Objectivity and stuff

I wanted to write about some of the interesting things people have been saying about reviewing but part of my brain obviously wants to talk about reason and evidence and those sorts of things. I guess I haven’t done much of that this year in attempt to look less like a philosophy professor.

Anyway – objectivity! The thing with objectivity as a word is that we (including myself) use it in a way that implies various things which maybe aren’t really part of what it means. Objectivity carries positive connotations and connotations of authority in contrast to subjectivity. Those connotations suggest impartial judgement and a lack of bias. That’s all well and good – words can mean whatever a community of users want them to mean but I think it creates confusion.

Here is a different sense of ‘objective’ – to say something is objective is to say that two people can follow the same steps/process and come up with the same answer reliably. Maybe we should use a different word for that but such processes are often described as ‘objective’ because they clearly contrast with subjective judgement.

The thing is that meaning does not in ANYWAY imply a lack of bias. Lots of systematic or automated processes can contain bias. Indeed we expect there to be biases in, for example, processes for collecting data. More extreme examples include machine learning algorithms which are inherently repeatable and ‘objective’ in that sense (and the sense that they operate post-human judgement) that nonetheless repeat human prejudices because those prejudices exist in the data they were trained on.

Other examples include the data on gender disparity in compensation for Uber drivers – the algorithm was not derived from human prejudices but there was still a pay disparity that arose from different working patterns that arose from deep-seated social disparities.

However, there is still an advantage here in terms of making information and data gathered more objective. Biases may not be eliminated but they are easier to see, identify and quantify.

Flipping back to ‘subjective’, I have discussed before both the concept of intersubjectivity (shared consensus opinions and beliefs that are not easily changed) as well as the possibility of their being objective facts about subjective opinions (e.g. my opinion that Star Trek: Discovery was flawed is subjective but it is an objective fact about the universe that I held that opinion).

Lastly the objective aspect of data can be mistaken for the more subjective interpretation of the data. In particular the wider meaning or significance of a data set is not established simply by the fact that the data is collected reliably or repeatedly.

Consider another topic: IQ. I’ve discussed before aspect of IQ and IQ testing and much of the pseudoscientific nonsense talked about it. Look at these two claims between Roberta and Bob:

• Roberta: My IQ is higher than Bob’s.
• Roberta: I am more intelligent than Bob.

The first statement may be an objective fact – it is certainly a claim that can be tested and evaluated by prescribed methods. The second statement is more problematic: it relies on opinions about IQ and the nature of intelligence that are not well established. The objectivity of the first statement does not establish the objectivity of the second. Nor does the apparent objectivity of the first imply that it does not have biases that may also impact wider claims based upon it.