Cataloging a year of blogging: the philosophical turn

Passion and motivation are strange and confusing facets of being. Many things about them feel paradoxical. For example, I really enjoy writing, categorizing, and — obviously, if you’ve read many of the introductory paragraphs on TheEGG — blabbing on far too long about myself. So you’d expect that I would have been extremely motivated to write up this index of posts from the last year. Yet I procrastinated — although in a mildly structured way — on it for most of last week, and beat myself up all weekend trying to force words into this textbox. A rather unpleasant experience, although it did let me catch up on some Batman cartoons from my childhood. Since you’re reading this now, I’ve succeeded and received my hit of satisfaction, but the high variance in my motivation to write baffles me.

More fundamentally, there is the paradox of agency. It feels like my motivations and passions are aspects of my character, deeply personal and defining. Yet, it is naive to assume that they are determined by my ego; if I take a step back, I can see how my friends, colleagues, and even complete strangers push and pull the passions and motivations that push and pull me. For example, I feel like TheEGG largely reflects my deep-seated personal interests, but my thoughts do not come from me alone, they are shaped by my social milieu — or more dangerously by Pavlov’s buzzer of my stats page, each view and comment and +1 conditioning my tastes. Is the heavy presence of philosophical content because I am interested in philosophy, or am I interested in philosophy because that is what people want to read? That is the tension that bothers me, but it is clear that my more philosophical posts are much more popular than the practical. If we measure in terms of views then in 2014 new cancer-related posts accounted for only 4.7% of the traffic (with 15 posts), the more abstract cstheory perspective on evolution accounted for 6.6% (with 5 posts), while the posts I discuss below accounted for 57.4% (the missing chunk of unity went to 2014 views of post from 2012 and 2013). Maybe this is part of the reason why there was 24 philosophical posts, compared to the 20 practical posts I highlighted in the first part of this catalog.

Of course, this example is a little artificial, since although readership statistics are fun distraction, they are not particularly relevant just easy to quantify. Seeing the influence of the ideas I read is much more difficult. Although I think these exercises in categorization can help uncover them. In this post, I review the more philosophical posts from last year, breaking them down less autobiographically and more thematically: interfaces and useful delusions; philosophy of the Church-Turing thesis; Limits of science and dangers of mathematics; and personal reflections on philosophy and science. Let me know if you can find some coherent set of influences.

Weapons of math destruction and the ethics of Big Data

I don’t know about you, dear reader, but during my formal education I was never taught ethics or social consciousness. I even remember sitting around with my engineering friends that had to take a class in ethics and laughing at the irrelevance and futility of it. To this day, I have a strained relationship with ethics as a branch of philosophy. However, despite this villainous background, I ended up spending a lot of time thinking about cooperation, empathy, and social justice. With time and experience, I started to climb out of the Dunning-Kruger hole and realize how little I understood about being a useful member of society.

One of the important lessons I’ve learnt is that models and algorithms are not neutral, and come with important ethical considerations that we as computer scientists, physics, and mathematicians are often ill-equipped to see. For exploring the consequences of this in the context of the ever-present ‘big data’, Cathy O’Neil’s blog and alter ego mathbabe has been extremely important. This morning I had the opportunity to meet Cathy for coffee near her secret lair on the edge of Lower Manhattan. From this writing lair, she is working on her new book Weapons of Math Destruction and “arguing that mathematical modeling has become a pervasive and destructive force in society—in finance, education, medicine, politics, and the workplace—and showing how current models exacerbate inequality and endanger democracy and how we might rein them in”.

I can’t wait to read it!

In case you are impatient like me, I wanted to use this post to share a selection of Cathy’s articles along with my brief summaries for your browsing enjoyment:

Big data, prediction, and scientism in the social sciences

Much of my undergrad was spent studying physics, and although I still think that a physics background is great for a theorists in any field, there are some downsides. For example, I used to make jokes like: “soft isn’t the opposite of hard sciences, easy is.” Thankfully, over the years I have started to slowly grow out of these condescending views. Of course, apart from amusing anecdotes, my past bigotry would be of little importance if it wasn’t shared by a surprising number of grown physicists. For example, Sabine Hossenfelder — an assistant professor of physics in Frankfurt — writes in a recent post:

It isn’t so surprising that social scientists themselves are unhappy because the boat of inadequate skills is sinking in the data sea and physics envy won’t keep it afloat. More interesting than the paddling social scientists is the public opposition to the idea that the behavior of social systems can be modeled, understood, and predicted.

As a blogger I understand that we can sometimes be overly bold and confrontational. As an informal medium, I have no fundamental problem with such strong statements or even straw-men if they are part of a productive discussion or critique. If there is no useful discussion, I would normally just make a small comment or ignore the post completely, but this time I decided to focus on Hossenfelder’s post because it highlights a common symptom of interdisciplinitis: an outsider thinking that they are addressing people’s critique — usually by restating an obvious and irrelevant argument — while completely missing the point. Also, her comments serve as a nice bow to tie together some thoughts that I’ve been wanting to write about recently.

Machine learning and prediction without understanding

Big data is the buzzword du jour, permeating from machine learning to hadoop powered distributed computing, from giant scientific projects to individual social science studies, and from careful statistics to the witchcraft of web-analytics. As we are overcome by petabytes of data and as more of it becomes public, it is tempting for a would-be theorist to simply run machine learning and big-data algorithms on these data sets and take the computer’s conclusions as understanding. I think this has the danger of overshadowing more traditional approaches to theory and the feedback between theory and experiment.