Data Protection in the XXI Century

Keyhole. Photo Credit: Public Domain

The previous century has given as a heritage the information technologies. There is a great consensus about the importance of IT as the new industrial revolution. It is true that the productivity or our societies has got a huge increase due to digital information processing and wide connectivity. In the first half of the XX century the world was local and national. However, today, the world is global and international. This cannot be possible without the capability to establish connections and to make electronic transactions and businesses in real-time through a totally connected communication network.

Modern organizations must be adapted to the new paradigm. The digitization of businesses is a must in the near future for any society and the adaptation of national legislations to the current real world is something unavoidable.

One of the main challenges of our societies can be that legislation usually is not very friendly with technology changes. The ancient Greek philosopher Aristotle said several millennia ago, that the laws must last for a long time in order to be effective. Many changes of laws only produce legal uncertainty that is very bad for the economic development. On the other hand, technology innovations drive changes in the customs and habits of people who can turn some laws into something void and useless, and sometimes a reformed legislation can be required. Governments must find a balance between legal certainty and reforms.

A totally connected world is not only a source of benefits but a source of threats too. In the same way as an open window can be an invitation to the thieves, and open connection is always a risk of being attacked by many kinds of delinquents. The need of security is every day growing, and not only to avoid robberies but anything. Any kind of crime can be executed or supported by the use of digital connections.

In addition, in a competitive world, digital connections can be used by the intelligence services of potentially enemy countries to get information, and the cyberspace can be used as a modern battlefield where is possible to make a great harmful impact with minimal resources and risks for the attackers. Modern warfare can be focused on different targets far from military ones. A state is not only the government but all its citizens. Chosen targets can be traditional as military facilities or critical infrastructure like power plants but can be more sophisticated as the sabotage of banks, stock exchange, or even the minds of people through fake news in an electoral process.

Nowadays, a cold war or even a hot one can be executed without sending spies to other countries. Connectivity provides full access to a lot of data that can be gathered and to control systems that let hackers to sabotage any activity.

In this scenario, with high connectivity, changing and complex technologies, and geopolitical unbalance, governments cannot be the only depositaries of the responsibility for a secure state. Private organizations and all the citizens must assume the risks and defend themselves from the threats and make a contribution to preserve their nations secure. The time for the compromise of military and police and the irresponsibility of the rest of the citizens has gone. Nowadays, the responsibility must be shared by all the citizens that are part of the states. People must forget to demand more rights and they must assume more duties.

Although this discourse can sound negative and it can produce fear about technologies, this is not the aim of this discussion. Technologies provide tools in order to avoid many of the risks that arise with connectivity too. With IT data security has been developed as a new science. It has evolved fast from 1975. Cryptography lets to preserve the privacy of data and to assure the authenticity of messages. Encrypted communications let that most of those risks commented previously can be avoided. Encryption acts as a door with a key that close the access for not desired people.

Current encryption and authorization techniques as PGP are used with trusted certificates that are provided by a certification authority, however, although many people do not know, PGP was developed to be used without a certification authority too. A certificate could be signed by several people providing high level of trust in function of the number of people and the trust that we have got in them. The scheme of secure messages can be provided without a central authority. This would let that people can establish their own trusted networks without the need of a regulator or a government. This is an example of how technology provides solutions to many of the risks of the current world without the need of great legislation changes if people are conscious of their responsibility on assuring data and messages. Fake news exists because people create networks sharing information but they do not create trusted ones. Fake news could be avoided if people only transmit original trusted messages instead of anything they receive.

The future will require new technologies to continue with this challenge. Encryption can be finally cracked, especially with faster computers that appear every year. The new paradigm in this field is post-quantum cryptography. Its aim is to produce cryptographic primitives that are secure against attacks using quantum computers.



Reductio ad absurdum

Poster of Modern Times. A movie by Charlie Chaplin. Photo Credit: Public Domain

In mathematics there is a common technique to demonstrate the falsity of hypotheses well known in logic as reductio ad absurdum.

Imagine that I consider that I have two functions named x and y with

x = f(t) and y =g(t).

If I suppose that x = y, this must imply that

f(t) = g(t) and then d f(t)/dt = d g(t)/dt.

If I discover from other means that

d f(t)/dt  ≠ d g(t)/dt

this would be an absurd and I can establish that the hypothesis

x = y is false.

Any person with some scientific education knows this logical method to determine the truth of hypotheses. Then, any person with some scientific education assuming that x = y and defending that d f(t)/dt ≠ d g(t)/dt is either a liar or an incompetent. Any time that we find an example of a point t where d f(t)/dt ≠ d g(t)/dt, we are demonstrating that x ≠ y.

This is too simple to be understood, however, there are people dedicated to management without mathematical and logical education that have strong prejudices to justify certain corporate policies but their managing actions against them usually demonstrate the falsehood of their prejudices and the futility of their policies.

Managing and leadership are not the ability to convince people about anything without considering the truth of what we are defending. Managing is related to the ability to use a limited set of resources in an effective and efficient way in order to reach some objective, and leadership is related to the ability to put a group of people to act in a coordinate way to reach that objective.

