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Mechanism of Citizen Evaluation of Policy Using Machine Self-Learning

机译：采用机器自学评估政策的公民机制

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Public policy is designed to influence society. In turn, members of society - citizens should evaluate this policy so that society can control it. According to the concept of digital society, policy evaluation is based on artificial intelligence solutions, primarily machine learning. In addition, when monitoring politics by society, it is necessary to take into account the human factor. The article discusses theoretical and practical issues that arise when a citizen applies the machine learning procedure to determine alternative evaluations - ratings of a politician. In conditions of uncertainty, it is assumed that this politician knows his ability to meet the needs of society better than the citizen. Using this knowledge, the politician can manipulate own activities in order to get higher ratings today and in the future. Such undesirable activity can lead to the failure to use the available opportunities in which the citizen and society as a whole are interested. To solve this problem in conditions of uncertainty, a mechanism for citizen evaluation of politician is proposed. This mechanism includes the procedure of machine learning of dichotomy and the formation of alternative ratings of a politician. Sufficient conditions have been found for the synthesis of such a mechanism in which a politician fully uses the existing opportunities in the interests of the citizen and society as a whole. The functioning of this mechanism is illustrated by the example of an evaluation of the national policy on vaccination against COVID-19 in the UK. Such a mechanism encourages the politician to use all available opportunities in the public interest. The developed mechanism can be used by any citizen for permanent evaluation of policy using machine self-learning. For this, for example, such mechanism can be implemented as an application on a smart phone.
机译：公共政策旨在影响社会。反过来，社会成员 - 公民应该评估这项政策，以便社会可以控制它。根据数字社会的概念，政策评估基于人工智能解决方案，主要是机器学习。此外，在通过社会监测政治时，有必要考虑人类因素。本文讨论了公民申请机器学习程序时出现的理论和实际问题，以确定替代评估 - 政治家的评级。在不确定的条件下，假设这位政治家知道他满足社会需求的能力比公民更好。使用这种知识，政治家可以操纵自己的活动，以便今天和将来获得更高的评级。此类不良活动可能导致未能使用公民和整个社会的可用机会。为了解决不确定性条件下解决这个问题，提出了对政治家的公民评估的机制。这种机制包括二分法的机器学习程序以及政治家的替代评级的形成。已经找到了综合这种机制的充分条件，其中政治家充分利用现有机会的公民和整个社会的利益。该机制的运作是通过对英国Covid-19疫苗接种政策的评估的例子来说明的。这种机制鼓励政治家使用公共利益的所有可用机会。开发机制可以由任何公民使用，用于使用机器自学评估政策。例如，例如，这种机制可以实现为智能手机上的应用程序。

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