Ethics is the expression of values in actual behaviour. So when you want to do data ethics it is about practical issues, and reconsidering entrenched routines. In the past few weeks I successfully challenged some routine steps in a clients’ organisation, resulting in better and more ethical use of data. The provision of subsidies to individuals is arranged by specific regulations. The regulations describe the conditions and limitations for getting a subsidy, and specify a set of requirements when you apply for a subsidy grant. Such subsidy regulations, once agreed have legal status.
With the client we’re experimenting in making it vastly less of an effort for both requester and the client to process a request. As only then does it make sense to provide smaller sized subsidies to individual citizens. Currently there is a rather high lower limit for subsidies. Otherwise the costs of processing a request would be higher than the sum involved, and the administrative demands for the requester would be too big in comparison to the benefits received. Such a situation typically leads to low uptake of the available funding, and ineffective spending, which both make the intended impact lower (in this case reducing energy usage and CO2 emissions).
In a regular situation the drafting of regulation and then the later creation of an application form would be fully separate steps, and the form would probably blindly do what the regulations implies or demands and also introduce some overshoot out of caution.
Our approach was different. I took the regulation and lifted out all criteria that would require some sort of test, or demands that need a piece of information or data. Next, for each of those criteria and demands I marked what data would satisfy them, the different ways that data could be collected, and what role it played in the process. The final step is listing the fields needed in the form and/or those suggested by the form designers, and determining how filling those fields can be made easier for an applicant, (E.g. having pick up lists)
A representation of the steps taken / overview drawn
What this drawing of connections allows is to ask questions about the need and desirability of collecting a specific piece of data. It also allows to see what it means to change a field in a form, for how well the form complies with the regulation, or which fields and what data flows need to change when you change the regulation.
Allowing these questions to be asked, led to the realisation that several hard demands for information in the draft regulation actually play no role in determining eligibility for the subsidy involved (it was simply a holdover from another regulation that was used as template, and something that the drafters thought was ’nice to have’). As we were involved early, we could still influence the draft regulation and those original unneeded hard demands were removed just before the regulation came up for an approval vote. Now that we are designing the form it also allows us to ask whether a field is really needed, where the organisation is being overcautious about an unlikely scenario of abuse, or where it does not match an actual requirement in the regulation.
Questioning the need for specific data, showing how it would complicate the clients’ work because collecting it comes with added responsibilities, and being able to ask those questions before regulation was set in stone, allowed us to end up with a more responsible approach that simultaneously reduced the administrative hoops for both applicant and client to jump through. The more ethical approach now is also the more efficient and effective one. But only because we were there at the start. Had we asked those questions after the regulation was set, it would have increased the costs of doing the ethically better thing.
The tangible steps taken are small, but with real impact, even if that impact would likely only become manifest if we hadn’t taken those steps. Things that have less friction get noticed less. Baby steps for data ethics, therefore, but I call it a win.