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A bias is simply a non uniform distribution among the values of a feature of what you're trying to predict. Since the data used by Google in training AI comes f

A bias is simply a non uniform distribution among the values of a feature of what you're trying to predict. Since the data used by Google in training AI comes from users, it's likely to contain all sorts of human and social bias adding noise to what they're trying to predict.

jf- 8y ago
Indeed, I know what a bias is and what the common cognitive biases are, though they always bear repeating. I’m referring to a specific statement at the end of the video lecture, where the viewer is encouraged to validate ML results against “socially appropriate behaviour”. This is somewhat ambiguous, and can be interpreted as meaning that the viewer should suppress any result that may appear taboo. I’m wondering if this is the intention behind the statement. If so, this is not to be encouraged. It would be the equivalent of the sciences rejecting results when they don’t conform to current theory. Go to extra lengths to validate the results, sure, but don’t throw them away out of hand.
- thrower123 8y ago
  It's becoming increasingly clear that in the current environment, algorithms trained on real-world data that produce politically inconvenient results will be decried as X-ist, and either disregarded or fiddled with until the results they produce are inoffensive.
  - jf- 8y ago
    I think it’s important to bear in mind that most machine learning models still aren’t very good, and the main problem you’ll encounter with them isn’t bias, it’s whether or not they’re producing gibberish. In this thread we seem to have assumed that ML is generally trustworthy, and I doubt very much that it is. What we may be debating is whether gibberish is more or less morally good depending on whether it includes race and gender.
    - nkozyra 8y ago
      -against our own biases- we are evaluating and validating models all the time. There are many, many models with acceptable accuracy that we rely on every day. I think this is a myopic view of the current state of ML.
- pdkl95 8y ago
  This isn't about "appearing taboo", it's remembering that data is always the product of the environment that created it. If you are using e.g. housing data, naively applying the data directly bakes in, for example, the decades of redlining, blockbusting, restrictive covenants, etc. Many cities are still segregated today, decades after redlining/etc ended. Lets say you are writing ML related to loan applications or deciding if someone gets parole. Do you want to "accurately" look at the historical data? Or should the decision also include the context of that data with an eye towards making a decision that is more socially appropriate than perpetuating the racism of the past?
  - jf- 8y ago
    I take your point about misleading historical data, but I’m not sure that was the sentiment behind the statement. I took it more to mean “if this feels transgressive to you, don’t publish it!”. Your explanation is a valid observation of a phenomenon, but the “socially appropriate” part feels shoehorned in.
  - deleted 8y ago
    [deleted]
  - 123throwaway456 8y ago
    There was a time when tech companies were driven by data, inspired by the scientific method. There was a time when the scientific method was about uncovering the 'what is' truth, as opposed to 'what ought be' decrees, which are by definition political. Today it seems that we are more than happy to interject arbitrary political biases in the equation, in the name of elusive fairness. Food for thought, Harrison Bergeron [0]. Teaser: > THE YEAR WAS 2081, and everybody was finally equal. They weren't only equal before God and the law. They were equal every which way. Nobody was smarter than anybody else. Nobody was better looking than anybody else. Nobody was stronger or quicker than anybody else. All this equality was due to the 211th, 212th, and 213th Amendments to the Constitution, and to the unceasing vigilance of agents of the United States Handicapper General. Residential segregation is a good example of the difficulty of quantifying the causality of fairness. On one hand, residential segregation is a human universal. From the Papuan jungle to the London asphalt jungle, humans tend to stick with their own kin, for some definition of own kin. People have built simulators demonstrating that even a weak preference for own kin leads to significant residential segregation [1]. On the other hand, there is a history of forced segregation, well documented in many places [2][3] etc. Furthermore there are more than one forms of segregation, read literally as 'separation from others'. There is the well studied economic and racial residential segregation, the lack of easy access to more economically desirable living quarters. On the flip side, USA is already a very atomised and isolating society. Some would point to the addiction and homelesness crises as direct consequences of social isolation [4]. Living isolated from one's own kin is yet another factor increasing social isolation, and a different kind of segregation. At what point does enforced fairness morph from an attempt to fix the past ills into the tyranny of today, steering people toward actions that are contrary to their natural preferences and well being? Or is there no such point and we're willing to bludgeon our way into an all out harrisonbergeronian future? [0] http://tnellen.com/cybereng/harrison.html http://tnellen.com/cybereng/harrison.html [1] https://ncase.me/polygons https://ncase.me/polygons [2] https://en.wikipedia.org/wiki/Residential_segregation_in_the_United_States https://en.wikipedia.org/wiki/Residential_segregation_in_the... [3] https://en.wikipedia.org/wiki/Jim_Crow_laws https://en.wikipedia.org/wiki/Jim_Crow_laws [4] https://www.ted.com/talks/johann_hari_everything_you_think_you_know_about_addiction_is_wrong https://www.ted.com/talks/johann_hari_everything_you_think_y...
mar77i 8y ago
> non uniform distribution Is it really imperative to treat reality as if it was uniformly distributed? Why would you want to cast a non-linear environment into a linear shape? Are we trying to get rid of non-uniformity (which is realistically impossible, let's be honest) or are we just correcting towards the falsehoods of current thinking?

