MC433 - week 9

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These are my notes from November 23 for MC433 at the London School of Economics for the 2017-2018 school year. I took this module as part of the one-year Inequalities and Social Science MSc program.

The usual disclaimer: all notes are my personal impressions and do not necessarily reflect the view of the lecturer.

Prediction, Accuracy, and (Un)fairness


Data mining and the discourse on discrimination (PDF) by Solon Barocas

A short paper from 2014 surveying the literature on how data mining can be used for discrimination:

Data justice (PDF) by Nathan Newman

On data being the bedrock of the new digital economy and so it becomes a matter of economic justice, not just personal privacy. Due to network effects etc etc, the companies are ossifying and the possibility of serious competition is declining. Suggests a combination of bottom-up and top-down approaches: greater consumer awareness and agency, but also better regulation. Some problems outlined: differential pricing; algorithmic profiling; using private data to enforce the will of employers; predatory debt (more than usual, anyway); the death of local journalism as advertiser money flees. Basically these companies are undermining all the presumed benefits of capitalism (which are at least semiotically important to keep people believing in the system) while accelerating all the rapacious elements. My favourite takeaway: Nicholas Carr’s term “digital sharecropping”, in which these companies take advantage of an “incredibly efficient mechanism to harvest the economic value of the free labor provided by the very many and concentrate it into the hands of the very few”.

Could have used some more editing (like what is, for instance) and it’s a little naive about taking corporate claims at face value. Could have also gone into the attention economy aspects, or how the “value” these companies generate actually fits in to the broader economic picture (imo they’re really just parasitic on commodity production). I have this hypothesis that the ad tech industry is just concentrated capitalism (the combination of its death drive & the technology necessary to fulfill it) which this report doesn’t really address at all, but I guess that just means there is a gap in the metaphorical market of ideas which I now have a duty to fill.

Worth reading, though.

Opening data zine by the Detroit Digital Justice Coalition

A (beginner’s) guide to data justice.



Question under discussion (which we didn’t have much time to cover, due to student presentations):

To what extent does the idea of representation differ across the three communication eras studied in this course?

My take: in the intelligent communication age, what matters is representation both among who designs the algorithms and in the data used. Very different from the one-to-many and many-to-many eras.