Arising restrictions of global economic growth due to limited natural resources and capacity of the biosphere adversely affect on people level of life and future expectation. That leads to mass depression and social instability. To consider this problem, psycho-physiological model of onward hedonist in consumer society is developed and investigated. This model is based on the fact that human nature generates a growing desire, needs to progress. After reaching the limits of growth, member of consumer society feel persistent negative emotions (...) and dissatisfaction. It results to socio-economic stagnation, social and international instability. The first type of possible solutions to this problem is based on the global expansion of the limits of growth (e.g., increasing of macroeconomic demand as a result of new Kondratieff technological cycle). The second type of solutions refers to local limits extension of growth within one or more countries, by means of their economic, political or military expansion in the world. But such solutions lead to international instability. The third type of solutions corresponds to periodical decreasing of the macroeconomic consumption followed by its growth (e.g., global financial crisis, decay of states and their unions, national and military conflicts, wars and so on). Such solution also leads to social and international instability. Humanistic solution consists in creativity development of people (especially middle class of developed countries) to focus on spiritual development. So rising desires of these people in spiritual realm can be satisfied under limits of global economic growth. (shrink)

The dependence of socio-political stability on the emotional expectations of voters is investigated. For this, a model of a socio-political system consisting of a society of voters and a democratically elected politician is considered. The neuropsychological model of the voter takes into account his emotional expectations. The social stability is guaranteed by the expectations of positive emotions of all voters. Socio-political stability means both the social stability and the re-election of politician. One type of voter is a Progressist who seeks (...) to fulfil his desires. It is shown that for the socio-political stability of the Progressist society, is enough a supportive environment. Another type of voter—a Phobic works in a fearful environment. It is shown that for the socio-political stability of the Phobic’s society, regular impacts that support their phobias are sufficient. As an example, emotional expectations, socio-political stability and information management in Eastern Europe are considered. The management of phobias in a weak economy is investigated. It is shown that in the absence of regular impacts supporting phobias a Phobic turns into a Progressist. Then the socio-political system becomes unstable with a weak economy and growth limits. The classification of the main social threats and phobias is given. The socio-political consequences of creating phobias, including corporate psychopaths and toxic leaders, are examined. Ways are proposed to resolve the contradiction between increased consumption and growth limits without the use of phobias, using high humanitarian technologies. (shrink)

This paper provides an early evaluation of Artificial Intelligence against COVID-19. The main areas where AI can contribute to the fight against COVID-19 are discussed. It is concluded that AI has not yet been impactful against COVID-19. Its use is hampered by a lack of data, and by too much data. Overcoming these constraints will require a careful balance between data privacy and public health, and rigorous human-AI interaction. It is unlikely that these will be addressed in time to be (...) of much help during the present pandemic. In the meantime, extensive gathering of diagnostic data on who is infectious will be essential to save lives, train AI, and limit economic damages. (shrink)

The increasing use of Artificial Intelligence for making decisions in public affairs has sparked a lively debate on the benefits and potential harms of self-learning technologies, ranging from the hopes of fully informed and objectively taken decisions to fear for the destruction of mankind. To prevent the negative outcomes and to achieve accountable systems, many have argued that we need to open up the “black box” of AI decision-making and make it more transparent. Whereas this debate has primarily focused on (...) how transparency can secure high-quality, fair, and reliable decisions, far less attention has been devoted to the role of transparency when it comes to how the general public come to perceive AI decision-making as legitimate and worthy of acceptance. Since relying on coercion is not only normatively problematic but also costly and highly inefficient, perceived legitimacy is fundamental to the democratic system. This paper discusses how transparency in and about AI decision-making can affect the public’s perception of the legitimacy of decisions and decision-makers and produce a framework for analyzing these questions. We argue that a limited form of transparency that focuses on providing justifications for decisions has the potential to provide sufficient ground for perceived legitimacy without producing the harms full transparency would bring. (shrink)

Decision-making assisted by algorithms developed by machine learning is increasingly determining our lives. Unfortunately, full opacity about the process is the norm. Would transparency contribute to restoring accountability for such systems as is often maintained? Several objections to full transparency are examined: the loss of privacy when datasets become public, the perverse effects of disclosure of the very algorithms themselves (“gaming the system” in particular), the potential loss of companies’ competitive edge, and the limited gains in answerability to be expected (...) since sophisticated algorithms usually are inherently opaque. It is concluded that, at least presently, full transparency for oversight bodies alone is the only feasible option; extending it to the public at large is normally not advisable. Moreover, it is argued that algorithmic decisions preferably should become more understandable; to that effect, the models of machine learning to be employed should either be interpreted ex post or be interpretable by design ex ante. (shrink)

Decision-making assisted by algorithms developed by machine learning is increasingly determining our lives. Unfortunately, full opacity about the process is the norm. Would transparency contribute to restoring accountability for such systems as is often maintained? Several objections to full transparency are examined: the loss of privacy when datasets become public, the perverse effects of disclosure of the very algorithms themselves, the potential loss of companies’ competitive edge, and the limited gains in answerability to be expected since sophisticated algorithms usually are (...) inherently opaque. It is concluded that, at least presently, full transparency for oversight bodies alone is the only feasible option; extending it to the public at large is normally not advisable. Moreover, it is argued that algorithmic decisions preferably should become more understandable; to that effect, the models of machine learning to be employed should either be interpreted ex post or be interpretable by design ex ante. (shrink)

Decision-making assisted by algorithms developed by machine learning is increasingly determining our lives. Unfortunately, full opacity about the process is the norm. Would transparency contribute to restoring accountability for such systems as is often maintained? Several objections to full transparency are examined: the loss of privacy when datasets become public, the perverse effects of disclosure of the very algorithms themselves, the potential loss of companies’ competitive edge, and the limited gains in answerability to be expected since sophisticated algorithms usually are (...) inherently opaque. It is concluded that, at least presently, full transparency for oversight bodies alone is the only feasible option; extending it to the public at large is normally not advisable. Moreover, it is argued that algorithmic decisions preferably should become more understandable; to that effect, the models of machine learning to be employed should either be interpreted ex post or be interpretable by design ex ante. (shrink)