Saturday, January 26, 2019

Algorithmic Unfairness Without Any Bias Baked In

Discussion of (un)fairness in machine learning hit mainstream political discourse this week, when Representative Alexandria Ocasio-Cortez discussed the possibility of algorithmic bias, and was clumsily "called out" by Ryan Saavedra on twitter:
It was gratifying to see the number of responses pointing out how wrong he was --- awareness of algorithmic bias has clearly become pervasive! But most of the pushback focused on the possibility of bias being "baked in" by the designer of the algorithm, or because of latent bias embedded in the data, or both:
Bias in the data is certainly a problem, especially when labels are gathered by human beings. But its far from being the only problem. In this post, I want to walk through a very simple example in which the algorithm designer is being entirely reasonable, there are no human beings injecting bias into the labels, and yet the resulting outcome is "unfair". Here is the (toy) scenario -- the specifics aren't important. High school students are applying to college, and each student has some innate "talent" $I$, which we will imagine is normally distributed, with mean 100 and standard deviation 15: $I \sim N(100,15)$. The college would like to admit students who are sufficiently talented --- say one standard deviation above the mean (so, it would like to admit students with $I \geq 115$). The problem is that talent isn't directly observable. Instead, the college can observe grades $g$ and SAT scores $s$, which are a noisy estimate of talent. For simplicity, lets imagine that both grades and SAT scores are independently and normally distributed, centered at a student's talent level, and also with standard deviation 15: $g \sim N(I, 15)$, $s \sim N(I, 15)$.

In this scenario, the college has a simple, optimal decision rule: It should run a linear regression to try and predict student talent from grades and SAT scores, and then it should admit the students whose predicted talent is at least 115. This is indeed "driven by math" --- since we assumed everything was normally distributed here, this turns out to correspond to the Bayesian optimal decision rule for the college.

Ok. Now lets suppose there are two populations of students, which we will call Reds and Blues. Reds are the majority population, and Blues are a small minority population --- the Blues's only make up about 1% of the student body. But the Reds and the Blues are no different when it comes to talent: they both have the same talent distribution, as described above. And there is no bias baked into the grading or the exams: both the Reds and the Blues also have exactly the same grade and exam score distributions, as described above.

But there is one difference: the Blues have a bit more money than the Reds, so they each take the SAT twice, and report only the highest of the two scores to the college. This results in a small but noticeable bump in their average SAT scores, compared to the Reds. Here are the grades and exam scores for the two populations, plotted:
So what is the effect of this when we use our reasonable inference procedure? First, lets consider what happens when we learn two different regression models: one for the Blues, and a different one for the Reds. We don't see much difference:

The Red classifier makes errors approximately 11% of the time. The Blue classifier does about the same --- it makes errors about 10.4% of the time. This makes sense: the Blues artificially inflated their SAT score distribution without increasing their talent, and the classifier picked up on this and corrected for it. In fact, it is even a little more accurate!

And since we are interested in fairness, lets think about the false negative rate of our classifiers. "False Negatives" in this setting are the people who are qualified to attend the college ($I > 115$), but whom the college mistakenly rejects. These are really the people who have come to harm as a result of the classifier's mistakes. And the False Negative Rate is the probability that a randomly selected qualified person is mistakenly rejected from college --- i.e. the probability that a randomly selected student is harmed by the classifier. We should want that the false negative rates are approximately equal across the two populations: this would mean that the burden of harm caused by the classifier's mistakes is not disproportionately borne by one population over the other. This is one reason why the difference between false negative rates across different populations has become a standard fairness metric in algorithmic fairness --- sometimes referred to as "equal opportunity."

So how do we fare on this metric? Not so badly! The Blue model has a false negative rate of 50% on the blues, and the Red model has a false negative rate of 47% on the reds --- so the difference between these two is a satisfyingly small 3%.

But you might reasonably object: because we have learned separate models for the Blues and the Reds, we are explicitly making admissions decisions as a function of a student's color! This might sound like a form of discrimination, baked in by the algorithm designer --- and if the two populations represent e.g. racial groups, then its explicitly illegal in a number of settings, including lending.

So what happens if we don't allow our classifier to see group membership, and just train one classifier on the whole student body? The gap in false negative rates between the two populations balloons to 12.5%, and the overall error rate ticks up. This means if you are a qualified member of the Red population, you are substantially more likely to be mistakenly rejected by our classifier than if you are a qualified member of the Blue population.

What happened? There wasn't any malice anywhere in this data pipeline. Its just that the Red population was much larger than the Blue population, so when we trained a classifier to minimize its average error over the entire student body, it naturally fit the Red population --- which contributed much more to the average. But this means that the classifier was no longer compensating for the artificially inflated SAT scores of the Blues, and so was making a disproportionate number of errors on them --- all in their favor.

The combined admissions rule takes everyone above the black line. Since the Blues are shifted up relative to the Reds, they are admitted at a disproportionately higher rate. 

This is the kind of thing that happens all the time: whenever there are two populations that have different feature distributions, learning a single classifier (that is prohibited from discriminating based on population) will fit the bigger of the two populations, simply because they contribute more to average error. Depending on the nature of the distribution difference, this can be either to the benefit or the detriment of the minority population. And not only does this not involve any explicit human bias, either on the part of the algorithm designer or the data gathering process, it is exacerbated if we artificially force the algorithm to be group blind. Well intentioned "fairness" regulations prohibiting decision makers form taking sensitive attributes into account can actually make things less fair and less accurate at the same time.

Thursday, January 10, 2019

2019 SIGecom Dissertation Award: Call for Nominations

Dear all,

Please consider nominating graduating Ph.D. students for the SIGecom Dissertation Award.  If you are a graduating student, consider asking your adviser or other senior mentor to nominate you.

Nominations are due on February 28, 2019.  This award is given to a student who defended a thesis in 2018.  It is a prestigious award and is accompanied by a $1500 prize.  In the past, the grand prize has been awarded to:

2017: Aviad Rubinstein, "Hardness of Approximation Between P and NP"
2016: Peng Shi, "Prediction and Optimization in School Choice"
2015: Inbal Talgam-Cohen, "Robust Market Design: Information and Computation "
2014: S. Matthew Weinberg, "Algorithms for Strategic Agents"
2013: Balasubramanian Sivan, "Prior Robust Optimization"

And the award has had seven runner-ups: Rachel Cummings, Christos Tzamos, Bo Waggoner, James Wright, Xi (Alice) Gao, Yang Cai, and Sigal Oren.  You can find detailed information about the nomination process at: We look forward to reading your nominations!

Your Award Committee,

Renato Paes Leme
Aaron Roth (Chair)
Inbal Talgam-Cohen