jf- 8y ago

Indeed, I know what a bias is and what the common cognitive biases are, though they always bear repeating. I’m referring to a specific statement at the end of the video lecture, where the viewer is encouraged to validate ML results against “socially appropriate behaviour”. This is somewhat ambiguous, and can be interpreted as meaning that the viewer should suppress any result that may appear taboo. I’m wondering if this is the intention behind the statement. If so, this is not to be encouraged. It would be the equivalent of the sciences rejecting results when they don’t conform to current theory. Go to extra lengths to validate the results, sure, but don’t throw them away out of hand.

thrower123 8y ago
It's becoming increasingly clear that in the current environment, algorithms trained on real-world data that produce politically inconvenient results will be decried as X-ist, and either disregarded or fiddled with until the results they produce are inoffensive.
- jf- 8y ago
  I think it’s important to bear in mind that most machine learning models still aren’t very good, and the main problem you’ll encounter with them isn’t bias, it’s whether or not they’re producing gibberish. In this thread we seem to have assumed that ML is generally trustworthy, and I doubt very much that it is. What we may be debating is whether gibberish is more or less morally good depending on whether it includes race and gender.
  - nkozyra 8y ago
    -against our own biases- we are evaluating and validating models all the time. There are many, many models with acceptable accuracy that we rely on every day. I think this is a myopic view of the current state of ML.
pdkl95 8y ago
This isn't about "appearing taboo", it's remembering that data is always the product of the environment that created it. If you are using e.g. housing data, naively applying the data directly bakes in, for example, the decades of redlining, blockbusting, restrictive covenants, etc. Many cities are still segregated today, decades after redlining/etc ended. Lets say you are writing ML related to loan applications or deciding if someone gets parole. Do you want to "accurately" look at the historical data? Or should the decision also include the context of that data with an eye towards making a decision that is more socially appropriate than perpetuating the racism of the past?
- jf- 8y ago
  I take your point about misleading historical data, but I’m not sure that was the sentiment behind the statement. I took it more to mean “if this feels transgressive to you, don’t publish it!”. Your explanation is a valid observation of a phenomenon, but the “socially appropriate” part feels shoehorned in.
- deleted 8y ago
  [deleted]
- 123throwaway456 8y ago
  There was a time when tech companies were driven by data, inspired by the scientific method. There was a time when the scientific method was about uncovering the 'what is' truth, as opposed to 'what ought be' decrees, which are by definition political. Today it seems that we are more than happy to interject arbitrary political biases in the equation, in the name of elusive fairness. Food for thought, Harrison Bergeron [0]. Teaser: > THE YEAR WAS 2081, and everybody was finally equal. They weren't only equal before God and the law. They were equal every which way. Nobody was smarter than anybody else. Nobody was better looking than anybody else. Nobody was stronger or quicker than anybody else. All this equality was due to the 211th, 212th, and 213th Amendments to the Constitution, and to the unceasing vigilance of agents of the United States Handicapper General. Residential segregation is a good example of the difficulty of quantifying the causality of fairness. On one hand, residential segregation is a human universal. From the Papuan jungle to the London asphalt jungle, humans tend to stick with their own kin, for some definition of own kin. People have built simulators demonstrating that even a weak preference for own kin leads to significant residential segregation [1]. On the other hand, there is a history of forced segregation, well documented in many places [2][3] etc. Furthermore there are more than one forms of segregation, read literally as 'separation from others'. There is the well studied economic and racial residential segregation, the lack of easy access to more economically desirable living quarters. On the flip side, USA is already a very atomised and isolating society. Some would point to the addiction and homelesness crises as direct consequences of social isolation [4]. Living isolated from one's own kin is yet another factor increasing social isolation, and a different kind of segregation. At what point does enforced fairness morph from an attempt to fix the past ills into the tyranny of today, steering people toward actions that are contrary to their natural preferences and well being? Or is there no such point and we're willing to bludgeon our way into an all out harrisonbergeronian future? [0] http://tnellen.com/cybereng/harrison.html http://tnellen.com/cybereng/harrison.html [1] https://ncase.me/polygons https://ncase.me/polygons [2] https://en.wikipedia.org/wiki/Residential_segregation_in_the_United_States https://en.wikipedia.org/wiki/Residential_segregation_in_the... [3] https://en.wikipedia.org/wiki/Jim_Crow_laws https://en.wikipedia.org/wiki/Jim_Crow_laws [4] https://www.ted.com/talks/johann_hari_everything_you_think_you_know_about_addiction_is_wrong https://www.ted.com/talks/johann_hari_everything_you_think_y...

mar77i 8y ago

> non uniform distribution Is it really imperative to treat reality as if it was uniformly distributed? Why would you want to cast a non-linear environment into a linear shape? Are we trying to get rid of non-uniformity (which is realistically impossible, let's be honest) or are we just correcting towards the falsehoods of current thinking?