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2Understanding algorithms

“The machine is not a thinking being, but simply an automaton which acts according to the laws imposed upon it.” ¹

Luigi Federico Menabrea (1809–1896)

on Babbage’s Analytical Engine

There are too few people like James Vacca: politicians who are diligently fighting for transparency and ways to regulate algorithms. Even if the latter are not as widespread in other countries as in New York City, they have long since become our constant companions. For more than 30 million Germans, Facebook’s algorithms determine what content they see in their timeline and which “friends” the online network suggests to them. Fitness trackers have become everyday accessories, recording how we move and automatically encouraging us to do sports regularly. Companies are increasingly using robo-recruiting software to hire employees. And the public sector is also gradually discovering algorithmic systems, for example to assign slots at schools and universities as fairly and efficiently as possible, and to prevent burglaries and thefts.

German ignorance, indecision and discomfort

Despite all these examples, when it comes to algorithms, ignorance, indecision and discomfort prevail in Germany.² According to a representative survey, almost half of the people in the country cannot say what the term algorithm means when asked; only 10 percent know exactly how algorithms work. Some 50 percent of respondents suspect at best the use of automated decision-making for dating portals or personalized advertising, while only a minority are aware of other areas of application, such as the pre-selection of job applicants or predictive policing. This ignorance is reflected in indecision: Almost half of the population has not yet decided whether algorithms bring more advantages or disadvantages – an extremely high figure in the world of opinion research. That shows that the public debate on this issue is still in its infancy. Moreover, the level of discomfort surrounding the topic also mirrors the uncertainty, with most respondents preferring human assessments to algorithmic ones. Almost three-quarters even advocate a ban on decisions made by software running on its own.

On the one hand, hardly any fears of daily interaction, on the other hand, a highly skeptical attitude – according to many studies, this ambivalent relationship characterizes the way Germans respond to digitalization.³ We have become so accustomed to some algorithms that we no longer perceive them as such. In the past, anyone who had to hit the brakes in a car on a wet road often found himself skidding. Thanks to ABS, sensors measure whether the vehicle is about to fishtail, and an algorithm automatically optimizes the rapidly repeated braking needed to safely slow the car. All the driver has to do today is put constant pressure on the pedal; it is no longer necessary to skillfully pump the brakes. According to a study carried out for Germany’s insurance industry, ABS and other assistance systems prevent what would otherwise be an unavoidable rear-end collision in approximately one out of every two critical situations.⁴

The algorithms hidden under the hood make their own decisions. Nevertheless, we hardly feel uneasy about it; on the contrary, every assistance system is one more reason for buying the car. Very few people are interested in how exactly software helps avoid collisions, change lanes and keep a safe distance from surrounding objects. On the other hand, we would probably feel much more discomfort if an IT company and not a judge were to decide on which prisoners should qualify for early release. How the government exercises its monopoly on power has a completely different impact on a society than even the most effective automotive tools.

A simple recipe

When the Muslim scholar Al-Khwarizmi taught his students written arithmetic in Baghdad in the 9th century, he could not have guessed that one of the most important terms of our time would be derived from his name. “Algorithm” means nothing more than a clearly formulated sequence of actions which is worked through step by step in order to reach a certain goal.

A baking recipe is also an algorithm. If you have the right ingredients and kitchen utensils and follow the instructions, you will get what you want: a delicious cake. Increasingly important in daily life are software algorithms, on which we focus in this book. They function according to the same principle. However, in their case it is not a human being but a computer that carries out the single steps.

A simple example: Suppose you want to sort a large list of numbers from the smallest to the largest. If a computer is to perform this task, it needs clear and, above all, unambiguous instructions as to what it has to do. The goal of “sorting numbers” must be broken down into individual steps. A software developer could use the so-called bubble sort algorithm for this purpose. In each step, the computer would compare adjacent pairs in the series of numbers and, if necessary, swap them if the second number is smaller than the first one. It must repeat this task until all neighboring pairs – and thus the entire sequence – are sorted in ascending order.

