Science Facing Interoperability as a Necessary Condition of Success and Evil
It addresses ethical challenges in AI for scientists and policymakers, highlighting a trade-off between progress and tyranny, but is incremental as it builds on existing critiques of AI ethics.
The paper examines how AI-driven interoperability enables new knowledge by connecting previously isolated data, but also creates ethical issues like bias and injustice by merging distinct social spheres.
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.