Algorithmic fairness in recommender systems requires close attention to the needs of a diverse set of stakeholders that may have competing interests. Previous work in this area has often been limited by fixed, single-objective definitions of fairness, built into algorithms or optimization criteria that are applied to a single fairness dimension or, at most, applied identically across dimensions. These narrow conceptualizations limit the ability to adapt fairness-aware solutions to the wide range of stakeholder needs and fairness definitions that arise in practice. Our work approaches recommendation fairness from the standpoint of computational social choice, using a multi-agent framework. In this paper, we explore the properties of different social choice mechanisms and demonstrate the successful integration of multiple, heterogeneous fairness definitions across multiple data sets.

Peer-reviews were found as a useful educational activity stimulating social learning but, then again, increasing the teacher's workload. In our paper we investigated the peer-review outputs with the aim to find out the ways of replacing the teacher's evaluation with students reviews. Although the findings are encouraging, an adjustment of the peer-review format and criteria could possibly improve the results.

In open environments, agents need to reason with knowledge from various sources, represented in different languages. Multi-Context Systems (MCSs) allow for the integration of knowledge from different heterogeneous sources in an effective and modular way. Whereas most knowledge bases (contexts) typically considered within an MCS are written in some description logic or some non-monotonic rule-based language, sometimes more expressive languages that combine the features of both these paradigms are necessary, such as Hybrid MKNF. However, since agents may not have access to specialized reasoners for contexts using all these languages, it proves useful to have tools that equivalently simplify or transform a given MCS into another MCS that only uses the reasoners that are available. In this article, we thoroughly investigate the relation between MCSs and Hybrid MKNF. We provide a number of transformations that show that Hybrid MKNF knowledge bases can be embedded into MCSs without the need for specific MKNF reasoners. To complete the picture, we also show that when an MKNF reasoner is available, it can be used to handle several description logic and rule contexts joined into a single MKNF context. Furthermore, we show that we can encapsulate the non-monotonic transfer of information between different contexts in one rule language context, allowing e.g. the use of external non-monotonic reasoners.

The concerns of efficient data management and logical inference on the Semantic Web often lead to disconnection between the surface structure of RDF/OWL data/ontologies and the background state of affairs. The PURO ontology background model language allows to explicitly capture the mapping between the foreground and background modeling layers. The background modeling primitives are intentionally kept analogous to those of RDF/OWL, namely, derived from the particular-universal and relationship-object distinctions. We project the PURO framework onto the W3C CPV family of structural design patterns, thus providing additional insights into them and possibly facilitating their selection and reuse.