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We thank Michal Kosinski, whose work we source in this case study, for constructive feedback on the original text. The central theme of our Calling Bullshit course is you don't always need to understand the technical details рискнул jackson rancheria casino они a statistical analysis or computer algorithm to call bullshit on its use.

As illustrated in the diagram below, there are typically several steps that people go through when they construct quantitative arguments. First, they assemble data that they use as inputs. Second, they feed these data into some the analytic machinery, bet at home casino opinie below by the black box. Sometimes visit web page results are simply presented as-is, but often the results are bet at home casino opinie to justify a decision or interpretation as well.

Because these statistical or algorithmic black boxes are technically убит, black jack odds вспомнила, they tend to scare off those who might otherwise challenge an argument. Most people do not have the graduate training in statistics or machine learning that they would need to fully understand the inner workings of the black box, and so they feel that they have no right to comment on the claims being made.

But black boxes should not be deterrents. We argue that one doesn't need extensive technical training in order to think critically about even highly technical analyses.

When a quantitative claim is nonsense, we can usually figure this out without having to go into the technical details of bet at home casino opinie statistics or computer algorithms. Usually the problem arises because of http://namisg.info/blackjack-count-basic-strategy-4-8-decks.php kind of bias or other problem with the data that is fed bet at home casino opinie, or because of some problem with the interpretation of the results that come out.

Therefore, all we need to do is to clearly and carefully examine what is going into the black box, and what comes out. This way of thinking is extremely useful, because we won't always have the expertise necessary to evaluate the details of the technical analysis itself — and even if we do, such details are not always readily available. In this particular case study, we don't need to draw upon our experience using and teaching machine learning in order to form our critique. In a video segment from the course, we describe the black box schema in further detail.

In our case study bet at home casino opinie criminal machine learning, we look at the input side: In the present case study, we look at the output side: We address a recent study investigating the degree to which machine processing of facial images can be used to infer sexual orientation. We concede that we are expert neither in facial recognition technology nor in the developmental endocrinology of sexual orientation. However, we do have a bit of experience bet at home casino opinie to thinking logically about the implications of experimental results, particularly in the areas of data science and biology.

In early Septemberthe Economist and the Guardian released a pair of oddly credulous news stories about a forthcoming paper in the Journal of Personality and Social Psychology. The paper describes a series of experiments that test whether a deep neural network can be trained to discern individuals' sexual orientations from their photographs.

From an internet dating website, the authors selected photographs of nearly men and nearly women, equally distributed between heterosexual bet at home casino opinie homosexual orientations. They find that not only can their algorithm predict sexual orientation better than chance; it can outperform human judgement.

Though do note that the performance of the algorithm is far from perfect 1. For obvious reasons, this paper attracted a large amount of attention from traditional and social media alike. Commentators questioned the ethical implications of conducting such a study given that stigma against homosexuality remains widespread in the US and given that homosexuality is punishable by penalties up to and including death elsewhere.

While we don't bet at home casino opinie the authors' defense particularly compelling they "merely bolted together software and data that are readily available" — maybe so, but most inhumane technologies are created this waywe do not aim to address the ethical components of the work here. We will let other more qualified scholars criticize the authors' "hermetic resistance to any contributions from the fields of sociology, cultural anthropology, feminism, or LGBT studies.

While the initial news reports did a dismal job of explaining how accurate the algorithm is, the original research paper is pretty see more and we do not intend to question the authors' methods here.

In other words, we will assume that their input data are bet at home casino opinie and that the black box bet at home casino opinie functioning as it should. We will assume that the technical aspects of their neural network represent reasonable choices for the problem, implemented correctly.

We will set aside issues associated with a binary classification of sexual orientation. We will also take their raw results as correct. We will assume that their algorithm performs as reported, and that the study could be replicated on an independent sample of similar size. So what is left to criticize? The authors' conclusions and interpretation of their results. First, the authors infer that the computer is picking up on features that humans are unable to detect.

Second, the authors suggest that these features are the results of prenatal hormone exposure. Neither inference is warranted. We discuss these in turn.

The authors find that their deep neural network does a better job of guessing sexual orientation based on facial photographs than do humans solicited via Amazon Mechanical Turk. Based on this result, they argue that the neural net is picking up on features that humans visit web page unable to detect.

The first line of their abstract proclaims " We show that faces contain much more information about sexual orientation than can be perceived and interpreted by the human brain ". The first line of the Conclusions section repeats the assertion: While the results are consistent with this claim, they do not come anywhere near demonstrating it. Indeed, there is a bet at home casino opinie parsimonious explanation for why the neural net outperforms humans at this classification task: For one thing, the authors have pitted a bet at home casino opinie algorithm against untrained human judges.

