Tomorrow is Good: The Professor and the Politician

The professor and the politician sat at the table of a lunchroom in The Hague with cups of fresh mint tea. The politician had invited the professor to talk about the transparency of algorithms. He wanted to set strict rules for the use of algorithms by the government, with emphasis on the word “strict,” he added.

The politician said, “I want a watchdog who will check all the government algorithms,” words which he clearly found unsavory. The professor noticed that the politician had a preference for the words “rules” and “watchdog”, and for the expression “with an emphasis on…”.

The usefulness of a watchdog

By the time they had finished their first cup of tea, they had found that there are roughly two types of algorithms: simple and complex. Simple algorithms, they thought, translate rules into a kind of decision tree. On a napkin the politician drew blocks and lines to represent this and as an example she cited the application for a rent allowance. She noted that there are already thousands of simple algorithms in use by the government.

The professor suggested that such algorithms could be made transparent relatively easily, but that this transparency would actually bring you back to the regulations on which the algorithm is based. Sipping his tea, he added: “So you could rightfully ask what the use of an algorithm watchdog would be in this case.”

At this point, the conversation stopped for a moment, but then they decided they agreed on this after all.

“B-uu-uu-t,” said the politician, looking ominous again, “then there are the complex algorithms. Neural networks and all that.'”

The professor looked thoughtfully out the window since that seemed like the right thing to do, then replied that neural networks are as transparent as the human brain. If you could make neural networks transparent, you wouldn’t be able to derive anything from them.

The politician nodded slowly. She knew that, too.

Training the network

You can train such a network, you can test the outcome and you can also make it work better, but transparency, or the use of an algorithm watchdog, wouldn’t add any value here either, the professor concluded.

Once again, the conversation came to a standstill.

The politician had spoken and the professor couldn’t disagree with her. “That’s precisely why I want a ban on the use of far-reaching algorithms by the government,” added the politician, “emphasis on the word ban.”

“The effect would then be counterproductive,” the professor said, “by prohibiting the use of algorithms by the government, you create undesirable American conditions in which commercial parties develop ever-smarter algorithms, become more powerful as a result, and in which the democratically elected government becomes marginalized.

The professor felt that the last part of his sentence had turned out to be softer than he would have liked. He considered repeating it, but instead asked “Why do you always use the word ‘watchdog’?”

“Because a watchdog conveys decisiveness,” the politician replied. “We want to make the public feel safe with the government, and a watchdog is a good representation of that.”

Curious bees

The professor was starting to feel miserable. The government as a strict watchdog? The image reminded him of countries like China. Or America.

“I don’t like that metaphor,” he said, “it has such an indiscriminate character. It’s powerful, but also a bit stupid and simplistic.

“Then why don’t you come up with a better analogy!” the politician challenged him cheerfully.

The professor was reminded of an article he had recently read and replied: “I think the image of a bee population would fit better.” It was a somewhat frivolous answer, but in a bee colony, curious bees are sent out to look for opportunities that are of value to the entire colony.

The politician laughed a lame laugh.

“Nice image, professor, but an algorithm bee wouldn’t work in the political arena!”

The professor suspected that the politician had a good point there.

They had one final cup of tea together and then once again went their separate ways.

bout this column:

In a weekly column, written alternately by Bert Overlack, Mary Fiers, Peter de Kock, Eveline van Zeeland, Lucien Engelen, Tessie Hartjes, Jan Wouters, Katleen Gabriels and Auke Hoekstra, Innovation Origins tries to figure out what the future will look like. These columnists, occasionally joined by guest bloggers, are all working in their own way on solutions to the problems of our time. So that tomorrow is good. Here are all the previous articles.

Tomorrow is good: Classification, identification, a search for true identity

Are you dead? Or are you a woman? Or are you a famous person off the radio or television? These are typical questions for ‘Who am I?’ – a popular quiz that is in essence based on classification processes. As in the classification of information into certain categories. The aim of the quiz is to create a category that is so small that there is only space for a single person in the end. The reason why the quiz is so popular is that the structure of questions and relevant follow-up questions are actually often too complex for most people. In other words, our own cluelessness make us laugh.

