independent
My previous comment was concerned with the importance of having clearly defined goals. This step is vital to understanding whether a dataset is appropriate for the problem being solved. However, cognitive bias' may appear due to a data scientist attempting to make data work when it is of poor quality. An independent review helps prevent blindspots and promotes successful projects
First, clarify your objectives and assess whether you have the right data to support these objectives.
This statement is in bold for a reason. It can not be understated how important context is to data analysis. This was explored in detail with previous readings about context and thick data. Like most goals in life, having a clearly defined answer to the question 'what am I trying to accomplish?', frames everything properly from the start.
Big Dick Data is a formal, academic term that we, the authors, have coined to denote big data projects that are characterized by patriarchial, cis-masculinist, totalizing fantasies of world domination as enacted through data capture and analysis.d-undefined, .lh-undefined { background-color: rgba(0, 0, 0, 0.2) !important; }1John Laudun. Big Dick Data projects ignore context, fetishize size, and inflate their technical and scientific capabilities..d-undefined, .lh-undefined { background-color: rgba(0, 0, 0, 0.2) !important; }1nyah bean
I can't express how helpful such plain terminology is when reading academic publications. Big Dick Data is similar to the ideas presented in Tricia Wangs piece on 'Thick' Data. Big dick data helps to draw attention to fallacies of the modern data science community, most glaringly, the overwhelming preference for large datasets that may not actually give the best sample for extracting insight.
Thick Data is data brought to light using qualitative, ethnographic research methods that uncover people’s emotions, stories, and models of their world.
Quantitative data is great for black and white thinking, with stark objective ideas used. However, human beings are unfortunately more complicated and dynamic than can be described by quantitative statistics alone. A prescriptive methodology is sometimes best achieved through qualitative 'thick' data.
By “politics,” I mean arrangements of power and authority in human associations as well as the activities that take place within those arrangements.
The whole concept of this article is fascinating and I had never previously considered technologies to be political. It reminds me of how the documentary "Coded Bias" discusses how AI facial recognition software is often deployed by the rich into poor neighborhoods as a sort of test. This further highlights the power disparities tech can produce
For McEwan, surfacing the mechanisms and discourses by which people and their data are made to Bwork^ under capitalism is vital to the development of effective resistance.
This seems to be the most wait and see method of moving forward and is grounded in scientific curiosity. I am also curious about what role personal data plays in regards to capitalism and the current socio-political climate.