Just as there are countless baking recipes, there are many different types of algorithms. In addition to the sorting algorithm described above, the simpler ones include spell-checking tools in word-processing programs. Complex algorithms, on the other hand, are able to learn on their own. For example, an algorithm in a self-driving car could come to understand that a ball rolling onto the road is likely to be followed by a child, and it would therefore reduce the vehicle’s speed. Whether simple or complex, in this book we are interested in algorithms that are relevant to society and that raise political questions.

When algorithms become political

Public debates and democratic decisions are sometimes necessary even in cases where one would not immediately suspect it. Navigation systems that display accidents and recommend detours have become an indispensable part of any car or smartphone. They used to recommend the same route to everyone when traffic jams occurred – leading in many cases to congested detours. Today, navigation systems redirect motorists to different routes depending on the current flow of traffic, reducing traffic load.

An interesting question from the policy perspective is which alternatives the navigation system is allowed to offer. If it is set to only show the quickest way, it might lead drivers through residential areas. At present, citizens’ initiatives are already being launched to block certain roads for through traffic and remove these shortcuts from route-planning software.⁵

And here is an intriguing thought experiment: Let us assume that a highway is to be temporarily closed and there will be a short and a long detour, both of which are needed to keep the traffic flowing. Which criteria should the navigation algorithm use to make its recommendation? An ecologically oriented programmer would perhaps specify that the fuel-efficient cars should be shown the longer route and the gas guzzlers the shorter. After all, this would protect the environment. However, it would not be fair from a social perspective if people with expensive luxury cars reached their destination faster than others. An algorithm optimized for fairness would probably be programmed to make a random choice about who is shown the long detour and who sees the short one. This in turn would not be the best alternative in terms of environmental impact. There is no clear right or wrong here; a policy choice is needed. And this should not be left to the car manufacturers or programmers, but should be discussed publicly.

Distorted images of a superintelligence

When we talk about algorithms, the term artificial intelligence (AI) quickly comes up. This refers to computer programs designed to imitate the human ability to achieve complex goals. In reality, however, AI systems have so far been anything but intelligent; instead, they are machines well trained for solving very specific tasks. People have to define the tasks and train the devices, because an algorithm does not know on its own whether a photo depicts a dog or a house or whether a poem was written by Schiller or a student in elementary school. The more specific the task and the more data the algorithm can learn on, the better its performance will be.

In contrast to human intelligence, however, AI is not yet able to transfer what it has learned to other situations or scenarios. Computers like Deep Blue can beat any professional chess player, but would initially have no chance in a game on a larger board with nine times nine instead of eight times eight squares. Another task, such as distinguishing a cat from a mouse, would completely overwhelm these supposedly intelligent algorithms. According to industry experts, this ability to transfer acquired knowledge will remain the purview of humans for the foreseeable future.⁶ Strong AI, also called superintelligence by some, which can perform any cognitive task at least as well as humans, remains science fiction for the time being. When we talk about AI in this book, we therefore mean what is known as weak or narrow AI which can achieve a limited number of goals set by humans.

The debate about artificial intelligence includes many myths. Digital utopians and techno-skeptics both sketch out visions of the future which are often diametrically opposed. Some consider the emergence of superintelligence in the 21st century to be inevitable, others says it is impossible. At present, nobody can seriously predict whether AI will ever advance to this “superstate.”⁷ In any event, the danger currently lies less in the superiority of machine intelligence than in its inadequacy. If algorithms are not yet mature, they make mistakes: Automated translations produce nonsense (hopefully not too often in this book), and self-driving cars occasionally cause accidents that a person at the wheel might have avoided.

Instead of drawing a dystopian distortion of AI and robots, we should put our energy into the safe and socially beneficial design of existing technologies. In the thriving interaction of humans and machines, the strengths and weaknesses of both sides can be meaningfully balanced. This is exactly the subject examined in the following two chapters.