When effectiveness and efficiency can be put in a mathematical expression, managing is a matter of maximizing a mathematical function. The problem here is that although you can find a way to analyze in a mathematical way the managing functions, your managing actions are many related to people instead of robots and the behavior of people, in spite of what is said by many sociologists, is under a lot of uncertainty. We can program the behavior of a robot but we cannot program the behavior of a person in an organization. Current organizations are very far from those ones shown in the movie “Modern Times” by Charles Chaplin.

That is the reason why leadership is so important to manage any business. However, the concept of leadership is many times considered as something related to magical powers, but this is very far from the truth. A leader is not a leader by the beauty, by the education or by their past. A leader is a leader because his people act in coordinated way following his plan to get some aim. Leadership, as any other function in a business, is something that must be measured by the results. A military general is a great leader if his men can take the hill. A football manager is a great leader if his players can win the game. It is not important how the leader gets that his people follow his instructions: through fear, a harangue, or because he is an exemplary person and others want to become as him. The truly important thing is that results are got instead of how they are got. The how is important too, but thinking in the long term because, although there is not an army without sergeants shouting, fear used as the only managing tool finally destroys any organization. This is known by any military general. Exemplary leaders provide managing styles more sustainable than the use of fear or lies often.

Management in the AI age

Unmanned Aerial Vehicle. Photo Credit: U.S. Army.

The world is changing ever while management tries to preserve organizations under control. Management cannot stop the world. It must adapt its organizations to the changes. There is an important difference between to stop changes and to preserve control. We can go farther and we can try to take advantage of the changing nature of the world and drive the changes. Management will not be trying to put the organization status under control but the changes. This is the viewpoint that enables innovation as a management activity.

The incorporation of new technologies to the management activity is a current trend. Big data, machine learning and artificial intelligence seem to be useful technologies in order to improve the management activity. However, this fact requires thinking about it.

In the last decades, most management gurus were centered in people. We have listened a lot of times that managers should be thinking in people because organizations are a set of people working together. Managers should have strong competences in the analysis of personal conflicts and how to cope with them. The concept of emotional intelligence arose in order to describe this need. The use of automated techniques seems to be a totally different vision of the managing activity and a comeback to the classic intelligence as a way of management over the emotional one.

In fact this is true. Rules and standards that we usually find in every organizations put limits to the conflicts or, at least, they make easier to find a solution to those conflicts. A rationalist viewpoint to the management would establish the proper rules and standards to drive the activity of the staff avoiding any possible conflict among different managers and employees. When we have done this, we can automate any task with a computer or a robot.

If we turn the TV on to get the news, we can find that this assumption is far from the real world and it sounds like a fairy tale.

Complex organizations are working under uncertainty every day. This uncertainty makes the capability to regulate about all impossible. We cannot anticipate any possible situation, we cannot assure that regulation always will be followed by the staff in a perfect way, and we cannot assure that a new regulation to eliminate a conflict cannot produce other conflicts in the organization. It is well known that increasing rules and bureaucracy hugely produces less efficient organizations while conflicts are multiplied instead of producing better ones with fewer conflicts.

If this is true, why are we thinking that new technologies will be able to improve the management activity?

The reason is that those technologies are trying to support improvements in the information that managers have to make decisions both quantitative and qualitatively. Better information implies better decisions and better decisions made through reasoning imply less conflict. They are not trying to establish more rules and standards but better ones.

The important issue now is to assure that those technologies will provide these improvements of the decision making process in an effective way. We cannot consider this assumption true always because computer programs are complex systems themselves, and many of them can be error prone.

Future managers will need knowledge about the capabilities and limits of these technologies in order to define their management work, and they should not forget the fact that organizations will continue being a set of people working, although probably smaller than now, with a set of machines and tools to carry out a certain enterprise. The day that we find an organization without people, it will not be an organization really. It will be simply a complex robot. And robots do not need managers to operate, only engineers and technicians.

Mathematical Intelligence

K.I.T.T. AI driven vehicle from Knight Rider TV Series. Photo Credit: Wikimedia Commons

One of the main problems in AI can be the lack of a mathematical definition of intelligence. The concept of intelligence is not related to optimization. Optimization is usually an automatic process based on mathematical algorithms. With the development of artificial intelligence we have found a link between them, however, this must be researched a little more.

The very first attempts to introduce intelligence in control systems were done through logic algorithms. Logic can be expressed in mathematical functions and then, logical reasoning can be considered a mathematical one.

It is true that AI algorithms can be used to solve some problems of optimization: however, they cannot provide an exact solution in mathematical terms for many problems.

As artificial intelligence has evolved from automation, we usually look at intelligence as a process of automation. But if we look in deeper, we can find that the process of intelligence usually tries to escape from automation algorithms. AI in control systems has been mostly used to provide supervisory control. I am going to put an example in order to illustrate this concept.

