Algorithm Ethics

An algorithm is a structured description on how to calculate things. Some of the most prominent examples of algorithms have been around for more than 2500 years like Euklid’s algorithm that gives you the greatest common divisor or Erathostenes’ sieve to give you all prime numbers up to a given maximum. These two algorithms do not contain any kind of value judgement. If I define a new method for selecting prime numbers – and many of those have been publicized! – every algorithm will come to the same solution. A number is prime or not.

But there is a different kind of algorithmic processes, that is far more common in our daily life. These are algorithms that have been chosen to find a solution to some task, that others would probably have done in a different way. Although obvious value judgments done by calculation like credit scoring and rating immediately come to our mind, when we think about ethics in the context of calculations. However there is a multitude of “hidden” ethic algorithms that far more pervasive.

On example that I encountered was given by Gary Wolf on the Quantified Self Conference in Amsterdam. Wolf told of his experiment in taking different step-counting gadgets and analyzing the differing results. His conclusion: there is no common concept of what is defined as “a step”. And he is right. The developers of the different gadgets have arbitrarily chosen one or another method to map the data collected by the gadgets’ gyroscopic sensors into distinct steps to be counted.

So the first value judgment comes with choosing a method.

Many applications we use work on a fixed set of parameters – like the preselection of a mobile optimized CSS when the web server encounters what it takes for a mobile browser. Often we get the choice to switch to the “Web-mode”, but still there are many sites that would not allow our changing the view unless we trick the server into believing that our browser would be a “PC-version” and not mobile. This of course is a very simple example but the case should be clear: someone set a parameter without asking for our opinion.

The second way of having to deal with ethics is the setting of parameters.

A good example is given by Kraemer et. al in their paper. In medical imaging technologies like MRI, an image is calculated from data like tiny elecromagnetic distortions. Most doctors (I asked some explicitly) take these images as such (like they have taken photographs without much bothering about the underlying technology before). However, there are many parameters, that the developers of such an algorithmic imaging technology have predefined and that will effect the outcome in an important way. If a blood vessel is already clotted by arteriosclerosis or can be regarded still as healthy is a typical decision where we would like be on the safe side and thus tend to underestimate the volume of the vessel, i.e. prefer a more blurry image, while when a surgeon plans her cut, she might ask for a very sharp image that overestimates the vessel’s volume by trend.

The third value judgment is – as this illustrates – how to deal with uncertainty and misclassification.

This is what we call alpha and beta errors. Most people (especially in business context) concentrate on the alpha error, that is to minimize false positives. But when we take the cost of a misjudgement into account, the false negative often is much more expensive. Employers e.g. tend to look for “the perfect” candidate and by trend turn down applications that raise their doubts. By doing so, it is obvious that they will miss many opportunities for the best hire. The cost to fire someone that was hired under false expectations is far less than the cost of not having the chance in learning about someone at all – who might have been the hidden beauty.

The problem of the two types of errors is, you can’t optimize both simultaneously. So we have to make a decision. This is always a value judgment, always ethical.

With drones prepared for autonomous kill decisions this discussion becomes existential.

All three judgments – What method? What parameters? How to deal with misclassification? – are more often than not made implicitly. For many applications, the only way to understand these presumptions is to “open the black box” – hence to hack.

Given all that, I would like to demand three points of action:
– to the developers: you have to keep as many options open as possible and give others a chance in changing the presets (and customers: you must insist of this, when you order the programming of applications);
– to the educational systems: teach people to hack, to become curious about seeing behind things.
– to our legislative bodies: make hacking things legal. Don’t let copyright, DRM and the like being used against people who re-engineer things. Only what gets hacked, gets tested. Let us have sovereignty over the things we have to deal with, let us shape our surroundings according to our ethics.


My slides on this topic:

At the last re:pubica conference I gave a talk and hosted a discussion on “Algorithm ethics” that was recorded. (in German):

Social Network Analysis of the Twitter conversations at the WEF in Davos

The minute, the World Economic Forum at Davos said farewell to about 2,500 participants from almost 100 countries, our network analytical machines switched into production mode. Here’s the first result: a network map of the Twitter conversations related to the hashtags “#WEF” and “#Davos”. While there are only 2,500 participants, there are almost 36,000 unique Twitter accounts in this global conversation about the World Economic Forum. Its digital footprint is larger than the actual event (click on map to enlarge).

There are three different elements to note in this visualization: the dots are Twitter accounts. As soon as somebody used one of the two Davosian hashtags, he became part of our data set. The size of the notes relates to its influence within the network – the betweenness centrality. The better nodes are connecting other nodes, the more influential they are and the larger they are drawn. The lines are mentions or retweets between two or more Twitter accounts. And finally, the color refers to the subnetworks or clusters generated by replying or retweeting some users more often than others. In this infographic, I have labelled the clusters with the name of the node that is in the center of this cluster.