This reasoning method is easier for computers. The structuring of questions – and relevant follow-up questions – can be converted comparatively easily into an algorithm that is able to classify these. The answers to the questions posed lead to classifications of items that have certain characteristics in common.


The right to privacy is considered so important in the Netherlands that it has been laid down in the Constitution. For example, there are rules for any data that might lead to the identification of a private individual. The term ‘identification’ in this context refers to a link with information related to identity, like a name, address, date of birth and social security number. Identification is as such intrinsically different from classification. Whereas the identification process leads to one (private) individual, the classification process leads to a category that may be small, except that in principle it does not name a specific person.

Nevertheless, in actual practice this distinction is vague. Most of us are willing to give up data that leads to our identity in exchange for the ease of convenience or forms of amusement. We provide messaging applications with real-time insight into our social network. We send our biometric data to hardware manufacturers in order to unlock our equipment. Nowadays, calendars, e-mails and documents are managed by service providers and private photos are placed in computer networks. Moreover, this data is increasingly becoming more of a valuable commodity which companies use to combine all kinds of data. And in that process, our true identities are revealed.

Numb fingers

As early as 2006, AOL (America Online) disclosed the key search terms used by some of its users. All of its users were given a unique number in order to safeguard their privacy. User 4417749 searched for ‘numb fingers’, ‘Single men 60’ and a ‘dog that urinates on everything’. It turned out to be fairly easy to create a category that was so small, only one person could fit into it. This was done based solely on the user’s key search terms. That’s when they came up with Mrs Thelma Arnold from Lilburn, Georgia.

Classification leads to categories, and identification leads to one private individual. Yet the combination of different types of data can reach an even deeper and more authentic level of identity, as in a ‘true identity’. A true identity has to do with the innermost essence of a human being. And with the social and cultural network wherein a person finds themselves, along with their profound underlying fears and aspirations. Mrs Arnold’s true identity is perhaps linked more to her key search terms than to her actual name.

About this column:

In a weekly column, written alternately by Bert Overlack, Mary Fiers, Peter de Kock, Eveline van Zeeland, Lucien Engelen, Tessie Hartjes, Jan Wouters, Katleen Gabriels and Auke Hoekstra, Innovation Origins tries to figure out what the future will look like. These columnists, occasionally joined by guest bloggers, are all working in their own way on solutions to the problems of our time. So that tomorrow is good. Here are all the previous articles.



Tomorrow is good: Data, not words, if you really want to do something about subversive organized crime.

Recently the report De Achterkant van Amsterdam (The Other Side of Amsterdam) was presented. A report on the investigation carried out by Pieter Tops and Jan Tromp into drug-related financial transactions and how they take place in the main city. The report describes in no uncertain terms the destructive effect of subversive crime on our society.

More or less at the same time as the presentation of this report, Den Bosch wrapped up the project Weerbaar (the Resilience project). A project, financed by the Dutch Ministry of Justice and Security, which revealed that an effective way to combat subversive crime starts with data. Test data from the Oost-Brabant Police has been combined with that from the municipality of Den Bosch along with information from open sources. All of this data was brought together in a scenario-based model that recognizes patterns, identifies indicators of subversive crime and generates future scenarios. The outcomes, possibilities and risks of this model were assessed by scientists from the Jheronimus Academy of Data Science. I had the privilege of being involved in this research myself.

Pieces of the puzzle

When the knowledge and experiences from De Achterkant van Amsterdam, are combined with the lessons from Weerbaar, an effective approach to combating subversive crime comes to the fore. De Achterkant van Amsterdam, for example, shows that research has been done into associations (as in non-profit organizations) that own expensive cars or a lot of real estate (Amsterdam CID, 2017). It is also known how many applications for licenses for the catering industry are privately financed (Municipal study, 2019). The number of expensive properties sold in Amsterdam without a mortgage was investigated (DNB, 2017). And it is known how many people act as financiers for personal loans, who, according to the tax authorities, do not have the capital to do so (Report on Personal Loans, 2019).

