It’s interesting as an approach to say that most of the sciences are moving towards this concept of being an information science. Even my home in Psychology is rooted in this idea of being driven by the data. As the large data revolution took hold, and big data has become more available there is a question that lingers over the discipline of how we should pursue getting answers to questions we may have in our discipline. Should it be driven by the person or by the data?
There are many good reasons for doing either, with machine learning approaches co-opting the same tools that Psychologists use to run analysis, linear regression, latent frameworks, and covariation between variables. As data has gotten more rich and complex there has been a surge of different types of modeling needed to meet the demand of researchers for their experiments. I believe it comes down to the problem of being able to de-black box the journey of the data and not just how to find its solution. To be able to understand the ramifications of the question being asked and not simply running it.
It was a couple years ago when I sat in a stuffy conference room and two scientists from Iran had come up with a machine learning approach of determining whether someone was gay using photographs and profile pictures. An interesting application of data that already seems problematic but especially so when considering being gay in Iran is illegal. Lots of questions came up about the validity of the project and whether the data was valid but these are the things we are going to have to contend with. As we get more sophisticated models and richer data, even though each piece of the data may contribute only a small margin to the greater statistical story, when adding 10,000 variables with 100,000,000 rows we can start to predict just about anything, the question is, should we?
Questions – There were many but these are the once I am going to start with:
How do we try to understand the data which goes through these computational models?
Is network security, like physical security measures (e.g. locks on doors), play more of a role of security theatre and deterrence rather than being fully secure?
Peter J. Denning and Craig H. Martell. Great Principles of Computing. Cambridge, MA: The MIT Press, 2015.
John D. Kelleher, Deep Learning (Cambridge, MA: MIT Press, 2019).