A New article in the December issue of Science details the new software program that Harvard scientists have created to analyze data. Apparently, the strength of the software is that it can find patterns in enormous datasets, and further, it can detect these patterns even though the scientist doesn’t specify what he or she is looking for. In other words, the software simply finds things–anomalous patterns– that MIGHT be interesting, rather than finding things that the researcher is trying to find, or things the researcher already is interested in.
This seems a key distinction. As one of the authors of the paper put it:
“This ability to search for patterns in an equitable way offers tremendous exploratory potential in terms of searching for patterns without having to know ahead of time what to search for,” said David Reshef.
To me there is a big intellectual problem lurking here. Are we turning too much of our inquiry over to the computer? The computer should exist to help us answer the questions that we as humans have identified. But what happens when the computer itself suggests the question? Assuming that the computer cannot identify all the possible questions that we might want to answer, are we at risk of letting the machine limit the kinds of questions that we ask?
Further, are the computer’s questions the same as ours? For sure, this dilemma exists in digital humanities. Sometimes it seems digital humanities projects– on GIS for instance, or especially text analysis using text searching strategies– have the computer cart in front of the intellectual horse. Do we really CARE about how many times a given word appears in a run of 18th century newspapers? Or are we discussing this only because the computer allows us to do so? In a similar way, will the computer at Harvard identify patterns that are important to it, but not intrinsically important to US? If the machine suggests the answer before we even had the question, is that really intellectual work? Or are we passive consumers of research then?