Faculty Boundaries
Faculty boundaries are the organizational dividing lines between the major disciplines of a university, such as between computer science, medicine, and law. In AI research, they are seen as an obstacle because many important questions can only be answered jointly by several disciplines.
A university is divided into large divisions. These divisions are called faculties. There is, for example, a faculty of medicine, one of law, and one of computer science. Each faculty has its own budget, its own professorships, and its own examination regulations. The dividing lines between these divisions are called faculty boundaries. When researchers from different faculties want to work together, they have to overcome these boundaries — and that is often more laborious than it sounds.
Why AI research fails at these dividing lines
Artificial intelligence is no longer a purely computer-science topic. A program that evaluates X-ray images needs doctors to assess whether the diagnoses are correct. A language model that reviews contracts needs lawyers. And anyone who wants to know how automated recommendations affect elections needs sociologists and psychologists. The interesting questions therefore almost always lie between the disciplines.
Universities, however, are built on a logic that dates back to the 19th century. Back then, it was clearly defined who dealt with what. Today, this means that a computer scientist and a physician can work at the same university without ever hearing of each other. Their institutes are sometimes spread across different parts of the city.
This is relevant for the economy because innovations frequently arise precisely at these interfaces. If a university does not enable such projects, they migrate to companies instead. Large technology corporations deliberately assemble mixed teams and advertise this to applicants.
What makes these boundaries so persistent in everyday life
The hurdles are rarely due to ill will. They are embedded in the administration. If a project receives funding, it must be clarified which faculty books it. If a doctoral candidate is being supervised, it must be determined which faculty awards her degree. For joint projects, the appropriate forms are often simply missing.
Then there is the question of what counts as good work. In computer science, contributions to major conferences count. In medicine, journals with high prestige count. Those who work between the fields often publish in places that nobody in their own discipline knows. This can slow down one’s own career, even though the research is good.
You can picture this like two neighboring countries with different currencies. Both are prosperous, but every exchange costs fees and time. This is exactly why many universities today set up their own centers that are deliberately not assigned to any single faculty. They receive their own budget and their own positions and function like a neutral zone.
Where the term appears in reports
You mostly encounter this term in press releases from universities and in science policy. A typical phrasing is that a new AI center works across faculties. This means that people from several disciplines are jointly employed there. Federal funding programs, too, now often explicitly require such mixed applications.
A second place is debates about AI regulation. Rules for artificial intelligence require technical knowledge and legal knowledge at the same time. If the two remain separate, laws emerge that are technically unworkable, or technology emerges that is not legally permitted. This is exactly what the criticism of rigid faculty boundaries targets.
A common misconception is to equate the term with interdisciplinarity. Interdisciplinarity describes the substantive collaboration between several disciplines. Faculty boundaries, on the other hand, describe the organizational structure that makes this collaboration harder or easier. One is the goal, the other is the obstacle on the way there.