In early 2018, I was at the Organization Science Winter Conference when a friend of mine suggested I submit to the Collective Intelligence Conference which would be held that year in Zurich. I figured why not, sent part of my dissertation that we had been revising (and still are) and was accepted to the conference. Though it was an exciting trip (storms led me to arrive in Zurich about an hour before the conference started) I had a positive overall experience. It was held that year in combination with HCOMP (Human Computation) so there was a machine learning / “computers as cognition” flare to the conference. As part of a grant, i was exposed to this type of work before and found it interesting. One thing that was evident there and at the same conference this year (held at my alma mater Carnegie Mellon University), is that Collective Intelligence (CI) is many things to many people.
I was expecting this year to be heavy on machine-learning, simulations, and micro-tasking as last year’s conference was. Instead, there was a strong focus on “ghost workers” (the workers on micro-tasks), the rise of AI, and detailing the parallel progress on problems of collective organization that many fields have investigated. Though there was still a computer science flair to the conference, I felt that much of the work was traditional social science focused. When some of the presenters commented on the crowd being programmers (as opposed to management/psych/soc scholars) I felt that there was probably less the case this year as compared to last.
So what is Collective Intelligence? A minority of research presented was on the ‘Collective Intelligence Quotient’ a score that teams receive that captures to extent to which they have developed “Collective Intelligence.” I honestly thought this concept was what CI would be about (that groups with high CI do better than we would expect) though this was proved wrong at Zurich. This work was pioneered in large part by Anita Woolley (who was on my thesis committee) who presented some of her new work and some posters from her and others presented some related research on the same topic. Most of the work presented, however, was on collective actions or collective aggregation writ large. Thus, a discussion of Uber drivers or Amazon Mechanical Turk workers as a collective, brought together by a system, to accomplish work can still be thought of as demonstrating intelligent work done by a group. Thus CI (the conference) is in large part about the most effective (in terms of performance and social responsibility) ways to bring people together to create positive outcomes. Like INGRoup, the focus of the conference is about these ideas as opposed to process or outcomes, which I think is great.
But, I left this year’s conference feeling a bit unsatisfied. The majority of the conference was composed of plenary sessions where stars talked about big ideas. I frequently felt like these sessions were intended to motivate the audience to see the world or its problems in a particular way as opposed to presenting research.This left me with the flavor that we were the congregation and we were being preached at with equal parts motivation (about the new problems the world faces that we can solve) and despair (that only policy makers could address the main issues). This isn’t inherently bad but it felt much different from last year. There was still interesting work discussed and I got to see many professors and colleagues I hadn’t seen in a while (and make some new connection) which was great.
My comment above also isn’t intended to be critical of the conference’s presenters or organizers, but merely a statement of my own feelings born in large part from my personal career position. There was very little room for someone like me as a worker (a teacher whose job will soon be automated) or me as a researcher (who is not looking at the problems of the future yet and using dated tools) in my current position and research stream. This in large part left me feeling like I need to be faster, pivot harder, and improve more, which isn’t a bad thing. But some of the work presented also seemed to present re-discovery of existing work as novel or the re-naming of existing constructs in sexy ways for no evident purpose. This evident disconnect was perplexing and could in part have been driven from my misunderstanding of presenters (time allocations were very short). So, I am left with very complicated feelings about myself and the conference. Not inherently bad, but perplexing.