Are we going to be slaves of algorithms?

Server published an interview with Josef Šlerka, an expert on new media (translation by Google Translate). He warns that we can become slaves to algorithms that we do not understand. This issue has been raised repeatedly in media. I don’t doubt the fact that the algorithms are much more important in our lives than ever before. I do not think that we understand all algorithms – especially neural networks are problematic in this regard because we do not know exactly why the network made a particular decision. We can only tell how well the network performs given the inputs and outputs used during training phase. Corner cases are sometimes unknown and analytical understanding in extreme situations is quite difficult. Let me, however, explain my slightly different and less pessimistic view on the role of algorithms in our lives.

Mr. Šlerka mentioned an experiment, in which Lukasz Barabasz showed that given location information of people during a longer time period, he is able to predict a person’s location the next day at a given time. He used data collected from cell towers. The problem in this case is not a prediction algorithm – it is quite simple and it performs pretty well (and in this case, we understand it pretty well too). We are just being predictable. If you have something to worry about in this example, it is the possibility to collect data (what Mr. Šlerka also mentions). There is even a scarier algorithm that can identify a particular person by their movement itself (even if it’s recorded with a different device). Our movement is like a fingerprint.

The problem is not the algorithm. The algorithm is like a mathematical equation – when you invent it, it exists. Inventions like this cannot be “undone” – it is not possible to forget or ban it once it’s out. Algorithm is like an idea. If we really care about our privacy, blaming the algorithms will not help. We need to make sure that these algorithms do not have enough inputs to do things we do not want them to do. Is it possible to create anonymized mobile phones, where the operator know how much to bill us, but does not know our location at any time? I bet it is possible, but is there enough consumer demand?

Quote praised in headline of the article reads (translation from Czech is mine): “With the advent of technology and applications of artificial intelligence and neural networks, the majority of people loses understanding about what a computer does, and how it makes it’s decisions. In other words, we become slaves to algorithms we do not understand. “

Let’s talk about two different methods of decision-making – i.e. “table-based decisions” and “fuzzy” decision. Computers have been criticized for being to discreet, for having no smooth decision area. They were not human enough. An example “table” decision process is for example deciding whether an ATM (algorithm) or a bank clerk (person) should let you withdraw money from your account. Both decisions are based on the same table: If the available account balance is greater than or equal to the amount the customer wants to withdraw, customer gets their money. If it is less, do not allow this withdrawal. The algorithm is the same for human beings and machines and we understand it very well.

How about a loan? Bank clerk can say “This customer looks insincere” or he “was too nervous.” Alternatively officer does not trust that the underlying business plan of a company asking for a loan is sound. This is not a table-based decision – the bank representative decides on the basis of their feeling, which can be justified, but surely it cannot be explained in exact terms. Another bank clerk could decide differently.

The algorithm for bank loans is (or can be) similar to this line of thinking. We taught the algorithm that people with certain credit profile do not pay back. The input can be: financial behavior (as learned from the customer’s history in the bank), age, number of children or any other additional information available to the bank. If the algorithm is based on neural network, it could just say “loan rejected”. No explanation. In most cases, the neural network’s output is a score on some scale (for example 0 to 1), in which case a negative decision is something closer to zero (or less than some predefined threshold). We do not know why exactly the network’s output is a particular score.

A common example of algorithm critics is high frequency trading (HFT). HFT algorithms are used very successfully for several years. A human being simply cannot make decisions about buying and selling of a variety of asset classes several times per second. Can they cause a crisis? A common example that they can get “crazy” is the book The Making of a Fly by Peter Lawrence, which sold on Amazon marketplace for $1,730,045.91 due to an algorithm that set this price. The problem was that there were two competing algorithms. They go through Amazon marketplace and try to find rare products and offer them at a higher price than other sellers. When someone buys a book from a seller that has a higher price (e.g. due to higher reputation of the seller), the author of this algorithm orders the book from a dealer with a lower price. When it arrives, they deliver it to final customer and keep the price difference as profit. It gets interesting when the original item is sold and the only vendors are the automated trading bots. They start to raise prices to top up the best available seller. And depending on the periodicity of checking and harvesting the marketplace, the price starts going up. Neither of the seller has the goods available. They rely on each other for delivering the nonexistent product. The algorithm tries to make a profit and this corner conditions are not accounted for – so they get “crazy” while seeking profit…

Are we different? During 1636-1637 we witnessed one of the first bubbles. In the Netherlands, tulips have become popular and everyone wanted this beautiful flower (or it’s bulb actually). Many people wanted it because of it’s inherent beauty, but a lot more people perceived the price increase and wanted to buy cheap and sell for more later. The result was a bubble and its collapse. In the winter of 1636-1637, some bulbs changed hands ten times a day. During the peak of the bubble in February 1637, some onions sold for more than ten times the annual income of a skilled craftsman. People went crazy for a while. Do algorithms really behave differently to us or they are just getting more similar to us? Isn’t that what worries us?

Shai Danziger of the University of the Negev has done an interesting research on the Israeli judicial system. He examined the results of 1112 parole hearings. The judges had an average of 22 years of experience and their decisions accounted for 40% of cases of parole decisions during the investigated 10-month period. The results are quite uncomfortable for justice: Judges decided in favor of parole before their morning snack, lunch and before the end of working hours with much lower probability. Parole was granted in up to 20% of cases. Immediately after a meal, the chance of a positive decision was 65%. Note that this is no small statistical error, but a significant difference.

Our decisions are controlled by a number of factors we do not understand. Our neural network in the brain makes decisions that we not only don’t understand, but they are not consistent. The level of certain hormones in our body, mood, concentration, and hunger, even the lighting, biases us. These biases are significant and affect lives of people around us (such as judges granting or not granting parole based on when they ate).

If we are asking ourselves whether we are slaves to algorithms we do not understand, I would first ask: Aren’t we slaves to senseless human decisions we do not understand right now? The algorithm decides consistently and if it is flawed, we can at least quickly find out and fix it. Can we fix people this way?

Personally, I would not neither overestimate nor underestimate the role and threat of algorithms. They are tools for people. Let’s talk about what data are collected about us. That is what gets abused. If it is a person looking at a data or a highly efficient algorithm, it does not make such a difference. What external organizations (or people, companies, States) have power over our lives? Rather than adding algorithms to what we should “fight against”, I decided to become interested in the necessary conditions for their functions – data collection. Let’s not fear the algorithms. Let’s fight against everything that we can control that limits our freedom. Whether it’s an algorithm, hungry judge or greedy state backed the wrong econometric model…