The big T word and why it matters more than ever
Imagine entering a room where a philosopher, a computer scientist, and a sociologist are engaged in a discussion about AI ethics. The philosopher is pacing back and forth, grappling with questions of whether machines can actually make moral choices. The computer scientist is frantically writing algorithms on a whiteboard, grumbling about "bias mitigation protocols." Meanwhile, the sociologist is gesturing towards charts illustrating how AI could concentrate power in the hands of a small number of tech giants.
They are all knowledgeable, and they are all right. However, they are all speaking completely different languages.
This too is what traditional academic collaboration typically amounts to, with each expert summarizing their insight in terms the others can understand, much like diplomats at a UN conference wearing headsets, waiting for their verbal cue. It works, but something gets lost in translation every time.
Transdisciplinarity is different. Instead of translation, it is fusion. Imagine those same three experts suddenly discovering that they are not studying different problems, but different manifestations of the same problem. The computer scientist's algorithms are not technical tools on their own—they are ethical agents who bring philosophical ideals to life and rebuild social structures. The philosopher's normative theories are not abstract constructs—they need to be coded into functioning systems that exist in actual communities. The sociologist's power dynamics are not separate from the code—actually, they are woven into how those systems function.
When working transdisciplinarily, something new is created that any of them could not have developed on their own. They develop approaches that are simultaneously computational, ethical, and socially aware. Not because they have been negotiated across the disciplines, but because they have transcended the artificial boundaries between them. Beyond this, they are developing the metacognitive, critically reflective abilities to move fluidly between different knowledge frameworks while maintaining intellectual honesty about their strengths, limitations, and appropriate applications.
This discussion has become more important than ever because AI ethics, and global problems such as sustainability, public health emergencies, or urban injustice, are not interested in the faculty divisions that are traditionally so prized by academia and other institutions. These are messy, complex, and thoroughly real-world problems. Addressing them requires thinking and practices that are equally complex and equally real. It is the difference between an orchestra where every section has their part to play separately and one where the music itself arises out of their genuine collaboration.
Photo ‘Orchestra in Concert’ by Manuel Nägeli on Unsplash