Fairification: Making Unfair Programs Fair

May 1, 2017

Over the past year, we have been exploring the notion of algorithmic fairness from a PL/verification perspective. Today I’m going to talk about a new paper we have that is appearing at CAV 2017: Repairing Decision-making Programs under Uncertainty, with Samuel Drews and Loris D’Antoni.

Algorithmic fairness

With software rapidly overtaking sensitive decision-making processes, like policing and sentencing, many people have been very concerned with unfairness in automated decision-making. The past year or so has seen lots of attention in this space, for example:

And this is just part of the popular coverage of algorithmic fairness. In our unpopular (academic) world, the action has been primarily in the machine learning arena, where researchers have been studying ways to learn fair classifiers, for certain definitions of fairness.

Unfair programs? We’ll fairify them for you!

While algorithmic unfairness is an alarming issue with potentially large-scale negative effects, I believe that the move to algorithmic decision-making has a silver lining: We can rigorously reason about programs, debug them, and fix them.

In our work, we went after the following problem: Say we’re given a program that decides whether to hire a job applicant that is unfair (more on what that means in a bit). By program I mean a piece of code that is maybe a machine learning model, a script distilled from the wisdom of a VP, an SQL query written by a data scientist, whatever! Our view is that such a program is probably not designed to be blatantly unfair. So what we’d like to do is to tweak it a little bit and make it fair—I like to (unofficially) call this process fairification.

The main question that I’ve avoided so far is how do you formalize fairness?! This is a deep philosophical problem. But computer scientists love to formalize the unformalizable! Recent work in the area has proposed several definitions. Let’s look at the one from Feldman et al., which formalizes the 80–20 rule of thumb from the Equality of Employment Commission here in the US:

The intuition is simple: the probability of hiring from the minority applicant pool is at least 80% that of hiring from the non-minority pool—assuming a binary split of the population.

OK, great. We’ve fully formalized fairness, leaving no room for philosophy.

For an ultra-simple illustration, say the program we have is the following: It only takes one thing about the applicant, the rank of the college they attended. If the applicant attended a top-ten school, then, good for them, they get hired!

def hire(urank):
   return 1 <= urank <= 10

Is this program fair? Well, it depends on the population! We will represent the population as a probabilistic model: 10% of the population are minorities; non-minorities go to schools ranked 10 on average; minorities go to schools ranked 15 on average (here is 1 for minority and 0 for non-minority).

With this population model, this program is unfair; on the above fairness definition, the ratio evaluates to ~0.6. How do we fix it?

Our approach proceeds like this: First, we characterize a class of programs using a sketch (I talked about sketches in the last post). One possible sketch here is the following, where ?? are unknowns.

def hire(urank):
   return ?? <= urank <= ??

Essentially, the sketch characterizes a family of programs (ML people call this a hypothesis class). In this case, we’ve knocked out the constants in the program and we’re hoping to replace them with new ones to make it fair. The same idea can be extended to not only constants, but also instructions and branching. The sketch encodes our repair model, the various ways in which we can tweak the original program.

Now, we want to find a completion of this sketch such that

The idea is that we want to give the program a small nudge to make it fair. One possible completion is the following:

def hire(urank):
   return 1 <= urank <= 15

This program happens to be fair, per the above definition, and is semantically close to the original program. In a sense, we kept increasing the upper bound on the college ranking until we got a fair program. Our tool would find such completion.

Before I discuss how we actually do the fairifcation, I have to state that I do not claim this is the best debiasing of our program or that that fairness conditions I used in this example is the most desirable in this setting. I simply intended this combination for illustration.

Fairification with program synthesis

The approach we used is a new method for program synthesis. Most work in the program synthesis literature attempts to find a program that satisfies a specification. Here, our specification is a probabilistic one. Our technique uses SMT solvers, fancy data structures, and a sprinkle of statistical learning theory for good measure. It traverses the space of programs and finds fair programs that are close to the original unfair one. Our technique can take an arbitrary fairness definition in a syntactic language that is expressions over probabilities of events, like the 80–20 rule we saw above.

For details, I invite you to read our paper. For a quick synthesis primer, I invite you to read an earlier post.

Looking forward

Most of the recent works have focused on unfairness in automation of bureaucratic processes, like loans, hiring, and others. But fairness and unfairness extend to any other area where we interact with software. In the near future, it appears that we’ll be interacting with robots, self-driving cars, and other autonomous agents. What does fairness mean there?

I believe there’s lots of interesting and important work to be done by the programming languages and verification communities on the issue of fair programs: How do we debug unfair programs? There are lots of algorithms introduced for fair classification; can we build verified implementations? Can we build programming-language support for reasoning about fairness in data analysis environments?

Fairification: Making Unfair Programs Fair - May 1, 2017 - Aws Albarghouthi