Within current debates about the future impact of Artificial Intelligence on human society, roughly three different perspectives can be recognised: the technology-centric perspective, claiming that AI will soon outperform humankind in all areas, and that the primary threat for humankind is superintelligence; the human-centric perspective, claiming that humans will always remain superior to AI when it comes to social and societal aspects, and that the main threat of AI is that humankind’s social nature is overlooked in technological designs; and the (...) collective intelligence-centric perspective, claiming that true intelligence lies in the collective of intelligent agents, both human and artificial, and that the main threat for humankind is that technological designs create problems at the collective, systemic level that are hard to oversee and control. The current paper offers the following contributions: a clear description for each of the three perspectives, along with their history and background; an analysis and interpretation of current applications of AI in human society according to each of the three perspectives, thereby disentangling miscommunication in the debate concerning threats of AI; and a new integrated and comprehensive research design framework that addresses all aspects of the above three perspectives, and includes principles that support developers to reflect and anticipate upon potential effects of AI in society. (shrink)

In this article, we critique the ways in which the people involved in the development and application of AI systems often visualize and talk about AI systems. Often, they visualize such systems as shiny humanoid robots or as free-floating electronic brains. Such images convey misleading messages; as if AI works independently of people and can reason in ways superior to people. Instead, we propose to visualize AI systems as parts of larger, sociotechnical systems. Here, we can learn, for example, from (...) cybernetics. Similarly, we propose that the people involved in the design and deployment of an algorithm would need to extend their conversations beyond the four boxes of the _Error Matrix_, for example, to critically discuss _false positives_ and _false negatives_. We present two thought experiments, with one practical example in each. We propose to understand, visualize, and talk about AI systems in relation to a larger, complex reality; this is the requirement of _requisite variety_. We also propose to enable people from diverse disciplines to collaborate around _boundary objects_, for example: a drawing of an AI system in its sociotechnical context; or an ‘extended’ Error Matrix. Such interventions can promote meaningful human control, transparency, and fairness in the design and deployment of AI systems. (shrink)

In open heterogeneous multi-agent systems, communication is hampered by lack of common ontologies. Ontologies may differ in naming conventions, granularity and scope. In such an environment, the agents must possess the right conversational skills to effectively exchange information even when the speaker's ontology is only approximately translatable to the hearer's ontology. Furthermore, the agents must be able to autonomously establish an ontology translation by exchanging parts of their ontologies. In this paper, we propose a layered communication protocol in which the (...) agents gradually build towards a semantically integrated system by establishing minimal and effective common ontologies. We tested our system, called ANEMONE, on a number of heterogeneous news agents. We show how these agents successfully exchange information on news articles, despite initial difficulties caused by heterogeneous ontologies. (shrink)