- and the citizen-engagement sides of the Smart City. A bottom-up approach is the most effective and ethical way to ensure that smart cities work and are used by citizens.

Ethical Issues

Ethical issues identified in the case study can be summarised in the following categories:

  • Availability and Accuracy of Data. Some private companies can be difficult when it comes to sharing data if there is no benefit to the company. However, availability of data is fundamental for the success of smart cities. Inaccurate data would not be detrimental in all cases (e.g., Helsinki and Amsterdam), but where accuracy is important, gathering good data may lead to privacy concerns.
  • Economics and Inequalities. Private investors and municipalities may differ in objectives, with investors focused more on economic benefits, and municipalities on sustainability and providing value to citizens, even when this is a hindrance in the economic development of projects.
  • Privacy and Data Ownership. Smart city projects aim to allow citizens greater control over their personal data and ensure their privacy. Citizens have to provide informed consent on the collection, storage and use of the data, and the data are anonymized
  • Transparency and Trust. There has to be a mutually beneficial relationship of trust between corporations, citizens and municipalities working on smart cities. Transparency, accessibility and availability of data, albeit not at the expense of privacy, is necessary to gain trust from citizens.

Mitigation Strategies

  • Availability and Accuracy of Data: Municipalities retrieve and use their own algorithmic training data instead of relying on third parties to avoid private companies becoming locked-in. Customer feedback enables the mitigation of inaccurate data and potential biases.
  • Economics and Inequalities: A bottom-up approach may provide more value to citizens even if this is less economically beneficial to companies.
  • Privacy and Data Ownership: Informed consent and anonymization of data allow citizens greater control of personal data. Transparency between partners and citizens is key. In cases where personal information is collected, datasets containing a minimum number of people hinders individual tracing.
  • Transparency and Trust: Certain laws request that city data are openly accessible and available, increasing transparency and trust amongst people involved.

Lessons Learned

This case study shows valuable insights related to smart cities. Since little research was done on specific SIS projects, the case study was especially relevant since it provides a detailed analysis of potential ethical implications that arise with using SIS within smart city projects. Specific insights arising from this case study include:

  • Elements mentioned in the literature (e.g., digital divide) are typically too general and broad to be of obvious direct relevance to individual projects.
  • The literature often concentrates on future-focused issues that may not even materialise, whereas smart city projects are more concerned with pragmatic tangible issues.
  • Municipality SIS projects are primarily concerned with the impact of SIS on (the majority of) their citizens, rather than other, more marginalised groups within society.
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