The Need for Data Philosophers

In my post On Philosophy, Science, and Data, I explained that although some argue philosophy only reigns in the absence of data while science reigns in the analysis of data, a conceptual bridge still remains between analysis and insight, the crossing of which is itself a philosophical exercise.  Therefore, I argued that an endless oscillation persists between science and philosophy, which is why, despite the fact that all we hear about is the need for data scientists, there’s also a need for data philosophers.

Of course, the debate between science and philosophy is a very old one, as is the argument we need both.  In my previous post, I slightly paraphrased Immanuel Kant (“perception without conception is blind and conception without perception is empty”) by saying that science without philosophy is blind and philosophy without science is empty.

In his book Cosmic Apprentice: Dispatches from the Edges of Science, Dorion Sagan explained that science and philosophy hang “in a kind of odd balance, watching each other, holding hands.  Science’s eye for detail, buttressed by philosophy’s broad view, makes for a kind of alembic, an antidote to both.  Although philosophy isn’t fiction, it can be more personal, creative and open, a kind of counterbalance for science even as it argues that science, with its emphasis on a kind of impersonal materialism, provides a crucial reality check for philosophy and a tendency to over-theorize that’s inimical to the scientific spirit.  Ideally, in the search for truth, science and philosophy, the impersonal and autobiographical, can keep each other honest in a kind of open circuit.”

“Science’s spirit is philosophical,” Sagan concluded.  “It is the spirit of questioning, of curiosity, of critical inquiry combined with fact-checking.  It is the spirit of being able to admit you’re wrong, of appealing to data, not authority.”

“Science,” as his father Carl Sagan said, “is a way of thinking much more than it is a body of knowledge.”  By extension, we could say that data science is about a way of thinking much more than it is about big data or about being data-driven.

I have previously blogged that science has always been about bigger questions, not bigger data.  As Claude Lévi-Strauss said, “the scientist is not a person who gives the right answers, but one who asks the right questions.”  As far as data science goes, what are the right questions?  Data scientist Melinda Thielbar proposes three key questions (Actionable? Verifiable? Repeatable?).

Here again we see the interdependence of science and philosophy.  “Philosophy,” Marilyn McCord Adams said, “is thinking really hard about the most important questions and trying to bring analytic clarity both to the questions and the answers.”

“Philosophy is critical thinking,” Don Cupitt said. “Trying to become aware of how one’s own thinking works, of all the things one takes for granted, of the way in which one’s own thinking shapes the things one’s thinking about.”  Yes, even a data scientist’s own thinking could shape the things they are thinking scientifically about.  Big data evangelist James Kobielus recently blogged about five biases that may crop up in a data scientist’s work (Cognitive, Selection, Sampling, Modeling, Funding).

“Data science has a bright future ahead,” explained Hilary Mason in a recent interview.  “There will only be more data, and more of a need for people who can find meaning and value in that data.  We’re also starting to see a greater need for data engineers, people to build infrastructure around data and algorithms, and data artists, people who can visualize the data.”

I agree with Mason, and I would add that we are also starting to see a greater need for data philosophers, people who can, borrowing the words that Anthony Kenny used to define philosophy, “think as clearly as possible about the most fundamental concepts that reach through all the disciplines.”


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