The project Weerbaar shows that it is entirely possible to combine the above sources together with police data and to translate these into practical scenarios. Moreover, there is no doubt that the results of all this is much more than the sum of its parts and that valuable, previously unknown insights have emerged. What has become clear from both studies is that many parties hold pieces of the puzzle when it comes to subversive organized crime, but there’s no one who is overseeing the whole puzzle.

It is important in this context to stress that it is of course not illegal for an association to own expensive cars or expensive buildings. Nor is it illegal to issue personal loans, nor illegal to purchase real estate without a mortgage. But when this information is combined with other types of data, for example from the Chamber of Commerce, the Land Registry, the Inland Revenue Service, the Salvation Army, the shopkeeper’s association, the municipality and the police – a picture can be formed of a situation that points to less legal practices.

This investigative method is as old as the Methuselah. Every detective works like this. The big difference is that a detective brings together information on an incidental basis and is only able to investigate a very limited amount of data, while technology can bring together data in a structural way (and in real time), and moreover, do this with very large amounts of data.

Underlying problems

One of the conclusions of the Tops and Tromp report is that the competency of ‘cooperation’ is not particularly highly developed within the Amsterdam governmental agencies. This brings us to the actual and underlying problem. Now that it is clear that subversive organized crime can be tackled more efficiently than is currently the case, the following painful question must be answered: Do we really want that? Is the disruptive nature of subversive crime on our society large enough for us to genuinely want to work together and share data? And do we sincerely want to look for the scope that laws and regulations offer us for that?

Because if the answer to this is ‘yes’, then from now on the motto is: data, not words.


About this column:

In a weekly column, written alternately by Maarten Steinbuch, Mary Fiers, Peter de Kock, Eveline van Zeeland, Lucien Engelen, Tessie Hartjes, Jan Wouters, Katleen Gabriels and Auke Hoekstra, Innovation Origins tries to figure out what the future will look like. These columnists, occasionally joined by guest bloggers, are all working in their own way on solutions to the problems of our time. So that tomorrow is good. Here are all the previous articles.

Tomorrow is good: Watch out for the undercover assistant.

“Apple listens to sex via Siri” has been headlining the media a lot over the past week. So, Apple joins Google and Amazon, the illustrious list of companies that listen in on our lives and are learning from us. Just like an undercover agent passes on information to their client, the undercover assistant is infiltrating our lives.

For years the big tech giants have been engaged in an epic battle to offer a digital assistant. The ultimate goal is to develop a personal assistant that makes the user’s life easier by carrying out certain tasks. But should it no longer be a secret that the real goal of these companies is to better understand their users, so as to gain a commercial advantage.

Breaking the rules

The problem that surfaced last week concerns voice activation or voice control. In order to be able to perform a task, the voice-activated assistant listens to the user (after all, how does Apple know that you said “Hey Siri” if it doesn’t listen in?). As soon as the assistant recognizes the catchphrase, the system is activated. This can lead to a situation in which the system ‘thinks’ it hears the catchphrase, even though it hasn’t been voiced. The system then starts recording, although it does not discover a question or an assignment. Well, in order to improve speech recognition, some of these soundbites are analyzed by humans. Privacy organizations are now examining the question of whether these rules have been violated when it comes to the use of personal data.

But the underlying question is much more pertinent. Are we sufficiently aware of what we are granting the Digital Assistant access to?

The telephone manufacturer has our biometric data at their disposal for unlocking our device. The messaging service has a very accurate and up-to-date picture of our contacts. The navigation app knows who lives where, and who has a relationship with whom. The social media company is able to predict our behaviour better than our partners, and the smart wearable collects health data and sports performances on a server. Lastly, our search engine knows our deepest fears and desires.

