On the Importance of Disciplinary Pride for Multidisciplinary Collaboration
I am a big fan of collaborations, even if they come with their own challenges. I always got further and enjoyed research much more because of my collaborators. I’m forever indebted to so many colleagues and dear, dear friends. Each and every one of them was better than me in some ways. To contribute, I had to remember my own strengths and bring them to the table. The premise of this post is that the same holds for collaboration between fields. It should be read as a call for theoreticians to bring the tools and the powerful way of thinking of TOC into collaborations. We shouldn’t be blind to the limitation of our field but obsessing on those limitations is misguided and would only limit our impact. Instead we should bring our best and trust on the other disciplines we collaborate with to do the same (allowing each to complement and compensate for the other).
The context in which these thoughts came to my mind is Algorithmic Fairness. In this and other areas on the interface between society and computing, true collaboration is vital. Not surprisingly, attending multidisciplinary programs on Algorithm Fairness, is a major part of my professional activities these days. And I love it – I get to learn so much from people and disciplines that have been thinking about fairness for many decades and centuries. In addition, the Humanities are simply splendid. Multidisciplinary collaborations come with even more challenges than other collaborations: the language, tools and perspectives are different. But for exactly the same reasons they can be even more rewarding. Nevertheless, my fear and the reason for this post is that my less experienced TOC colleagues might come out from those interdisciplinary meetings frustrated and might lose confidence in what TOC can contribute. It feels to me that old lessons about the value of TOC need to be learned again. There is a lot to be proud of, and holding to this pride would in fact make us better collaborators not worse.
In the context of Algorithmic Fairness, we should definitely acknowledge (as we often do) that science exists within political structures, that algorithms are not objective and that mathematical definitions cannot replace social norms as expressed by policy makers. But let’s not take these as excuses for inaction and let’s not withdraw to the role of spectators. In this era of algorithms, other disciplines need us just as much as we need them .