Kulynych, Overdorf, et al., are firmly on the side of ‘the little person’ raging against the algorithmically optimised machines in the hands of powerful service providers. They want to empower those disadvantaged by such systems to fight back, using protective optimisation technologies (POTs). In short this involves manipulating the inputs to the system so as to tip things into a better balance (at least as seen by the deployer of POTs tactics).
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The first example in the paper looks at how to combat negative externalities caused by Waze for residents of towns and neighbourhoods adjacent to busy routes. Three towns reporting such issues are Leonia in New Jersey, Lieusaint in France, and Fremont in California. The authors use route planning software to figure out local road changes (specifically, what slowdown in local traffic) is required to prevent Waze from sending traffic through the town.
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A second example looks at credit scoring. Here a group perceiving themselves to be disadvantaged by an algorithm have one option at their disposal: changes the inputs to the machine learning system by taking and repaying loans. "The POT must inform the collective about who and for which loan they should apply for and repay, in such a way that they reduce the false-negative rate on the target group after retraining." This is a bit like the concept of poisoning in adversarial machine learning, but used with the intention of decreasing an error for a given target group.
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Things get complicated pretty quickly though:
Unfortunately, the POT increases the false positives. That is, it shifts the harm to the bank, which would give more loans to people who cannot repay, increasing its risk.
There are also consequences for taking a loan that you can’t repay (e.g. dispossesion), which make it unclear whether it’s in the interests of the group the measure was trying to protect either.
From the article:
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