In return for a little bit of convenience, we hand over en masse sensitive or very specific (personal) data to commercial companies. We do this more or less consciously, and in accepting the terms and conditions, we take the privacy risks for granted. But the Digital Assistant is designed to connect data from various applications. This creates a risk that is not only greater than the sum of its parts, it transcends the individual user and has far-reaching consequences for (the privacy of) others.

Diary access

Take the following, hypothetical example: When I ask the digital assistant “What’s my next appointment?” that question can only be answered if the Assistant already has access to my calendar. This includes appointments with non-users. For example: Sanne’s Birthday Party. In itself, this is not sensitive information. But when I then give the command “Navigate to Sanne de Vries”, the assistant gets her address and links it to the navigation app. When I instruct the assistant “Pay 50 euros to Sanne for the trip to Sudan”, payment details are linked. And when later in the evening I ask the assistant to send Sanne the text – “I still can’t believe you’re going to be a mother,” her mobile number is accessed from my contact list.

Interaction with the digital assistant produces an image of Sanne, whereby her name, date of birth, address, mobile number, financial data, travel plans, and even information about her health are all brought together. And of course the digital assistants do not work in ‘splendid isolation’. They form a global network and learn from each other. The Alexa’s, Siri’s, Cortana’s and Google Assistants provide their real bosses with an increasingly accurate picture of the lives of their users, but also indirectly of the lives of the relationships of these users.

Mission accomplished

Creating and ‘running’ an undercover agent is time consuming and very expensive. When the value of the information exceeds the costs, the mission is successful. A similar calculation applies to the digital assistant. We have to realize that assistants which are offered under the pretext of being ‘free’ are created and run by commercial companies. And those companies make a lot of money from information about your life and mine.


Tomorrow is good: Algorithms don’t discriminate

In recent weeks there has been a lot of attention in the news about the use of algorithms by the government to detect fraud and crime. Congratulations! I’d say we have a government that’s getting more efficient and moving with the times. It would be much more disturbing to learn that the government still does not use predictive algorithms.

Yet in the media, this issue was highlighted from an entirely different perspective. For instance, the Dutch news agency NOS published this article, which in English is titled : “Government uses algorithms on a large scale, ‘risk of discrimination’. This article went on to state that the use of predictive algorithms involves a high risk of discrimination. This article led to indignant reactions from readers, which made it clear that the discussion on the use of algorithms is to a large extent guided by emotions. In doing so, the fact that the title of the article is tendentious to say the least, but is also factually incorrect, seems to have been overlooked.

Better and faster than people

The literal meaning of the word discrimination is ‘the act of making a distinction’. And that’s exactly what an algorithm does. It classifies data on the basis of their relationships into characteristics. And it does that much better and faster than people are able to do. But if you take the literal meaning of the word discrimination as a starting point, the assertion that the use of predictive algorithms entails a high risk of discrimination is nonsensical. You would then have to state: ” Algorithms discriminate, that’s what they’re made for.”

Nevertheless, in a social context, discrimination stands for something quite different. This is about making illegal distinctions (on the basis of gender, religion, conviction, sexual orientation, etc.). And that’s exactly what an algorithm does not do. An algorithm always produces the same output with the same input. It is amoral and cannot therefore, by definition, make an illegal distinction. Popularly put; an algorithm is not subject to a night’s sleep deprivation or an unpleasant experience with the downstairs neighbor. Whereas people are. Still, the confusion is understandable. An algorithm is created – and learns – on the basis of data. And that is where the difficulty lies. Data is not free of human influence and can be ‘biased’ in many ways. It is therefore quite possible that there are aspects hidden in data that lead to discrimination.

Simple to detect

So just as discriminatory aspects may be hidden in the human process, they may also be hidden in data. The main difference, however, is that discrimination within the human process is very difficult to detect and correct, as we have learnt from history. Discrimination within data, on the other hand, turns out to be relatively easy to detect, and also much easier to correct. Algorithms are able to contribute to this.

That is why, on the basis of the social significance of the word discrimination, I would like to make the following point: Algorithms do not discriminate. Provided they are controlled by people, they can contribute to a society wherein everyone shall be treatedd equally on equal terms.

