Abstract
The main aim of the paper was to estimate the impact of older employees aged 55-74 on productivity compared with other age groups. The research (foundation of the paper) was conducted in 72 countries in the period 2000-2021. Countries were divided into three groups composed of 24 economies according to their GDP pc in 2021, i.e. the lowest GDP pc, the middle GDP pc, and the highest GDP pc countries. The study covered the five age and sex groups of employees: 25-34, 35-44, 45-54, 55-64, and 65-74. The Productive Capacities Index (PCI) built by UNCTAD and its selected categories were assumed to be dependent variables. The research results obtained for three groups of countries indicate that older employees had stronger positive impact on improving PCI than younger employees, especially on human capital development and private sector productivity. Our outcomes also suggest that older employees are better integrated than younger age groups with ICT tools that increase work productivity. To sum up, we can state that older employees can be a key factor in economic development due to their knowledge and experience, provided that others are willing to learn from them and they receive relevant organizational support.

Abstract
This study investigates the evolutionary impact of applying artificial intelligence (AI) technology to foreign language education. By employing complex systems thinking, the relationship between foreign language education and AI technology is explored, and dynamic models are employed to analyze the evolutionary patterns of AI technology in foreign language education. Through model analysis and numerical simulations, the interactive effects between foreign language education and AI technology in different modes are revealed. The findings demonstrate that, under different coupling modes, foreign language education and AI technology can achieve self-organizing evolution. When the interaction coefficient between foreign language education and AI technology is appropriately set, AI technology exhibits emergent properties for foreign language education. Lastly, suggestions are presented to promote the sound development of foreign language education and AI technology.

Abstract
In recent years, the EU has been pushing forward ground-breaking legislation that covers new digital environments and services, with a strong focus on Ethics and AI. This includes legislation such as the Artificial Intelligence Act, the Digital Services Act or the General Data Protection Regulation. This legislation is, however, often written in very general and high-level terms, leaving a lot of space for interpretation, and a gap concerning how it could or should be implemented, realistically. In this paper we look specifically at the principle of Transparency in the Digital Services Act. Specifically, we discuss the requirements concerning Transparency in the regulation, we identify the gaps, and propose concrete measures that can be considered to facilitate and guide its implementation.

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Abstract

Abstract
In recent years, several regulatory initiatives have been carried out at the European Commission level to ensure the ethical use of Artificial Intelligence, including the General Data Protection Regulation, Data Governance Act, or the Artificial Intelligence Act. However, there is also a need for technological solutions that effectively enable the implementation of this regulation in a realistic and efficient way. The main goal of this work is to propose and implement such a technological solution, relying on the notion of observability. The hypothesis is that a set of ethics metrics can be implemented along a domain-agnostic Data Science/Artificial Intelligence pipeline. These metrics, when observed in real time, will allow not only to assess the level of compliance of the pipeline with ethics standards at different levels, but also allow for a timely reaction by the organization when the data, the model or any other artifact in the pipeline exhibits undesired behavior. In this way, some of the most important ethical principles of AI are guaranteed: responsibility and prevention of harm. This work aims to identify a large group of ethics metrics, implement them, map them onto the different stages of a typical Data Science / AI process, and determine whether the presence of these metrics ensures or contributes to the development of AI solutions that can be considered ethical according to the latest European regulation.