Imagine a mobile robot or a car driven by AI. The optimal route from a point A to a point B is a straight line. We do not need AI in order to make that the device reaches the point B. AI is required when we cannot define the optimal route. If there is an obstacle in route of the mobile robot, it must change the automatic calculation in order to describe a new path. The intelligence of the device lets to escape from the optimal path because it can incorporate new information from the environment.

Intelligence is not the capability to solve formal problems only but the capability to adapt itself to different scenarios too. In this point, we can link intelligence with complexity. A complicated system has a lot of interconnected parts and its state is deterministic. A complex system has a lot of interconnected parts and its state have a certain degree of uncertainty.

To consider that intelligence is logical reasoning is to focus it on the support of the analysis of complicated systems; however, complex systems require more things than logical reasoning.

These problems arose in the early days of artificial intelligence. The answer for the analysis of uncertainty was the definition of new logic, known as fuzzy logic, that lets to provide a single solution for a problem where state variables are under uncertainty. Tools like experts systems were adapted to use of fuzzy logic.

Complex systems are characterized because they can change sharply their behavior under unexpected events. Intelligence offers solutions to those changes trying to preserve the system under control. In the example of the mobile robot, AI would calculate a new path to the point B after the detection of an unexpected obstacle in the way. In this case, AI is not optimizing the path but abandoning it after the unexpected event. The system is, then, under control because it is following a new path although it cannot follow the previously defined one. On the other hand, a probabilistic system would have calculated the initial path considering the probability of finding an obstacle in the way but this probability is impossible of knowing in most situations.

This concept can be very important when we are using big data to determine the behavior of a system. We use big data in order to define probabilities that can be incorporated to the algorithms. We are assuming that we can avoid any unexpected event increasing the amount of information, what is very vain as I am going to explain now.

When we play the dice, we assume that the probability of getting a certain result is 1/6 because we assume that it was perfect and homogeneously constructed from a physical viewpoint. If we accept that it was not perfectly built, we can use the big data. Once it has been thrown out many times, we can get different probabilities from the historic data that would adjust to its defects. But it does not provide information about a new dice. We cannot define a probability for the new dice, only the possible results without a probability for any of them. The result must be seen as uncertainty instead of risk.

Intelligence is a way to cope with complex systems and environments. Great geniuses of science have been people that have been able look at certain problems in a way far from the mean. Intelligence never has been a matter of statistics calculation but a matter of changing models of analysis. It has been related to the enlargement of the system domain to provide more general solutions and to apply it to a particular one. It is a matter of better identification of problems instead of simple logical or probabilistic reasoning. Perhaps, complexity analysis can provide a good starting point to produce new AI algorithms from a mathematical viewpoint.

Artificial Intelligence and moral

Humanoid Robot. Photo Credit: Public Domain

There are many people worried about AI however there are few people worried about human stupidity. This is a sign of the decadence of our societies, although there is a simple explanation. Stupidity is something natural that surrounds us from our birth. We have learnt to cope with stupidity however; many people are not trained to cope with intelligence.

I always say that stupidity is worse than evilness because the evil know their limits but the stupid do not. In terms of risk analysis evilness is under control, however, stupidity is uncontrollable. Following this concept, from a political viewpoint the establishment of high moral standards without the establishment of high education standards for people with power is something useless.

When common people think about AI, they are confusing intelligence with evilness. Evilness is a moral affair but intelligence is not. For the great philosophers like Kant reason and moral are intellectual abilities. Kant explains that there are many things that cannot be known through pure reason however we can made a lot of things like moral judgements because our nature need of them, although we cannot define them like absolute truths.

From an industrial viewpoint, AI is interesting in order to provide reason for a machine, in order that it can improve the effectiveness and the efficiency of any decision-making process. This is related to the Kantian concept of pure reason, and it is very far from the Kantian concept of moral.

Kant was the first philosopher that tried to establish the limits of reasoning. As reasoning has limits, it is possible to keep it under control and, AI is not related to moral because moral is out of the domain of reason.

In this point, detractors of AI would say that it is its problem but it is not the real problem. In the same way as human nature requires moral judgements that are not provided by the pure reason, we could program and provide moral judgements for AI devices out of the reasoning engine. Any AI would have the moral that we program for it. Again the problem about the moral of any AI is related to the moral judgement of their programmers. An example of this could be the three laws of robotics by Asimov. Moral establishes principles of behavior that are applied in front any decision of action in order to get a moral judgement to define the action as good or bad. But this would be out of any pure reasoning process. The problem that arises here is who has to define the moral for robotics because what is good for a person can be bad for another one.

This conceptual scheme is easily understood for the traditional implementation of AI under logic algorithms, however, it is more difficult to implement in other kind of AI based on neural nets, for instance. Conceptually, we would need to train the neural net to learn the operational function and to learn the moral one.

In the film “I, robot” Will Smith’s character hates robots because one of them decided through probabilities to save him instead of another person. The question here is: Would he hate humans if a man decided to save him because he was a public servant and the other person not, for instance, deciding through a political prejudice? Of course, he as a policeman was prepared in order to give his life to save other people and in both cases it would not be considered.