“All persons in the Netherlands shall be treated equally in equal circumstances. Discrimination on the grounds of religion, belief, political opinion, race or sex or on other grounds whatsoever shall not be permitted.” (Article 1, Dutch Constitution)


About this column:

In a weekly column, alternately written by Eveline van Zeeland, Jan Wouters, Katleen Gabriels, Maarten Steinbuch, Mary Fiers, Lucien Engelen, Peter de Kock, Tessie Hartjes and Auke Hoekstra, Innovation Origins tries to find out what the future will look like. These columnists, occasionally supplemented with guest bloggers, are all working in their own way on solutions for the problems of our time. So tomorrow will be good. Here are all the previous episodes.

Tomorrow is good: Where Art and Science meet

It’s common practice that a column reflects the personal point of view of the writer. However, as a Canadian philosopher, Marshall McLuhan once stated, ‘A point of view can be a dangerous luxury when substituted for insight and understanding’. As this is my first contribution to Innovation Origins, I would like to take the opportunity to devote this article to the intersection of Art and Science. Not only is this a subject that defines my professional career, it offers me the possibility to briefly introduce myself as ‘the new kid on the block’ of columnists that regularly contribute to Innovation Origins.


Having been educated at the Amsterdam School of the arts, I travelled the world as a filmmaker -or modern-day storyteller- for fifteen years. Subsequently, I went to work for the Dutch national police as chief of several covert operations sections. During this time, I earned my PhD based on combining art and science to anticipate criminal behaviour. Only recently, I have been appointed Professor of Practice at Tilburg University as head of the department Data Science in Crime and Safety at the Jheronimus Academy of Data Science in Den Bosch.

Some people (including artists and scientists) find the territory where art and science meet difficult to navigate. While artists are primarily concerned with the subjective realm of emotions, scientists are concerned with the body of knowledge that can be rationally explained and reliably applied. However, in my opinion, the ‘common source’ of the two disciplines should be appreciated.

Social creatures

By nature, humans are fearful social creatures. From our bare existence, we have been terrified by things we don’t understand and (maybe, therefore) have become social creatures that want to communicate and describe our experiences. Both art and science are attempts to understand and describe the world around us. One might argue that science is primarily related to the process of understanding, while art is related to that of communication. But in my opinion, understanding the world and describing the experience are essential for a successful scientist as well as a competent artist.

My exploration of the common ground between art and science has enhanced my appreciation for the wonders of art and deepened my recognition for the wisdom of science. To me, art and science are best to be seen as two complementary perspectives on the world. Both artist and scientists operate at the verge of what is known, and by their curious nature aspire to understand that which is unknown. Moreover, artist and scientists share fundamental characteristics: a natural curiosity, a drive for experimentation, an ability to doubt, a fearlessness to make mistakes, and to learn from their failures. However, while the similarities may be obvious, the two disciplines rarely integrate, and that is unfortunate. For where two rivers meet, the strongest current occurs. Integrating art and science provides a deeper understanding of the world, beginning in wonder and ending with wisdom.

I do realise that I may have introduced a dangerous luxury by confusing ‘point of view’ with ‘insight and understanding’. But then again; wasn’t Marshall McLuhan just another fearful social creature? It’s not that he predicted the Word Wide Web thirty years in advance.

Marshall McLuhan (1911 – 1980). McLuhan used the metaphor ‘global village’, to describe a situation in which all ‘participants’ had more or less equal access to public information. It was not until about thirty years later when the internet was invented, that McLuhan’s metaphor was fully realised.

About this column:

In a weekly column, alternately written by Eveline van Zeeland, Jan Wouters, Katleen Gabriels, Maarten Steinbuch, Mary Fiers, Carlo van de Weijer, Lucien Engelen, Peter de Kock, Tessie Hartjes and Auke Hoekstra, Innovation Origins tries to find out what the future will look like. These columnists, occasionally supplemented with guest bloggers, are all working in their own way on solutions for the problems of our time. So tomorrow will be good. Here are all the previous episodes.