1.2 Goal of the book
As stated above, digital technology breakthrough and artificial intelligence in particular can help to address many of the world’s biggest challenges. The pace of technological progress that is now being developed across the world is incredibly rapid. At the same time AI itself brings new challenges and raises serious legal and ethical questions.6 This phenomenon is not new. Usually, legal concepts and norms had to adjust to the novelty challenges posed by the progress in the sphere of science, culture, politics, economy. It is not different with the current technological changes.7
This phenomenon is being seen from various angles. Eventually it is about locating the phenomenon of algorithmic changes within the societies in the governance and regulatory environment. The regulation can be seen as polycentric social system with six elements, creating its dynamics: goals and values, knowledge and understanding, tools and techniques, behaviours of individuals, behaviours of organisations and trust and legitimacy.8 Regulatory environment could be defined as organised attempts to manage risks or behaviour to achieve a publicly stated objective or set of objectives.9 According to this theory there are two main forms of regulation: command and control regulation and design-based regulation. The first form of regulation refers to the use of legal or regulatory rules that dictate behaviour. They come with punishment and incentive mechanism. In reaction to them, on the side of the addressee of these norms is an arbitrage to comply for the reward or to ignore and risk punitive consequences.10 The second form of regulation, which is design-based, is to create regulatory standards adjusted to the design of the entire regulated system. In other words, it is based on constructing an architecture adapted to human behaviour that matches the preferred behaviours.11
According to this concept much of the present algorithmic governance and regulatory framework constitutes a design-based sort of regulation.12 In line with this theory, a design-based regulation and algorithmic decision support system is a type of nudging. Nudging is a regulatory philosophy that has its origins in behavioural economics based on the assumption deriving from cognitive psychology claiming that people are less rational than universally believed. They display biases and psychologically preferred stereotypes.13 That sometimes deviates them from the expectations of rational choices theory and causes damages to their own long-term well-being. The best example of this is shown in the general tendency to over prioritise the short-term future. People discount the value of future events too much that is instead of doing it according to an exponential according to a hyperbolic curve. They favour sooner rewards even though they are smaller rather than larger ones if they ought to come later.14
The same concept can also apply to regulatory domains. Legislators and regulators may create a kind of decision-making situation building so called choice architectures that benefits from the nature of human psychology and nudge them into preferred behavioural patterns.15 This approach has gained a lot of popularity only recently. The public authorities have started to set up behavioural analysis units to implement nudge-based policy settings in multiple areas.16
Therefore, nudging is a type of design-based regulation because it is not about enforcing the created rules and regulations but about handwriting policy preferences into behavioural architectures. The algorithmic governance systems work like nudges especially within decision-support systems. These are forms of algorithmic governance which use data-mining techniques to present choice options. People typically do not question the defaults provided by our algorithmic systems they use on a daily basis. This same mechanism might be used as a support regulatory framework where algorithmic decision support systems are used in many policy domains.17
Against this theoretical background, we would like to draw the goal of our book, which is to outline the general regulatory approach of the EU towards algorithmic reality. Ethics is the central notion around which all regulatory steps are revolving. And regulation of what is supposed to be an ethical AI takes various forms. It is both commands based, and design based. It combines proper centralised legislative measures with decentred regulation resting in hands of interested stakeholders. Thus, there is a complex network of top-down measures and bottom-up initiatives, binding and non-binding rules, hard and soft laws, horizontal and sectoral rules, supranational, international, national and industry-based regulations. Such a complex, intertwined regulatory environme...