I rather enjoyed the Stephen Ramsay reading and was particularly struck by his perspective on the notion that DH tools and algorithms cannot be objective or neutral, nor should they be. "It cannot be neutral...since there is no level at which assumption disappears. It must, rather, assert its utter lack of neutrality with candor, so that the demonstrably non-neutral act of interpretation can occur" (182). This ties into the topic of selection bias and preservation we were talking about with the archive, but comes up repeatedly in Ramsay's essay as well. He stresses early on that the point of DH is not to find objective answers to interpretive problems, which is an easy leap to make when including algorithms and math into the humanities. Rather, data visualization is another way to present and sort through data that already exists, allowing for new avenues of interpretation of patterns that would be harder to notice otherwise.
It feels good to see a scholar outright acknowledge and interrogate the notion of neutrality in algorithms. As this is all in favor of interpretation and analysis, the tools being used and/or created are being crafted for that specific purpose, and will thus be 'tainted' by our academic assumptions from the outset. People have a tendency to assume algorithms and math are impartial and immune to bias, and it is oddly validating to see Ramsay outright saying that is silly and everything is biased by our assumptions in some way.