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Algos tend to inherit the biases of their developers. Let's say you're a woman on your period and that the bleeding, cramps and pain can get severe. You might
by arkitaip 8y ago
Algos tend to inherit the biases of their developers.
Let's say you're a woman on your period and that the bleeding, cramps and pain can get severe. You might need to visit the rest room or maybe take a few more pauses than the modeled average, that happens to be based on men, during certain days. The algorithm that is about to fire you doesn't factor in these issues when it decides to fire you for not meeting its performance quota.
- dx034 8y agoThe example you describe inherits the bias of the sample group, not the developer. A woman comparing individual performance with averages might notice monthly fluctuations rather than a man, but it doesn't mean they'd always recognize it or even look for it.
- visarga 8y agoThe algorithm should have average figures specific to all relevant factors: age, sex, race, nationality, immigration status, not a one-fits all approach. But it's a horrible idea in the first place.
- sagebird 8y agoHahaha, wow -- full circle, this inadvertently arrives back at a racist system it seeks to counteract! For example, if it averages that hispanics are 1.6 times faster than the average worker at sorting boxes, does that mean it should fire a hispanic who is outperforming a white co-worker because adjusting for averages, that individual is 'under-performing'?
- deleted 8y ago[deleted]