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Setting aside the rudeness, you basically just agreed with the parent. They said people don't want an AI with no bias, we want an AI that mirrors own biases. Y
by naasking 4y ago
Setting aside the rudeness, you basically just agreed with the parent. They said people don't want an AI with no bias, we want an AI that mirrors own biases.
Your example of loans is perfectly in line with this: we have a social bias against racism and racial profiling, and we want our AIs to reflect that bias. Purely on the question of risk assessment, race must meaningfully impact a loan risk or the AI wouldn't develop that bias [1], but that isn't a correlation we want the AI to consider in its risk assessment, ie. we build in a different bias.
[1] race in this case would be a proxy for all sorts of complex historical socioeconomic factors
- visarga 4y agoI think people are conflating the technical sense of "bias" to the social sense. Technically, a biased model is one that overfits the training data and has poor generalisation. Ethically, a biased model would be one that reinforces discrimination.
- naasking 4y agoI agree the parent poster was using the ethical/social sense of "bias" in calling a discriminatory loan risk AI biased, but I don't think it's overfitted and so I don't think this is what the original poster meant. In the loan example, the objective is to assess risk. Assuming the data we feed it draws equally from all races, the AI will still almost certainly infer a racial component to loan risk. This is not a statistical or systematic error, this is an accurate assessment of society due to long-standing racial inequalities. I think the original poster meant that ethics is not an objective science, and so our preference for certain ethical reasoning is itself a bias. "Correcting" the "biased" outcomes we don't like that are apparent in the data, and that are a reflection of society at large, involves biasing the training data.