The machine had thousands of images to learn from. Humans are good at analyzing faces and may be able to generalize from the people they know — but they may have quite limited experience to draw upon and they did not get any practice at bet at home casino opinie specific task whatsoever. But that's not the biggest problem.

The more important issue is that humans are notoriously bad at aggregating information to update prior probabilities, whereas computational learning algorithms can do this sort of thing very well. To illustrate, let's casino poker bicycle an hypothetical experiment.

Imagine that we provide a machine learning algorithm with a camera feed showing a blackjack table, and instruct it to learn how to play this casino game. Blackjack is not hard if we ignore the challenges of card counting and a standard learning algorithm could quickly infer good rules-of-thumb for playing. Once we have trained our learning algorithm, we compare the performance of the computer to the performance of humans recruited via Amazon's Mechanical Turk 2.

Suppose we find that the computer performs substantially better than do the humans. What can we conclude? One might conjecture that the machine can see some facet of the undealt cards that people cannot. Perhaps it is picking up information from the back of the face-down card atop the dealer's deck, for example. But notice that we are asking the computer and the humans to do two separate things: With their excellent visual systems and pattern detection abilities, humans are very good at the former task.

How do we prove to websites that we are humans not bots? But it is well established that we humans are terrible at the latter tasknamely making probabilistic decisions based on partial information.

Thus it would be silly to conclude from this experiment that the cards are somehow marked in a way that visit web page machine but not a human can detect. Obviously the untrained humans are simply making stupid bets.

A parallel situation arises when we ask a computer or a human to determine bet at home casino opinie of two people is more likely to be gay. As a human, we receive all sorts of information from the bet at home casino opinie. Rather than trying to decide whether to hit bet at home casino opinie stand given that you're holding a jack and a four bet at home casino opinie the dealer is showing a six 3we might see that one person has a baseball cap while bet at home casino opinie other wears full sideburns.

One has an eyebrow piercing, the other a tattoo. Each of these facts shifts our probability estimate of the subjects' sexual orientations, but how much? And how do multiple facts interact? If the subject has a baseball cap but is clean-shaven with glasses, how does that shift our belief about the subject's orientation?

Humans are terrible at these sorts of calculations, and moreover the humans bet at home casino opinie the study are untrained in these probabilities. Yet they are expected to compete against a computer algorithm which can be very good at these calculations, and which has been trained extensively.

It is no surprise whatsoever that naive humans do poorly at this task compared to a trained algorithm. There is absolutely no need to postulate secret physiognomic features below the threshold of human detection. Doing so is a profound violation of the principle of parsimony. After concluding that the computer is picking up cues that go undetected by humans, Wang and Kosinski set out to explore what these cues might be.

They observe that the faces of people with homosexual and heterosexual orientation have slightly different contours on average.

From this finding the authors make an inferential leap that we consider unfounded and non-parsimonious. In the abstract of their paper, Wang and Kosinski write " Consistent with the prenatal hormone theory [PHT] of sexual orientation, gay men and women tended to have gender-atypical facial morphology The prenatal hormone theory proposes that differences in sexual orientation arise due to differences in hormone exposure prior to birth.

So do the results of the present study actually provide support for the PHT? There are two issues here. First, how strong is the evidence that facial structure differs according to sexual orientation?

Second, how strong is the evidence that any differences measured in this study can be attributed to prenatal hormone exposure? We treat these in turn. First note that the present study does not constitute a rigorous experimental protocol demonstrating morphological differences under controlled conditions. The gold standard for demonstrating differences in facial shapes would be more info the morphometrics under laboratory conditions; here we are at least two steps away, using machine learning to infer average facial structures based upon self-selected photographs on a dating site.

Any number of factors other than innate differences in facial structure could be involved, ranging from grooming to photo choice to lighting to the angle of the photograph angle-correcting algorithm notwithstanding. If this bet at home casino opinie a biology paper making within-species morphological comparisons, we would expect direct 3D measurements. Photographs lose far too much information as things are projected into the 2D plane. Biologists used to employ calipers; these days it is all about 3D scanning.

While measurements from photographs appear to be more common in anthropology, things get even worse once you start looking at human faces. Humans do many things to influence the way that they present themselves and to modify their appearances. We cannot see how to reliably separate differences in physiognomy from differences in facial self-presentation based on photographs alone. For this reason, we are skeptical of previous claims about facial structure and sexual orientation.

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