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Classifying anything related to people is a political act; it isn't possible to be apolitical and "just make a classifier". The classifier either accounts for t

by pdkl95 9y ago

Classifying anything related to people is a political act; it isn't possible to be apolitical and "just make a classifier". The classifier either accounts for the biases already present in the data, or they are present in the output, obscured (laundered) behind an opaque wall of machine learning.

closeparen 9y ago
Unjust policies laundered those biases into reality, at which point they stop being biases and start being facts with unjust causes. Whitewashing the training data to make predictions based on a hypothetical injustice-free world where redlining never happened will just give you garbage predictions.
- pdkl95 9y ago
  I agree that it's hard to get good predictions. The point, however, is that ignoring systemic bias and other historic influences is worse. > and start being facts with unjust causes. Of course. That means you know the data contains an "unjust" bias. Do you want to account for that bias, or do you want to proceed anyway risking problems (e.g. Simpson's Paradox). There are more confounding factors in heaven and earth, closeparen, than are dreamt of in your model. https://mathbabe.org/2017/01/04/recidivism-risk-algorithms-are-inherently-discriminatory/ https://mathbabe.org/2017/01/04/recidivism-risk-algorithms-a... https://mathbabe.org/2013/01/29/bill-gates-is-naive-data-is-not-objective/ https://mathbabe.org/2013/01/29/bill-gates-is-naive-data-is-...
  - closeparen 9y ago
    Confounding factors undermine causal arguments. You can build a perfectly useful model without knowing about the hidden variables. A risk-management approach to the criminal justice system doesn’t call for fudge factors. It’s fundamentally unacceptable. Risk management is all about using people’s demographics against them (see: all insurance pricing). Wokeness on the part of the risk managers doesn’t fix that. The criminal justice system needs to treat people as individuals and not classes - this requires that it steer clear of evidence-based risk assessment.

by pdkl95 9y ago

closeparen 9y ago

Unjust policies laundered those biases into reality, at which point they stop being biases and start being facts with unjust causes. Whitewashing the training data to make predictions based on a hypothetical injustice-free world where redlining never happened will just give you garbage predictions.

pdkl95 9y ago
I agree that it's hard to get good predictions. The point, however, is that ignoring systemic bias and other historic influences is worse. > and start being facts with unjust causes. Of course. That means you know the data contains an "unjust" bias. Do you want to account for that bias, or do you want to proceed anyway risking problems (e.g. Simpson's Paradox). There are more confounding factors in heaven and earth, closeparen, than are dreamt of in your model. https://mathbabe.org/2017/01/04/recidivism-risk-algorithms-are-inherently-discriminatory/ https://mathbabe.org/2017/01/04/recidivism-risk-algorithms-a... https://mathbabe.org/2013/01/29/bill-gates-is-naive-data-is-not-objective/ https://mathbabe.org/2013/01/29/bill-gates-is-naive-data-is-...
- closeparen 9y ago
  Confounding factors undermine causal arguments. You can build a perfectly useful model without knowing about the hidden variables. A risk-management approach to the criminal justice system doesn’t call for fudge factors. It’s fundamentally unacceptable. Risk management is all about using people’s demographics against them (see: all insurance pricing). Wokeness on the part of the risk managers doesn’t fix that. The criminal justice system needs to treat people as individuals and not classes - this requires that it steer clear of evidence-based risk assessment.