In September 1984, Vladimir Rybakov published his algorithm to determine the admissible rules of intuitionistic propositional logic. Creating this algorithm was no easy matter: the conceptual machinery required to do so had to be created nearly from scratch. Numerous logicians have used and explored comparable concepts in the years since. At the end of my time as a PhD candidate, I connected Rybakov’s approach to these now well-understood concepts. The resulting paper Decidability of Admissibility has just been published.

Many organisations rely on technological systems to help mitigate compliance risk. Think for example of the post-event transaction monitoring systems used throughout the financial sector to mitigate risks of money laundering and terrorism financing. Increasingly sophisticated quantitative methods such as machine learning are finding their way into the field. How can the compliance professional stay on top of these developments?

Sharing scientific thinking with a lay audience can have interesting consequences — intended and otherwise. Back in 2016, I attended one such lecture on the topic of “attention”. I suppose it is fair to say that neither me nor the speaker could have foreseen this consequence: a machine-learning based classification model of viewing behaviour.

Compliance heeft toe te zien op het integer gebruik van algoritmes. Algoritmes zijn zelden waardenvrij. In hun implementatie wordt al dan niet bewust stelling genomen in de spanningen tussen meerdere conflicterende waarden. De uitdaging is om te borgen dat deze stellingnames bewust plaatsvinden in de daarvoor gepaste gremia.

Algoritmen helpen om op grote schaal beslissingen te nemen. Het is echter lastig om achteraf toe te zien op de kwaliteit van deze beslissingen. Toezicht zou zich met name moeten richten op het wordingsproces van algoritmes: de stappen die genomen worden om te komen van probleemomschrijving tot...