Sunday, November 10, 2019
The unescapable (im)perfection of data
In data science, we often work with data collected from people. In the field of recommender system research, this data consist of ratings, likes, clicks, transactions and potentially all sorts of other quantities that we can measure: dwell time on a webpage, or how long someone watches a video. Sometimes we get so caught up in creating our systems, that we forget the underlying truth:
Data is unescapably imperfect.
Let's start to unpack this with a simple example. Think about a step counter. It's tempting to argue that this data is perfect. The step counter counts steps and that seems quite straightforward. However, if you try to use this information to draw conclusions, you run into problems: How accurate is the device? Do the steps reflect a systematic failure to exercise, or did the person just forget to wear the device? Were they just feeling a little bit sick? Are all steps the same? What if the person was walking uphill? Why was the person wearing the step counter? How were they reacting to wearing it? Did they do more steps because they were wearing the counter? How were they reacting to the goal for which the data was to be used? Did they decide to artificially increase the step count (by paying someone else to do steps for them)?
In this simple example, we already see the gaps, and we see the circle: collecting data influences data collection. The collection of data actually creates patterns that would not be there if the data were not being collected. In short, we need more information to interpret the data, and ultimately the data folds back upon itself to create patterns with no basis in reality. It is important to understand that this is not some exotic rare state of data safely ignored in day-to-day practice (like the fourth state of water). Let me continue until you are convinced that you cannot escape the imperfection of data.
Imagine that you have worked very hard and have contolled the gaps in your data, and done everything to prevent feedback loops. You use this new-and-improved data to create a data-based system, and this system makes marvelous predictions. But here's the problem: the minute that people start acting on those predictions the original data becomes out of date. Your original data is no longer consistent with a world in which your data-based system also exists. You are stuck with a sort of Heisenberg's Uncertainty Principle: either you get a short stretch of data that is not useful because it's not enough to be statistically representative of reality, or a longer stretch of data, which is not useful because it encodes the impact of the fact that you are collecting data, and making predictions on the basis of what you have collected.
So basically, data eats its own tail like the Ouroboros (image above). It becomes itself. As science fictiony as that might sound, this issue has practical implications that researchers and developers deal with (or ignore) constantly. For example, in the area of recommender system research in which I am active, we constantly need to deal with the fact that people are interacting with items on a platform, but the items are being presented to them by a recommender system. There is no reality not influenced by the system.
The other way to see it, is that data is unescapably perfect. Whatever the gaps, whatever the nature of the feedback loops, data faithfully captures them. But if we take this perspective, we no longer have any way to relate data to an underlying reality. Perfection without a point.
And so we are left with unescapable.