4 Matching Annotations
  1. Jan 2026
    1. In fact, researchers must decide how to exercise their power based on inconsistent and overlapping rules, laws, and norms. This combination of powerful capabilities and vague guidelines can force even well-meaning researchers to grapple with difficult decisions.

      Should it be researchers who solely must decide this? I feel as though that not only puts a lot of responsibility onto individuals alone but also leaves potentially detrimental ambiguity across social research. I know many research studies must receive approval via the IRB to ensure responsible conduct, so this phrasing seems a little flawed

    1. More generally, social researchers will need to combine ideas from social science and data science in order to take advantage of the opportunities of the digital age; neither approach alone will be enough.

      The author emphasizes that both social and data science must be considered in tandem with one another to inform comprehensive research and to optimize the opportunities offered by this digital era. It seems that a limitation then could be a lack of resources like time or monetary means to ensure quality considerations in both aspects.

    2. The “Internet of Things” means that behavior in the physical world will be increasingly captured by digital sensors. In other words, when you think about social research in the digital age you should not just think online, you should think everywhere.

      In my Intro to Information class, I recall my professor discussing the substantial impact that many researchers deem AI to have — an impact that is only comparable to that of the introduction of the Internet. It makes me think about how revolutionary AI feels to be in the midst of its development and how I often overlook this scale when thinking about the internet simply because it's all I've known throughout my upbringing. The digitization and automation of many once manual processes is so commonplace now, I can only imagine how much it'll change in the upcoming years

    1. But when Blumenstock and colleagues aggregated their estimates to Rwanda’s 30 districts, they found that their estimates were very similar to estimates from the Demographic and Health Survey, which is widely considered to be the gold standard of surveys in developing countries. Although these two approaches produced similar estimates in this case, the approach of Blumenstock and colleagues was about 10 times faster and 50 times cheaper than the traditional Demographic and Health Surveys.

      I wonder where the discrepancies lied, as I am sure there were some valuable differences between Blumenstock's aggregate estimates and that of the Demographic and Health Survey's. Additionally, I wonder about the extent to which the results of this machine learning model approach would be successful in other countries or via a different means of database. This first section really highlights how how incredibly nuanced the implications of Machine Learning are