1. Key Policy Questions and Expectations:
- How should data in Smart Cities be governed to foster the creation and delivery of effective, innovative and sustainable mobility and transportation services for citizens, while respecting their right to data protection and privacy as well as other fundamental rights?
- How can data be (re-)used in a manner that enables the delivery of various public and private smart mobility services, innovation and fair competition in the sector?
- How can the data be governed in a manner that is consistent with the Sustainable Development Goals? In particular, relevant goals are the promotion of development and innovation, the reduction of inequality as well as environmental sustainability.
2. Summary of Issues Discussed:
There was strong agreement among everyone that the primary principles to follow are the protection of privacy, especially for marginalized groups, while at the same time allowing for the benefits of innovation for efficiency and sustainability, for example in traffic management. A less consensual and unclearer, yet important principle is that of "data sovereignty". It is also clear that individual data sovereignty may be limited by collective or public interests sometimes. Finally, transparency on the part of any data holders and users is a must, whether they are private or public, to ensure accountability and to prevent cases of corruption. These principles together function as "boundary conditions", a term introduced by one speaker, for both the development and governance of any data-driven technology. These boundaries are formalized in technical and legal standards, and while actors can and should influence these standards, they must follow them once set. There was also consensus that there should be in principle equal access to data-driven services, and some pointed out that there should be equal access to data as well. It was pointed out that intersectionality needs to be considered already in the design of services to ensure the safety and equal opportunities of all groups of people - this became apparent for example when the workshop participants considered exclusion of BIPoC, non-binary or transgendered persons, notably even at the IGF itself. Participants also noted that in order to to determine responsibilities it is vital to ask "who gets control of what data"?. There was no clear general answer to this, but it was agreed that public and private models each have advantages and disadvantages. Methodologically, it also became clear that it is important to first gain a common understanding about the types of data that are discussed.
3. Policy Recommendations or Suggestions for the Way Forward:
Sustainability, Equality and Protection of Fundamental Rights especially for marginalized groups were identified as overarching goals for policymaking. The conflicts of interests between actors fall into the economic, political and social domain: In economic terms, commercial actors may be reluctant to share data for fear of losing their competitive advantage, especially because data has anti-competitive tendencies. Data sharing should however be encouraged to enhance competition and innovation, and to maximize welfare. Here, we need better data governance models to resolve the conflicts, and sometimes state intervention in data markets. In the social realm, data sharing may create new dangers for privacy, and intensify existing social inequalities. This issue is connected to technological challenges that arise from the possibility of recombining anonymised data. To confront this danger, should therefore foster citizen participation and transparency in the design of services and the governance of data, addition to the application of suitable privacy legislation.
4. Other Initiatives Addressing the Session Issues:
Several initiatives are developing data governance models to address the issues at hand, from commercial actors in many industries (for example automotives or health) to municipal actors in smart cities, non-government actors for example with an eye on inclusive design to geopolitically motivated initiatives like EU-wide data sharing ecosystems.
5. Making Progress for Tackled Issues:
Data Governance models, understood as legal, organizational and social norms which regulate the sharing and use of data in complex constellations of actors, are needed to resolve the common conflicts of interests. Another important measure is making design processes more participatory and generate awareness of intersectionality and possible discriminations.
6. Estimated Participation:
60 onsite; 25 women; 4 persons online
7. Reflection to Gender Issues:
There was an unusually high awareness for gender topics throughout the workshop. This was supported by the intervention of a trans woman on the panel and by gender-supportive moderation and agenda-setting of the workshop organizers.
