Programming a computer to perform a task is a powerful way to reveal hidden assumptions. What the problem of machine learning on human data reveals, is there is no objectivity. Our most 'objective' models will simply learn and then reinforce biases and prejudices and cause harm to people. We can't build objective, neutral, value-free models when it comes to human behavior, because humans change their behavior in response to the models. When we reject stereotypes we are imposing a set of values, just as much as when we embrace them. Machine learning forces us to confront the fact that this is exactly what we are doing.
There's a kind of philosophical crisis going on here. We need an entirely new way to think about the limits of objectivity in human sciences, and how to create ethical models in the presence of feedback between science and society. The language of objective physical science doesn't work.
Isn't the ideal system to not assume anything about the content it's analyzing, but to base the model on behavioral patterns instead? In the case of Google ranking, either a site has traffic/low bounce/backlinks/social cred or it doesn't--why is Google attempting to 'read' and analyze the content itself? (Other than to serve ads of course)
Because Google no longer just searches for literal strings of text, ranked by links.
They now infer your meaning and find results that match your intent -- even if the exact search string doesn't exist in the search results.
For more on this, read about Hinton's work on "thought vectors" [1].
So if Google can't determine the meaning of African American language as well as it can standard English, then that content is likely to be less visible in search results.
[1] https://www.theguardian.com/science/2015/may/21/google-a-ste...
> Some aspects of communication are likely to prove more challenging, Hinton predicted. “Irony is going to be hard to get,” he said. “You have to be master of the literal first. But then, Americans don’t get irony either. Computers are going to reach the level of Americans before Brits.”
Bing seems to do the same thing. I really wish there were a grep for the web because both engines get it wrong even when I try to exclude results.
It's basically grep for the web, since it uses the actual query instead of the meaning of it.
As an aside - this is a slippery slope conversation. Some people believe language requires rules. Other, more special people, think language is fluid and anarchist. The latter of those two do not make search engines.
My guess is that it's unlikely to provide sources that are in one of these dialects, as the link density will be low.
A further problem, though, is that search engines try to understand the query and the content searched at a deeper level than mere keyword matches. For example, they may try to pick up synonyms (if I search for “movies in <local area>”, perhaps results for “films” might be helpful too) and associations.
So that when you search for "cheesecake recipes", you don't get a well-sourced, high-traffic article from a respectable website describing in detail how the new business initiative by Cheesecake Factory is a recipe for disaster.
* Disclaimer: I pulled that example out of thin air. There's actually no such article.
Depends what you mean by "the corpus". Let's take the example of loan applications. Maybe the training data contains credit scores, biographical information, and personal essays about the applicants from loan officers.
Let's say loan officers tend to use more negative language when talking about black people.
Any decent machine learning system given racial data will not adopt those biases. In fact, it will counteract them. It will determine that e.g. the semantic content of the essays is an indicator of loan suitability, and it will also notice if it has to e.g. add something to the essay score for black people to make the best decisions.
As long as the output data is objective, competent ML systems will account for any bias in the input data.
> Should we attempt to detect prejudices in machine learning models
We don't have to. If the output examples we are training on are true, the ML algorithm won't adopt any incorrect biases.
This is literally the point of machine learning. You want to make the best possible decision. If your algorithm has incorrect human biases, you're losing money. ML naturally accounts for this stuff.
And the bias people are probably talking about is bias in the data, not bias in the "input", as you put it.
See http://arxiv.org/abs/1607.06520 for a counterexample to your point.
Do you know what I mean? There's truth as in what the data says, and there's truth as in what's underlying before social processes " corrupt " the truth. This paper examines ways to get at the latent truth.
Do you think that's a useful endeavor? It's dismissive to say it has anything to do with what the "author likes".
Ah, so you're defining "truth" as "whatever I imagine the world would be like if society worked the way I wished it did". There's another word for that; "fantasy".
This is rarely the case when working wtih real data, and thus inspecting whether our models are biased against protected classes is probably one of the most important things an ML practitioner should do.
There is no reason an ML algorithm would be biased against a "protected class". It doesn't know what those are. It's possible that the algorithm will uncover a truth that you don't like, like that there as differences in risk profiles across race or gender, but that doesn't mean the algorithm is incorrectly biased. It just means that reality is at odds with how you might want it to be.
Which data are you referring to? In most cases, the training data isn't human-generated, and if it is, we usually want to match human behavior as close as possible.
Read anything written by Solon Barocas: http://solon.barocas.org/
The issue of prejudice stems from refusing to treat AAVE as a valid input to an AI, mirroring the racism in American culture that similarly devalues and excludes it. Output can conform to some approximation of "standard" form without being problematic, following the pattern of interaction between standard and dialect speakers. As a speaker of the standard, it's gauche and derisive to imitate or refuse or comprehend a dialect speaker, not to respond in your own form.
What is really the difference between spelling a word wrongly and using a non-dictionary word to express something. We except one as "slang" but will criticize the other because they don't comply with established rules. Isn't that what slang is? A break from established rules.
Anyways, just a thought, because I agree with you; if you publish - proofread. I just find it interesting to throw a Socrates argument at myself from time to time.
Btw and somewhat of topic.. this was the Pinker interview that was most interesting:
Which is precisely why social media (e.g. your newsfeed) is such an awful source for news, especially when you consider how low the standards are on digital publishing these days...
If the training corpus for a machine learning model contains stereotypes and biases, then the output of the model will reflect those prejudices.
When that model is used in large-scale applications, it will not just repeat those biases, it will amplify them. It propagates biased language, which feeds back into the model in a self-reinforcing vicious cycle of increasing bias.
Should we attempt to detect prejudices in machine learning models and actively counter them, creating a self-reinforcing virtuous cycle of balance and tolerance?
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As an aside, for an article about language, this one has a lot of typos. Doesn't anyone proofread before posting?