yummyfajitas
Joined 19 years ago
Karma
41,937
Submissions
12,329
Member since
Feb 3
Recent submissions
Comment · 9 years ago
You're wildly mischaracterizing what I said. I didn't suggest "people can't handle my truth". I suggested the article we are discussing says there are certain truths that ML systems should not learn.
The article explicitly recommends building systems which can't learn those things, and suggests characterizing them is a first step:
"We recommend addressing this through the explicit characterization of acceptable behavior. One such approach is seen in the nascent field of fairness in machine learning, which specifies and enforces mathematical formulations of nondiscrimination in decision-making (19, 20). Another approach can be found in modular AI architectures, such as cognitive systems, in which implicit learning of statistical regularities can be compartmentalized and augmented with explicit instruction of rules of appropriate conduct (21, 22)."
According to the article, the most closely related work "is concurrent work by Bolukbasi et al. (6), who propose a method to “debias” word embeddings."
(Recall that in the context of the article, "bias" is something that generates true predictions that are objectionable rather than something which is false.)
Comment · 9 years ago
The example the parent poster described is "doctors are 66% male", which could very well be true.
Comment · 9 years ago
The parent's point is that they may not be absorbing stereotypes from humans at all. They may be generating accurate beliefs about the world from text representations of the world.
Comment · 9 years ago
[flagged]
Comment · 9 years ago
See Fig 1 and 2.
Comment · 9 years ago
The paper has them:
- An AI correctly infers (simply by reading text) that a physicist is male and a nurse is female.
- An AI correctly infers the gender of humans with androgyonous names.
- An AI infers insects are unpleasant and flowers are pleasant to humans.
- An AI also infers that African American names are more likely to be associated with unpleasantness than European names.
[edit: to those who dislike this comment, can you tell me what you object to? Which of my concrete examples is not in the paper?]
Comment · 9 years ago
The usual definition of bias in ML papers is E[theta_estimator - theta]. That is explicitly a systematically wrong prediction.
In any case, the paper suggests that this "bias" or "prejudice" is better described as "truths I don't like". I'm asking if the author knows of any cases where they are actually not truthful. The paper does not suggest any, but maybe there are some?
Comment · 9 years ago
Do you have any evidence that this effect results in machines making systematically wrong inferences?
Near as I can tell, your paper shows that these "biases" result in significantly more accurate predictions. For example, Fig 1 shows that a machine trained on human language can accurately predict the % female of many professions. Fig 2 shows the machine can accurately predict the gender of humans.
Normally I'd expect a "bias" to result in wrong predictions - but in this case (due to an unusual redefinition of "bias") the exact opposite seems to occur.
(Drawing on your analogy with stereotypes, it's probably also worth linking to a pointer on stereotype accuracy: http://emilkirkegaard.dk/en/wp-content/uploads/Jussim-et-al-... http://spsp.org/blog/stereotype-accuracy-response )
Comment · 9 years ago
Similarly, flat earthism is an orthodox theory since it was orthodox at one point and is not completely dead.
Comment · 9 years ago
I agree that in some historical time, some of Murray's views might be considered orthodox. That doesn't make them orthodox today - nowadays he's subversive.
Comment · 9 years ago
Charles Murray wasn't discussing human biodiversity at all. His talk was on an entirely different topic. No one argued against him at all. They merely disrupted his talk and then assaulted him and others.
The Nazi in question did not engage in any violent acts at that gathering. That makes him a peaceful protester.
Comment · 9 years ago
This article is a little odd. It describes conservatives as being worried that professors are "subversive", but it's exactly the opposite - conservatives worry that colleges are enforcing and indoctrinating people in the orthodoxy.
It's the rare subversive academics, e.g. Charles Murray, who are being assaulted and chased of campus for holding unorthodox opinions.
And it's happening off campus as well - for instance, we recently had a peaceful (but unorthodox) anti-war activist assaulted: https://antifascistnews.net/2017/04/09/richard-spencer-just-...
Comment · 9 years ago
Perhaps you'd prefer to be regulated by the whims of a corporation & their pursuit of profit extraction rather than the democratic legitimacy of government, but thankfully, most of us would not.
On the contrary, most of us would prefer this. That's why yellow cabs are losing market share everywhere that men with guns don't take away their right to choose.
Comment · 9 years ago
Of course there's a difference.
In a taxi you can rip off a firangi or fail to pick up a black passenger at your leisure. With Uber, this will cause your ratings to drop below 4.3, or your acceptance rate/cancellation rate to drop below/above whatever threshold they use. Then you get kicked off the platform.
Uber is providing the necessary regulation of the system that governments fail to provide.
Comment · 9 years ago
That's a lot of nice sounding platitudes, but it doesn't really tell me anything concrete beyond "TeMPOraL dislikes Uber".
Blog: http://www.chrisstucchio.com
[email protected]