Like many Americans, I have a love-hate relationship with technology: I inwardly cringe when my preschooler clamors for screen-time with our iPad instead of storytime with a book. Our municipalities, our government, our insurers, and even the vendors of books are awash with technology as well. At a recent hackathon, the expert from the local transit authority confessed that with logs of accidents, and data about the wealth and race of inhabitants, they have too much data to inform decision-making.
Understanding how the humanities have traditionally approached big problems can inform how experts in data science can model meaningful conclusions based on the same skillful concern with answering questions based on a serious inquiry. Humanists, after all, are experts at probing the largest questions of our species. One example might be mastering what philosophers have said about topics like justice or gender since Aristotle, unpacking the values behind those concepts, and coming to a new understanding of how those ideas are changing in our own day. The traditional role of the humanities is to elevate the ambitions of human beings, asking what it means to be a citizen, an heir to the legacies of learning on many continents, or an individual with the capacity of dissent.
Now more than ever, it is important for those who work with big data to train in the questions of the humanities – as for those in the humanities to make clear the relevance of their tools of critical thinking to data scientists. The values of the humanities are the values of treating those questions – and many smaller ones – through skillful scholarship.
The particular skills of humanities scholarship take many forms, but they all agree in emphasizing serious engagement with texts and their contexts. They ask about the nature of the evidence at hand,the values that govern the inquiry,and the many ways of modeling those concepts. These skills, among other things, allow scholars to produce both a strong consensus about truth where it is found, while simultaneously making room for dissent about issues of interpretation, identity and meaning. Skillful interpretation of the data allows scholars to agree about the facts (for example, which manuscripts are the authentic production of a particular medieval scribe), while establishing room for dissent about the interpretation of those facts (for example, characterizing the perspective of Biblical literalism versus historical interpretation).
I recently proposed the concept of “Critical Search” as a general model for how humanistic values translate into the world of data. Critical Search has three major components that mirror how traditional humanists have approached big questions in the past: seeding a query, winnowing, and guided reading.