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The same datasets given to human operators would not produce biased results. And it isn't merely a question of unbalanced classes. The "bias" is, in part, aris

The same datasets given to human operators would not produce biased results. And it isn't merely a question of unbalanced classes. The "bias" is, in part, arising from the inability of machine algorithms to apply rich models to datasets; and to treat "alike images" "alike" -- because the machine has no model of "alike". In the case of images, luminosity values on an image sensor are not constitutive of human facial identity. Any algorithm unable to derive actual identity markers from these is necessarily fundamentally limited -- and routinely, these limitations lead to bias.

scotty79 5y ago
> The same datasets given to human operators would not produce biased results. Actually they would. That's where the "all Asians look the same" comes from. Skewed training set.
- mjburgess 5y ago
  The issue is the limited ability to fit a morphological model to facial features which are "distinct from the ones you are used to". Machines aren't fitting a morphological model. They are being fed 2D pixel patterns -- this information is profoundly insufficient. "Contrast bias in pixel patterns" is not the same as "needing a few more cues to fit a morphological model". The former can never be made into an example of the latter; and therein is the problem.
  - azinman2 5y ago
    It’s as insufficient as a human looking at a photo. We don’t outlaw humans looking at surveillance videos, so why is a machine fundamentally any more biased here?
    - mjburgess 5y ago
      Machines are simply using co-occurance relationships in pixel patterns. Humans have rich models of other humans which we "fit" to the extremely shallow data present in a photograph. This "fitting" is prior to our perception, so as soon as you're perceiving you're also using vast amounts of prior knowledge to reconstruct your environment. The actual "photos" contain almost no information at all -- and lead to gross "stupid" errors.
      - azinman2 5y ago
        Humans similarly can be reduced down to relationships of nearby photo receptors. That’s exactly how V1-V4 works in our human brain… building detectors that go higher and higher level from nearby bits of information from two 2D sensors. In the same way, most neural networks CNNs do exactly this as well. CV models similarly get trained to produce their own higher level abstractions of the world that they, like us, fit data too. As much like computers, we too can be subject to all kinds of gross stupid errors, like optical illusions, mistaken identities, etc. The best CV models are often fine tuned from more generalized models that can recognize thousands and thousands of different kinds of objects, even if in the end they only need to find 1 kind. This is because they build generalized representations of the world. None of this is to suggest there any bias that fundamental to them. We’re starting to see fruits of multi-task learning where models are integrating across domains, which ends up improving accuracy even for just one modality by incorporating more world knowledge. So it’s not like we’ve reached some kind of theoretical limit as to what they can do, and it turns out that theoretical limit is sexist and racist. Nothing could be further from the truth.
        mjburgess 5y ago
        NNs aren't generative causal structural models of reality they are models parameterised by pixels, not by jawlines to mistake one for the other is to mistake and misunderstand intelligence we are not in a humean nightmare of sensory input -- we have bodies which have causal contact with reality, and from these bodies we have built a structural understanding of reality our perception applies this structure to our "sensory input" -- thinking doesnt terminate in the visual cortext, that is where it begins by the time we are seeing we are seeing vastly more than is present on our retina
    - deleted 5y ago
      [deleted]
Tenoke 5y ago
>The same datasets given to human operators would not produce biased results. What? People produce biased results ALL the time based on the 'datasets' they've seen. Additionally, in this specific case humans looked at it, too and made the exact same mistake.

scotty79 5y ago

> The same datasets given to human operators would not produce biased results. Actually they would. That's where the "all Asians look the same" comes from. Skewed training set.

mjburgess 5y ago
The issue is the limited ability to fit a morphological model to facial features which are "distinct from the ones you are used to". Machines aren't fitting a morphological model. They are being fed 2D pixel patterns -- this information is profoundly insufficient. "Contrast bias in pixel patterns" is not the same as "needing a few more cues to fit a morphological model". The former can never be made into an example of the latter; and therein is the problem.
- azinman2 5y ago
  It’s as insufficient as a human looking at a photo. We don’t outlaw humans looking at surveillance videos, so why is a machine fundamentally any more biased here?
  - mjburgess 5y ago
    Machines are simply using co-occurance relationships in pixel patterns. Humans have rich models of other humans which we "fit" to the extremely shallow data present in a photograph. This "fitting" is prior to our perception, so as soon as you're perceiving you're also using vast amounts of prior knowledge to reconstruct your environment. The actual "photos" contain almost no information at all -- and lead to gross "stupid" errors.
    - azinman2 5y ago
      Humans similarly can be reduced down to relationships of nearby photo receptors. That’s exactly how V1-V4 works in our human brain… building detectors that go higher and higher level from nearby bits of information from two 2D sensors. In the same way, most neural networks CNNs do exactly this as well. CV models similarly get trained to produce their own higher level abstractions of the world that they, like us, fit data too. As much like computers, we too can be subject to all kinds of gross stupid errors, like optical illusions, mistaken identities, etc. The best CV models are often fine tuned from more generalized models that can recognize thousands and thousands of different kinds of objects, even if in the end they only need to find 1 kind. This is because they build generalized representations of the world. None of this is to suggest there any bias that fundamental to them. We’re starting to see fruits of multi-task learning where models are integrating across domains, which ends up improving accuracy even for just one modality by incorporating more world knowledge. So it’s not like we’ve reached some kind of theoretical limit as to what they can do, and it turns out that theoretical limit is sexist and racist. Nothing could be further from the truth.
      - mjburgess 5y ago
        NNs aren't generative causal structural models of reality they are models parameterised by pixels, not by jawlines to mistake one for the other is to mistake and misunderstand intelligence we are not in a humean nightmare of sensory input -- we have bodies which have causal contact with reality, and from these bodies we have built a structural understanding of reality our perception applies this structure to our "sensory input" -- thinking doesnt terminate in the visual cortext, that is where it begins by the time we are seeing we are seeing vastly more than is present on our retina
  - deleted 5y ago
    [deleted]

Tenoke 5y ago

>The same datasets given to human operators would not produce biased results. What? People produce biased results ALL the time based on the 'datasets' they've seen. Additionally, in this specific case humans looked at it, too and made the exact same mistake.