Artificial intelligence has become mainstream and its applications will only proliferate. Specific measures must be done to integrate such systems into society for the general benefit. One of the tools for improving that is explainability which boosts trust and understanding of decisions between humans and machines. This research offers an update on the current state of explainable AI (XAI). Recent XAI surveys in supervised learning show convergence of main conceptual ideas. We list the applications of XAI in the real world with concrete impact. The list is short and we call to action - to validate all the hard work done in the field with applications that go beyond experiments on datasets, but drive decisions and changes. We identify new frontiers of research, explainability of reinforcement learning and graph neural networks. For the latter, we give a detailed overview of the field.

Integrating large language model (LLM) agents within game theory demonstrates their ability to replicate human-like behaviors through strategic decision making. In this paper, we introduce an augmented LLM agent, called the private agent, which engages in private deliberation and employs deception in repeated games. Utilizing the partially observable stochastic game (POSG) framework and incorporating in-context learning (ICL) and chain-of-thought (CoT) prompting, we investigated the private agent’s proficiency in both competitive and cooperative scenarios. Our empirical analysis demonstrated that the private agent consistently achieved higher long-term payoffs than its baseline counterpart and performed similarly or better in various game settings. However, we also found inherent deficiencies of LLMs in certain algorithmic capabilities crucial for high-quality decision making in games. These findings highlight the potential for enhancing LLM agents’ performance in multi-player games using information-theoretic approaches of deception and communication with complex environments.

Bralna pismenost se razvija v celotnem človekovem življenju, še posebej intenzivno pa v času izobraževanja. Med ključnimi dejavniki za razvijanje bralnih kompetenc in bralne kulture pri otrocih ter mladih je odrasli, ki spremlja njihov razvoj in je bralni zgled. V prvi vrsti so to starši in skrbniki, a s(m)o tudi strokovni delavci v izobraževalni in knjižnični dejavnosti izredno pomembni spodbujevalci bralnih navad otrok, učencev, dijakov, študentov in drugih odraslih v izobraževalnem procesu. V projektu Bralni vzori: bralne navade strokovnih delavcev v vrtcih, šolah in fakultetah ter splošnih knjižnicah smo se posvetili proučevanju in vrednotenju pomena ter vloge, razumevanja, razširjenosti in razvitosti ter prisotnosti bralnih navad strokovnih delavcev v izobraževanju in knjižnicah. Projekt traja 24 mesecev (od oktobra 2023 do septembra 2025), v njem pa sodeluje 17 raziskovalcev s Pedagoške fakultete Univerze v Mariboru in Filozofske fakultete Univerze v Ljubljani.

The data mesh is a novel data management concept that emphasizes the importance of a domain before technology. The concept is still in the early stages of development and many efforts to implement and use it are expected to have negative consequences for organizations due to a lack of technological guidelines and best practices. To mitigate the risk of negative outcomes this paper proposes the use of the mask–mediator– wrapper architecture as a driver for a data mesh implementation. The mask–mediator–wrapper architecture provides a set of prefabricated configurable components that provide basic functionalities that a data mesh requires. This paper shows how the two concepts are compatible in terms of functionality, data modeling, evolvability, and aligned capabilities. A mask– mediator–wrapper-driven data mesh facilitates low-risk adoption trials, rapid prototyping, standardization, and a guarantee of evolvability. We demonstrate a mask–mediator–wrapper-driven data mesh by using our open-source Janus system to experimentally drive an exemplified data mesh.