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>and is more likely to associate men with computer programming So would any reasonable human. I think the real problem these advocacy groups have with AI model
by swebs 7y ago
>and is more likely to associate men with computer programming
So would any reasonable human. I think the real problem these advocacy groups have with AI models is that they're not biased. They reflect the real world based on data and evidence, rather than conforming to progressive dogma. I fear that instead of using AI to overcome any wrong assumptions we may have, we're just going to get AI diversity officers to "correct" models that draw any uncomfortable conclusions.
- rolltiide 7y agoRight, it is cognitively negligent to ignore these things, at least in the context of what an exact machine will tell you. That doesn't mean accept it, it means to implement the exceptions and a way to update them or understand sensitivities
- loopz 7y agoThe whole point of Machine Learning is that you don't, and shouldn't, implement exceptions, manual weights, any of that complex parametrization, etc. The machine should figure all that out itself, even if it needs millions of internal parameters to do it! Sounds neat, until you trust it with your life.
- rolltiide 7y agoah yes the cultured machine learning proponents and their reputation for picking up social cues
- 40acres 7y agoWe know that boys and girls are conditioned by society to be more interested in certain fields than others. It's why my cousin asked me to get a robot for her son's birthday as opposed to a dress for her daughter. Do we want our biases embedded in AI? Or do we want a model that hopefully can eliminate some of the subjectivity that humans add (which in some cases we absolutely need)?
- ultrablack 7y agoWhat do you mean “conditioned”? I see no evidence of this. Kids here i scandinavia are free to do or pursuit whatever they want. And from a very early age, before they are able to talk, boys like things, girls likes people.. And later, they choose accordingly.
- crooked-v 7y agoA simple example is toy stores usually having "boy toys" and "girl toys" areas, with tech-related items included in the former more than the latter.
- deleted 7y ago[deleted]
- bluGill 7y agoIs that boy toys, or tech related? Girl toys area or doll area. Hard to tell the difference. Yes the dolls will have more princesses, but is that demand or conditioning?
- loopz 7y agoAccording to the Vedas, mind gets conditioned simply by living life. It is so ingrained, we don't even notice it, but we can't live a life without it either. It's not a negative, but an awareness thing.
- rvense 7y agoI am also in Scandinavia, have small kids and am around a lot of parents, and I see plenty of evidence for this. I am also around a lot of nerds, and I also see plenty of reasons why young women opt out of nerdy circles.
- barry-cotter 7y agoDon’t dance around your point, make it. What are the reasons?
- gbrown 7y ago> "So would any reasonable human." That's the entire point of the article, our biases, as reflected in the world, are learned by models. Consider the following: * More computer programmers are men than women (descriptive statement, no problem) * A predictive model correctly identifies that more computer programmers are men than women (a prediction based on observed data, no problem) * A recruiting agency uses a predictive model to recruit computer programmers. Due to the way the model was trained, it excludes qualified women (not OK, a clear misapplication of an algorithm) Feel free to try this thought experiment in other contexts where existing biases can be amplified through algorithms.
- swebs 7y agoI'm fail to see how step 2 leads to step 3. The fact that there are less woman programmers overall should not have any affect on P(good|woman,programmer).
- crooked-v 7y ago2 leads to 3 because in a naively designed system, being a man is a stronger predictor of being a good programmer than being a woman, because there are more men than women in high-ranking and high-paying programmer positions.
- deleted 7y ago[deleted]
- reroute1 7y agoYes but only in very poorly designed systems. We have those now with AI it's no different. I still see no reason to jump from 2 to 3 other than incompetence in design.
- loopz 7y agoThe other poster stated "predictive model". A predictive model could easily identify males to be "more fit" (higher p-value) than females, in aggregate. The programmer tries to evade this bias, by ie. removing gender values in the system. However, the system STILL identify males "more fit", due to correllated values (likes Mancester United/Liverpool). Machine Learning is not much more than advanced statistics, so data biases gets amplified, thus naive application is often a poor fit. Even worse, the experts are at loss on how to make such predictive systems "perform better", without introducing biases on their own, since the data and categorizations themselves may be full of biases. In the real world, incompetence may not be a huge hurdle when selling complex systems. Also, these biases are invisible, until one thinks about them or spots them in the wild.
- 6gvONxR4sf7o 7y agoA reasonable person isn't going to say that the asymptotic complexity of merge sort is more masculine than feminine. If the goal is to learn whether programming associated with gender, it's unbiased. If the goal is to learn whether the definition of programming is associated with gender, it's biased. The usual goal of NLP is to learn language and (waves hands) meaning. The current tools do this by learning associations, but that's the method, not the goal. The critique is on the methods. The critiques are saying that it's led to trouble, which we'll have to correct for. We want to make it unbiased in the definition based sense, where programming is totally unrelated to gender.
- tomrod 7y agoAlmost. We have to be careful about the data generating process and how we describe the output of AI. If there systematic bias in data collection, it flows through.
- hnick 7y agoAI/ML can be used to analyse the current state of things, and in that regard I think your statement is correct. It can also be used to make predictions and decisions. The question "Is this person likely to be a programmer?" would probably accurately identify a man as more likely to be a programmer, revealing the bias in the data and our society. But "Is this person suited to be a programmer?" sounds similar but is a very different question and we shouldn't confuse the two, especially when letting a machine decide.