We introduce Lovelace, a tool for creating corpora of semantic graphs. The system uses graph expansion grammar as a representational language, thus allowing users to craft a grammar that describes a corpus with desired properties. When given such grammar as input, the system generates a set of output graphs that are well-formed according to the grammar, i.e., a graph bank. The generation process can be controlled via a number of configurable parameters that allow the user to, for example, specify a range of desired output graph sizes. Central use cases are the creation of synthetic data to augment existing corpora, and as a pedagogical tool for teaching formal language theory.

Fairness is central to the ethical and responsible development and use of AI systems, with a large number of frameworks and formal notions of algorithmic fairness being available. However, many of the fairness solutions proposed revolve around technical considerations and not the needs of and consequences for the most impacted communities. We therefore want to take the focus away from definitions and allow for the inclusion of societal and relational aspects to represent how the effects of AI systems impact and are experienced by individuals and social groups. In this paper, we do this by means of proposing the ACROCPoLis framework to represent allocation processes with a modeling emphasis on fairness aspects. The framework provides a shared vocabulary in which the factors relevant to fairness assessments for different situations and procedures are made explicit, as well as their interrelationships. This enables us to compare analogous situations, to highlight the differences in dissimilar situations, and to capture differing interpretations of the same situation by different stakeholders. CCS Concepts: Computer systems organization Embedded systems; Redundancy; Robotics; Networks Network reliability. INTRODUCTION Fairness is a fundamental aspect of justice, and central to a democratic society [50]. It is therefore unsurprising that justice and fairness are at the core of current discussions about the ethics of the development and use of AI systems. Given that people often associate fairness with consistency and accuracy, the idea that our decisions as well as the decisions affecting us can become fairer by replacing human judgment with automated, numerical systems, is appealing [1, 16, 24]. All authors contributed equally to this research. Authors listed alphabetically Authors' addresses: Andrea Aler Tubella, andrea.aler@umu.se, Nevertheless, current research and journalistic investigations have identified issues with discrimination, bias and lack of fairness in a variety of AI applications [41].