Justice Is the System Around Your Data: The Third Value of the DoGoDa Oath

Three criteria decided which students at DUO, the Dutch student grant agency, got flagged for a home visit: their type of education, the distance between their address and their parents’, and their age. Every student was measured by exactly the same rule. No machine learning anywhere in the process, just a set of criteria applied identically to everyone. In November 2024 the Autoriteit Persoonsgegevens ruled the whole approach unlawful. That verdict took a regulator and years to arrive. Nobody near the data had flagged it before then.

This is value 3 of 8 in the proposed data professional’s oath I have been reading and writing through, post by post: justice, right after dignity and interpersonal trust. At first sight justice looks a lot like fairness. If you ask me, the split between them decides what our job actually is.

The split I draw

Fairness lives within the data, the logic and the decisions they drive. Justice lives in the system around that. Treating everyone fairly by the rule, and still keeping them from what is rightfully theirs, is still injustice.

That distinction matters because fairness itself is not one settled idea. Ask ten data people to define a fair model and you get definitions that contradict each other: equal treatment on paper does not guarantee an equal outcome, and optimizing for one reasonable notion of fair can undercut another without anyone noticing. So my take for data professionals: treating fairness as the whole assignment cuts the job short by one layer. A team can get the fairness layer right and still ship a system that does injustice, because injustice is a property of what the system does in the world, not only of what the model does with its inputs.

No model required

DUO’s criteria prove the point without a single model in the room. Type of education, address distance, age: three variables, applied the same way to everyone, no protected attribute anywhere in the formula. Students with a non-European migration background scored higher on exactly those three criteria and were checked and visited more often as a result. The Autoriteit Persoonsgegevens found no objective justification for the pattern and ruled the processing unlawful.

That is the part worth sitting with. Every layer of that system passed its own test: the same rule, applied to everyone, with no explicit discrimination built in anywhere. Even though the scoring was equal across the board, it still caused injustice, because the rule’s outcome fell unevenly on people who were entitled to the same grant as anyone else. A fairness audit of the scoring logic alone would have found nothing wrong. Only a look at what the system did to real students, over time, would have caught it.

The duty of our seat

Data literacy keeps spreading, and tools like TimeXtender and Microsoft Fabric put analytics engineering in the hands of a much wider group of builders than five years ago. The odds of creating something good with data are rising. So are the odds of feeding a system that does injustice. I do not think most of us set out to build the second kind, on the contrary. But history has proven us capable of building unjust systems anyway, the DUO case is one example among many, and organizations have also proven capable of catching and correcting them once someone outside the room asked the right question.

Here is where the duty lands: we, BI professionals, data analysts and engineers alike, are almost never in the room where these systems get invented. The scope, the target variable, the criteria a case worker acts on, those decisions usually sit one or two departments away from the data professional’s desk. So if we are going to swear an oath that includes justice, let it be this one: always be on the lookout for the systems our data products might be feeding, and act the moment we get a whiff that something is wrong.

Dignity, the first value in this series, said a person is never just a row in your data. Interpersonal trust, the second, said the numbers behind a decision must stay explainable to the person acting on them. Justice adds the layer around both: watch what your product feeds into, not just what your product does on its own terms. The DUO criteria passed their own test. The lookout was the test nobody ran.

Happy to think this through with you.