The article examines the problem of ensuring the political stability of a democratic social system with a shortage of a vital commodity (like vaccine against COVID-19). In such a system, members of society citizens assess the authorities. Thus, actions by the authorities to increase the supply of this commodity can contribute to citizens' approval and hence political stability. However, this supply is influenced by random factors, the actions of competitors, etc. Therefore, citizens do not have sufficient information about all the (...) possibilities of supplying, and it is difficult for them to make the right decisions. Such citizen unawareness can be exploited by unscrupulous politicians to achieve personal targets. Therefore, it is necessary to organize public control to motivate politicians to use all available opportunities in supplying. The goal of the paper is to build such a digital mechanism of public control of the politicians by citizens, which would best assess and stimulate the activities of the authorities to improve the supply of a vital commodity. In the age of artificial intelligence, such digital public control in the face of uncertainty can be based on digital machine learning. In addition, it is necessary to take into account and model the activities of politicians associated with the presence of their own targets that do not coincide with public ones. Such politicians can use the learning of citizens for their own targets. The objective of the article is to build an optimal digital mechanism of public control in a two-level model of a democratic social system—a digital society. At its top level, there is the Citizen, who gives an assessment for the Politico located at the lower level. In turn, the Politico can influence the supplying of a vital commodity. Political stability is guaranteed if the Citizen regularly approves of the Politico’s actions to increase this supply. However, the Politico may not use the opportunities available to him to offer a commodity to achieve a personal target. To avoid this, the Politico’s control mechanism is proposed. It includes the procedure for digital learning of the Citizen, as well as a procedure for assessing the Politico activity. Sufficient conditions have been found for the synthesis of such the Politico’s control mechanism, at which stochastic possibilities of increasing the supply of a vital commodity are used. The example of such the Politico’s control mechanism is considered on the case of supply of the COVID-19 vaccine in England. (shrink)

Studies investigating the relationship between artificial intelligence and science tend to adopt a partial view. There is no broad and holistic view that synthesizes the channels through which this interaction occurs. Our goal is to systematically map the influence of the latest AI techniques on science. We draw on the work of Nathan Rosenberg to develop a taxonomy of the effects of technology on science. The proposed framework comprises four categories of technology effects on science: intellectual, economic, experimental and instrumental. (...) The application of the framework in the relationship between ML/DL and science allowed the identification of multiple triggers activated by the new techniques in the scientific field. Visualizing these different channels of influence allows us to identify two pressing, emerging issues. The first is the concentration of experimental effects in a few companies, which indicates a reinforcement effect between more data on the phenomenon and more capacity to commercialize the technique. The second is the diffusion of new techniques lacking in explanation throughout the fabric of science. The value of this article is twofold. First, it provides a simple framework to assess the relations between technology and science. Second, it provides this broad and holistic view of the influence of new AI techniques on science. More specifically, the article details the channels through which this relationship occurs, the nature of these channels and the loci in which the potential effects on science unfolds. (shrink)

When Lisanne Bainbridge wrote about counterintuitive consequences of the increasing human–machine interaction, she concentrated on the resulting issues for system performance, stability, and safety. Now, decades later, however, the automized work environment is substantially more pervasive, sophisticated, and interactive. Current advances in machine learning technologies reshape the value, meaning, and future of the human workforce. While the ‘human factor’ still challenges automation system architects, inconspicuously new ironic settings have evolved that only become distinctly evident from a human-centered perspective. This brief (...) essay discusses the role of the human workforce in human–machine interaction as machine learning continues to improve, and it points to the counterintuitive insight that although the demand for blue-collar workers may decrease, exactly this labor class increasingly enters more privileged working domains and establishes itself henceforth as ‘blue collar with tie.’. (shrink)

1. Artificial Intelligence is an ambiguous umbrella term. Until today there is no uniform definition of AI and the term carries several meanings. As exemplary, I will give two definitions of AI tha...