The UK government is asking the public sector to work with data in an ethical responsible way. Therefore they have released an ethical framework to spur data ethical innovation. It deals with 7 principles and are mainly initiatives done on top of complying with existing law such as the General Data Protection Regulation, GDPR.
The UK public sector data ethics framework is centered around the user, the citizen (1) just like we do in DataEthics.eu’s principles (which will be released in an updated and detailed version in September). It also states that data ethics is build on top of existing legislation (2) including ‘data protection by design’ which is considered a legal requirement under GDPR.
The third principle (3) deals with proportionality and here principles of anonymisation is key. It states:
“When using personal data it is good practice to work with data that has been de-identified to the greatest degree possible. This process is often called anonymisation, although it is important to note that many forms of data can never be fully and irreversibly anonymised. Pseudonymisation is a related process of removing the most re-identifying components of the data and storing them separately. If data is anonymised to the greatest degree possible, it is likely to be out of scope of data protection law as it is no longer considered personal data. Pseudonymised data, however, is subject to the same laws as fully identifiable personal data.”
You can find more on anonymisation in this UK guide on Anonymisation Decision-making Framework.
Principle four (4) deals with the limitations of data and thus the quality of data. That there are often errors which need to be considered and bias from from historical decision making, or unrepresentative surveys or social media. Principle five (5) deals with your practices that need to be robust and consistent. Your algorithms must be accountable and you must be able to explain them to non-specialist.
It framework ask you to be transparent (5) about the tools, data and algorithms you use to conduct your work, working in the open where possible. This allows other researchers to scrutinize your findings and citizens to understand the new types of work we are doing. Wherever possible and without endagering privacy and intregrity of data you should work with open sources processes. And again, explainability is key:
“The more complex data science tools become, the more difficult it may be to understand or explain the decision-making process. This is a critical issue to consider when carrying out data science or any analysis in government. It is essential that government policy be based on interpretable evidence in order to provide accountability for a policy outcome.”
When it comes to personalisation there are also a explainability issue in the final principle (7) about responsible use.
“If fewer choices are presented as a result of personalisation, monitor your model continuously to make sure it’s still personalising effectively without negative consequences. You should also be prepared to be transparent about this process as it is essential you can explain clearly how any algorithm is personalising information.”
Both development and implementation teams must understand how findings and data models should be used and monitored with a robust evaluation plan.