The 100 Resilient Cities Centennial Challenge launched by Rockefeller Foundation published a draft “Code of Conduct’ that seeks to provide guidance on best practices for resilience building projects that leverage Big Data and Advanced Computing.
The following seven core principles serve to guide data projects to ensure they are socially just, encourage local wealth- & skill-creation, require informed consent, and be maintainable over long timeframes:
- Open Source Data Tools – Wherever possible, data analytics and manipulation tools should be open source, architecture independent and broadly prevalent (R, python, etc.).
- Transparent Data Infrastructure – Infrastructure for data collection and storage should operate based on transparent standards to maximize the number of users that can interact with the infrastructure.
- Develop and Maintain Local Skills – Make “Data Literacy’ more widespread. Leverage local data labor and build on existing skills. The key and most constraint ingredient to effective data solutions remains human skill/knowledge and needs to be retained locally. In doing so, consider cultural issues and language.
- Local Data Ownership – Use Creative Commons and licenses that state that data is not to be used for commercial purposes. The community directly owns the data it generates, along with the learning algorithms (machine learning classifiers) and derivatives. Strong data protection protocols need to be in place to protect identities and personally identifying information.
- Ethical Data Sharing – Adopt existing data sharing protocols like the ICRC’ s (2013). Permission for sharing Right Not To Be Sensed – Local communities have a right not to be sensed. Large scale city sensing projects must have a clear framework for how people are able to be involved or choose not to participate.
- Right Not To Be Sensed – Local communities have a right not to be sensed. Large scale city sensing projects must have a clear framework for how people are able to be involved or choose not to participate.
- Learning from Mistakes – Big Data and Resilience projects need to be open to face, report, and discuss failures. Big Data technology is still very much in a learning phase. Failure and the learning and insights resulting from it should be accepted and appreciated.