I’m not normally one to be spontaneous. I like to plan. But after a compelling hallway exchange about a shared interest in talking about the underlying ideology and assumptions of the Quantified Self Practice, Dave Marvit and I pulled together a last minute breakout session for the second day of the conference. 

Even with minimal context and no advertising, we had about twenty people show up to talk. We ended up covering everything from what we think of human behavior, the knowability of observable phenomena, Truth with a capital T, the “special snowflake” uniqueness of individuals, actor network theory, and zen Buddhism. Judging from my tweets from the day, a friend afar wondered if I had “been body-snatched by a wise-cracking philosophy professor.” 

I wish I had been able to articulate it better in closing out the session, but what makes questioning assumptions so important to me, especially in the context of talking about self knowledge through numbers, is that acknowledging assumptions is the ultimate in self-knowledge, and self-awareness. Assumptions are where we start from. It’s where we build knowledge from. It’s why we have disciplines, why we have epistemologies. Acknowledging assumptions is about acknowledging how we come to know what we know.

I find so often, even in fairly self-aware and intellectual technology communities, that assumptions go unacknowledged and unquestioned. And when it comes to the subject of talking about data, we talk about it in its most literal Latin sense as something “given,” or accepted as it stands.

Assumptions lie at the root of conversations that end up at cross purposes. We talk past each other because we’re not even talking about the same thing. That Friday afternoon at the conference was a study in contrasts. We shifted from one conversation about disciplines and epistemologies, into a discussion amongst scholars with completely different methods and interests. We had come together to talk about common goals, but realized we had very different approaches to knowledge in studying QS-related research. (Whitney has a great breakdown of this session here.)

Some from the medical and hard sciences community were interested in building documentation and supporting repeatable QS experiments, lending more scientific rigor to the process of the n=1 experiment. Others were more interested in talking about a space for sharing research that related to self-tracking and Quantified Self practices that doesn’t normally have a space in the conference because it’s not a personal enough story. But the conversation devolved as lines between traditional academic publishing, expertise, and authority crossed with the qualitative and quantitative researchers in the room. There were a lot of methods and approaches to knowledge flying around the room, even though we were all there sharing a common interest in the subject. We had all came to a discussion with some assumptions about what “QS Research” meant.

Acknowledging assumptions are also really important to me as I start thinking about what kind of positions we’re trying to take in the book. We’re pulling these threads together to offer a cohesive framing to understand how our world is changing as data plays an increasingly important role in our lives. What are we as a society taking as given? 

Echoing Kevin Kelly’s closing remarks at the conference, we have to “invent the future that we want.” But I still wonder about who this “we” is.  Who is it that’s building this future? What are our assumptions about the world, and how do we come together on normative positions about the trajectory of our relationship to technologies that we build?

Technology writing tends to fall towards to polarized sides: we assume the best of the technology and write about its dazzling potential, or we assume the worst and write pieces that fall into fear-mongering and dystopic visions. What if we assume that both scenarios are possible, but that intervening forces and contexts influence the direction our society and technology? That’s where our disciplines, our ideological positions come into play.

As for me, I’m still questioning my assumptions. I learn a lot about my assumptions by understanding what they are not. It’s a process of elimination, a negative definition, much like the “neti neti” approach of recognizing that it’s not this, and not that that came up in our freewheeling QS breakout.

As always, the QS conference raised more questions than answers for me, but at least maybe we can get closer to some answers by first acknowledging what take as given, or what we know for certain.