Science Facing Interoperability as a Necessary Condition of Success and Evil

TL;DR

The paper discusses how AI-driven interoperability of systems is both essential for scientific progress and a source of ethical challenges, such as bias and social dominance, requiring careful consideration.

Contribution

It highlights the dual nature of interoperability as a necessary condition for scientific advancement and a potential ethical risk in AI systems.

Findings

Interoperability enables new scientific connections and progress.

It can lead to bias reproduction and social dominance.

Interoperability poses ethical challenges in AI systems.

Abstract

Artificial intelligence (AI) systems, such as machine learning algorithms, have allowed scientists, marketers and governments to shed light on correlations that remained invisible until now. Beforehand, the dots that we had to connect in order to imagine a new knowledge were either too numerous, too sparse or not even detected. Sometimes, the information was not stored in the same data lake or format and was not able to communicate. But in creating new bridges with AI, many problems appeared such as bias reproduction, unfair inferences or mass surveillance. Our aim is to show that, on one hand, the AI's deep ethical problem lays essentially in these new connections made possible by systems interoperability. In connecting the spheres of our life, these systems undermine the notion of justice particular to each of them, because the new interactions create dominances of social goods from a sphere to another. These systems make therefore spheres permeable to one another and, in doing so, they open to progress as well as to tyranny. On another hand, however, we would like to emphasize that the act to connect what used to seem a priori disjoint is a necessary move of knowledge and scientific progress.