4 ms·

What are you saying? That its better to just blindly trust in the output of a blackbox AI output? That naively feeding in all possible data into an AI is the "

What are you saying? That its better to just blindly trust in the output of a blackbox AI output? That naively feeding in all possible data into an AI is the "best" way to do it? It is been well documented that blindly trusting AI just leads to it perpetuating human stereotypes and worsen flawed systems. https://dl.acm.org/doi/10.1145/3531146.3533138 https://dl.acm.org/doi/10.1145/3531146.3533138 https://www.aclu.org/news/privacy-technology/algorithms-in-health-care-may-worsen-medical-racism https://www.aclu.org/news/privacy-technology/algorithms-in-h... It's not some great insight that offering different inputs will result in different outputs and MAYBE we want to get better output with a different input.

fallingknife 4y ago
Assuming that a human stereotype is untrue is just as biased as assuming it is true. If you judge a model by how well it confirms your previous bias, then the model tells you nothing you didn't already know.
- impalallama 4y ago
  - MichaelZuo 4y ago
    To be fair to the parent, prejudice means, according to the OED: preconceived opinion that is not based on reason or actual experience. So preconceived opinions based on reason or actual experience do not count as prejudice.
  - hcurtiss 4y ago
    This response is unnecessarily hostile. A machine stereotyping may have overwhelming accuracy. In that case, I would argue the machine is not wrong. It's our policy response to its outputs that may or may not be wrong. Changing the machine to disregard accurate descriptions of the real world only serves to bias the machine.
  - 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 ago
      I 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 ago
        I 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.

fallingknife 4y ago

Assuming that a human stereotype is untrue is just as biased as assuming it is true. If you judge a model by how well it confirms your previous bias, then the model tells you nothing you didn't already know.

impalallama 4y ago
- MichaelZuo 4y ago
  To be fair to the parent, prejudice means, according to the OED: preconceived opinion that is not based on reason or actual experience. So preconceived opinions based on reason or actual experience do not count as prejudice.
- hcurtiss 4y ago
  This response is unnecessarily hostile. A machine stereotyping may have overwhelming accuracy. In that case, I would argue the machine is not wrong. It's our policy response to its outputs that may or may not be wrong. Changing the machine to disregard accurate descriptions of the real world only serves to bias the machine.
- 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 ago
    I 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 ago
      I 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.