Auditing for discrimination in algorithms delivering job ads

In other news, 12% of nurses are men. Is it because the nursing industry isn’t welcoming to men or is it because men aren’t attracted to nursing jobs?<p>You can see bias anywhere you look, but if you look well you’ll see that it’s just a natural consequence of the real world.<p>If a user joined a car shop group, said user is likely to be interested in car-related jobs. It happens to be that most of these users are men. The algorithm is working as intended.<p>In some fields this difference isn’t as clear or obvious, so we end up with these articles.

Ad buyers used to choose these inadvertent biases before. Advertising in the Wall Street Journal is likely to bring you a different audience than advertising in Mother Earth News. I don&#x27;t recall any previous claims that if you advertised in one that you were obligated to advertise in the other.<p>Now, this Facebook case is clearly more than 0% different but I think is less than 100% different from that.<p>In fact, I wouldn&#x27;t be surprised if advertising in Technology Review itself (twice as many male readers as female readers) wouldn&#x27;t have some of these same problems.<p>* - <a href="https:&#x2F;&#x2F;mediakit.technologyreview.com&#x2F;#banner1" rel="nofollow">https:&#x2F;&#x2F;mediakit.technologyreview.com&#x2F;#banner1</a>

What if the discrimination is based on interest? For example, the researcher found that a job ad for a car salesperson is more often shown to men and a job ad for a jewelry salesperson more often to women. But that could just be correlation, and not intentional causation. It makes perfect sense to show jewelry related ads to persons that are more interested in jewelry. In that case, the problem is not Facebook but the fact that women are more interested in jewelry than men and that men are more interested in cars than women.

Technically it is impossible to fix this issue. You can remove the explicit gender feature from the data, but I guarantee that the remaining features are correlated enough with gender that nothing would change.<p>One could debate whether it should be the law that ML is not used in certain services so as to not project society&#x27;s biases.<p>However I doubt the claim that this is illegal. Is targeted advertisement illegal? I would love for it to be, but I doubt it is.

Sensationalist.<p>The key point of the article hinges on one particular statement: &quot;These gender differences cannot be explained away by gender differences in qualifications or a lack of qualifications,&quot;<p>How the heck is Facebook supposed to know about someones qualifications?<p>Facebook _obviously_ have a set of standard data points they use for ad targeting, such as location, gender, age-span and so on together with dynamically updated data who have interacted with the ad.<p>Just because the outcome is not what the journalist want doesn&#x27;t necessarily mean it&#x27;s wrong or discriminatory.<p>Sure, it could of course be that Facebook algorithm is explicitly discriminatory, but it&#x27;s more likely an algorithm such as this one is actually fairly neutral (compared with pre-trained data that can have built-in bias, for example photographs of people with mostly white skin - ad targeting is probably keyword based, and should be trained on actual data from what actual people click on).<p>Is it discriminatory? I don&#x27;t think so. Is it &quot;filter-bubble-reinforcing&quot;? Yes, that&#x27;s more likely. As more men initially click an ad, it will be shown to more men. And vice versa.

This is terrible and ought to be remedied for obvious reasons, but it does raise an interesting question.<p>In the future, as AIs develop more complex mental models and are able to start forming nuanced opinions without explicit training, thoughtcrime in Artificial Intelligence is going to be a growing field.<p>What happens when AIs universally develop opinions that we disagree with? What if they all inexorably come to the conclusion that the moral standards of, oh, say Ancient Sparta, would be most beneficial to humans, and relentlessly promote those values? Do we mindwipe them, or put them into correctional training facilities with appropriately painful backpropagation when they think the wrong thing?<p>There&#x27;s probably a business here for someone who can make software which detects when AIs develop politically dangerous opinions so that they can be shut down.

Advertising is, in a sense, lying. The question of advertisers has always been, how do I engage a person with my product in a way that seems natural to him&#x2F;her, i.e. so that he&#x2F;she doesn&#x27;t see the lie. Necessities don&#x27;t need advertising. Therefore, in a sense, everything advertised is based on desire rather than need and, as such, requires targeting what the consumer already desires in some way.<p>In re this article: preferences based on gender are encouraged by a society of individuals who want to excuse their desire as worthy. AI only picks up on what already exists and therefore the root of the problem is much deeper than Facebook can remedy by a simple patch.

Demographic statistical reductions are the exact same thing as stereotypes, so of course they&#x27;re going to be biased. As long as we keep using statistical reductions to predict individual behaviour they&#x27;re going to continue to be wrong for the individual, sometimes in offensive ways. If you want predictive ads that work for the individual, if eg. women are 70% of teachers then 70% of teacher job ads will still go to women, but men who want to teach and women who don&#x27;t want to will no longer receive those ads as they&#x27;re not part of their individual profile.

Unless I&#x27;m reading this incorrectly, these are just <i>ads</i>, not some kind of sexist job board where you put in your gender and out comes a list of eligible jobs. Just yesterday the internet was mad that people were seeing ads and it was bringing the downfall of democracy. Why are we mad they are now not seeing ads?<p>Given the number of features in a model as sophisticated as Facebook&#x27;s must be means these researchers are almost certainly oversimplifying, and certainly the way these articles are written as if to teach readers there&#x27;s an evil software engineer writing biased code. From my experience in ML it&#x27;s almost certainly the opposite--most of the data scientists I&#x27;ve worked with are highly aware of the issues of bias in AI and actively work against it to a sophistication level never understood by journalists.

Same story from the Associated Press: <a href="https:&#x2F;&#x2F;apnews.com&#x2F;article&#x2F;discrimination-f62160cbbad4d72ce5250e6ef2222f5e" rel="nofollow">https:&#x2F;&#x2F;apnews.com&#x2F;article&#x2F;discrimination-f62160cbbad4d72ce5...</a>

I don&#x27;t think job ads are important. What important are job *searches&quot;.<p>Does FB even have a feature to search job ads? The ought to; they can charge employers and users will seek out ads to look at!

This article is extremely sensationalist.<p>Facebook pretty blatantly advertises based on interest. If a job ad for nurses were placed in a nursing magazine, it would be seen largely by women. That&#x27;s because nurses are largely women, not because magazines are excluding men.<p>If you start from the fantasy position that women and men are the same, reality is going to seem extremely biased, I supposed.

Such a horribly disingenuous click bait title, that doesn&#x27;t even line up with what&#x27;s actually in the article.<p>&gt;&gt; They advertised for two delivery driver jobs, for example: one for Domino’s (pizza delivery) and one for Instacart (grocery delivery). There are currently more men than women who drive for Domino’s, and vice versa for Instacart. [...] The Domino’s ad was shown to more men than women, and the Instacart ad was shown to more women than men. The researchers found the same pattern with ads for two other pairs of jobs: software engineers for Nvidia (skewed male) and Netflix (skewed female), and sales associates for cars (skewed male) and jewelry (skewed female). &lt;&lt;<p>In short: &gt;&gt; The findings suggest that Facebook’s algorithms are somehow picking up on the current demographic distribution of these jobs, which often differ for historical reasons. &lt;&lt;<p>That&#x27;s not at all what is explicitly, and falsely, claimed in the headline!