I was one of the organizers of the 2nd workshop on
Fairness, Accuracy and Transparency in Machine Learning (FATML) at ICML 2015, and in my alternate career as moderator of data mining panels, I moderated the closing panel. The panelists were
Fernando Diaz from MSR New York,
Sorelle Friedler from Haverford College,
Mykola Pechenizkiy from Eindhoven Instt. of Technology and
Hanna Wallach from UMass-Amherst and MSR.
While my original intent was to do a review of the panel, it became clear that the panel discussion touched on themes that were bubbling up throughout the day. So what follows is organized by panel questions, but weaves in discussion from outside the panel as well.
This year's workshop, unlike the one at NIPS 2014, had a bit more of a technical focus: we had some of the early researchers in fairness work from Europe and Japan give talks on their work. So as a counterweight, I thought I'd ask the panel to look beyond the presentations for the first question:
Question 1
What is one thing you think we are missing (or should be paying more attention to) in our current discussions of fairness and discrimination in terms of interaction with the social/policy/legal world OUTSIDE CS ?
Some themes that emerged:
...on the difference between Europe and the US
Mykola made an interesting observation based on his experience in educational data mining. Governments around Europe are very concerned about the use of student data (including health records and academic information) for any kind of data driven tools, and have severely regulated use of such data. As a result, the nascent field of educational data mining has been crippled by lack of access to data.
This is almost the opposite of the situation in the US, where data driven policy is the newest buzzword in town, and those of us who are interested in issues of fairness and transparency feel like we're constantly on the outside looking in (though attention to these issues is increasing).
...connecting to other communities
It's been clear from the beginning that discourse on fairness and transparency in machine learning must draw on the corresponding discourses in society at large. Which means that before we can start solving problems, we have to understand what the problems really are. This came through very strongly in the discussions. To paraphrase one of the panelists, "Computer science likes to solve problems, and that's the problem !" (also known as "slap a metric on it and optimize").
So what are the different communities we should be connecting to, and how ?
a) Connecting with social science
A major concern is the "prediction vs understanding" problem. For the most part, machine learning is about prediction: you classify, label, clustering, regress, rank and so on. But in the study of society and the dynamics of human interactions, the goal is not just to predict how humans might behave, but to
understand their behavior. Which is to say, data analysis (or even fairness-aware analysis) has to be the first step in a larger conversation, rather than the last one.
While I don't think this issue is specific to fairness and transparency, it plays a role in understanding the sources of inequality and discrimination. It's not enough to to detect examples of bias: what must happen next is an investigation of why the bias is happening.
(ed:
personally, while I understand this concern, I don't think it's necessarily something computer scientists need to prioritize. This is after all what the social sciences do, and it doesn't make sense for us as computer scientists to merely try to acquire those skills. I think we need to be aware of the deeper issues of understanding a domain, but we also have strengths that we bring to the table and I'll say more about that later)
"galaxies don't care how they are studied, but people do"
Another point that was made over and over is that issues of fairness and bias are not abstract: they affect actual people. Keeping the human in focus is important for the ethical underpinning of what we do, and even how we might design experiments.
b) connecting with journalists
Nick Diakopoulos gave a talk on "algorithmic accountability" in journalism. In addition to talking about what made research on fairness newsworthy:
- discriminatory/unfair practices
- mistakes that denies a service
- censorship
- activities that break the law or social norms
- false prediction
he made the strong argument that (government) legitimacy comes from transparency, and talked about what that might entail in the age of data driven policy, including transparency involving data collection, the algorithms used, the inferences generated, and the humans involved in the process.
(
ed: I don't think that our demands on transparency should be limited to government entities: the sad fact is that at least in the US, much of what would be considered basic internet infrastructure is controlled by private corporations, and they should be held to similar standards: if not for legitimacy, at least for fairness)
c) connecting with the law
Our fearless leader
Solon Barocas made a number of interesting observations on the connection between algorithmic fairness and the law, all the while disclaiming IANAL :). But his point (which he's made before) is worth repeating. One of the things that computer science can do well is make precise concepts that might be defined vaguely or only indirectly through case law. And then we can get to work teasing out the relationships between different concepts (both abstractly and computationally). Indeed, the idea of a "reduction" between concepts in fairness might be one of the most useful things that computer science can uniquely contribute.
It's clear we're in a "let a thousand definitions bloom" phase in fairness research. And it's interesting to see the different reactions to this: on the social science side, there appears to be some nervousness that we're "playing games with math", but from Solon's comments this doesn't seem like a bad thing as long as we're also trying to connect the definitions together.
Question 2
In your view, what’s the next most pressing question we should be asking (limited to INSIDE CS to distinguish from the previous question) ?
...better definitions
It was very clear from the discussion that we need broader definitions of F-A-T beyond what's mathematically plausible. One particular example that's reminiscent of the metrics for privacy: There's a notion of "utility": how much can we make the data or the task "fair" without changing the underlying results produced by the "unfair" data/algorithm. The problem is that utility itself is not very well defined. Firstly, you might be benefiting from discriminatory policies, so your perceived "utility" itself is a problem. Trying to maintain this defeats the purpose of fairness. Secondly, even if this is not the case, the framing of the question as a tradeoff implies that these two notions are necessarily in opposition. That shortchanges the moral imperative of fairness and is different from the parallel situation in privacy. Finally, we measure utility in terms of classifier accuracy. But that's a very poor notion of overall task effectiveness. For example, is there a Bayesian perspective to bring to this ?
At any rate, since we are good at understanding tradeoffs in computer science, we should understand the different dimensions of the space of fairness preserving methods, rather than limiting ourselves to a one-dimensional false dichotomy of "fairness vs utility".
...better usable artifacts
Nick asked us the following question at the panel:
when a CEO or an agency head comes to us and asks "what should we do about this fairness stuff". what do we tell them ?
We didn't have a good response, and that was interesting. While we're beginning to explore the space of what's possible, we don't have clear examples of artifacts to hand over and say "use this".
As usual, the topic of benchmarking came up. I joke that when industry folks bring up the issue of benchmarking, I always ask "so where's the data" and they usually go very silent. But I do think there are useful data sets to be explored that come to us from the government. Some common data sets that get used are the US census data on salaries and a German data set on consumer credit. The entire data set from the Ricci court case is also available (even though it's tiny), and there are Bureau of Justice recidivism data sets to play with.
Of course this goes against the imperative coming from the social scientists to look at specific domains and ask meaningful questions in that domain. And I think we need to look more at the literature on fairness and bias over the decades and extract data that people have studied.
...better problems
For the most part, researchers have been considering binary classification as the suspect task. But of course there are much more general tasks that we could be considering: what about unsupervised learning ? what about structured prediction ? Is there a way to define fairness when you don't have a simple binary response variable and binary attributes ?
One final question I asked was this:
Question 3
do we have to solve the causality problem in order to talk about fairness ?
This question was possibly not as well-posed as I would have liked, but it led to interesting discussions.
The law deals with intent, because the goal of the law is to assign responsibility. Algorithms are not agents and can't exhibit intent. Causality is a proxy for intent, in that if we can say that something caused something else, we can assign blame in a different way. In fact there were two talks at the workshop that talked about causality directly in the context of fairness.
But causality is a very hard problem. It's extremely subtle (if you doubt this, read through some of the examples Judea Pearl discusses in his
book), and extremely controversial: different camps have their own view of how to mechanize causal inference, and the battles there make frequentists and Bayesians look like life-long friends.
In the discussion that followed, it became clear that there were really two ways of thinking about causality as it relates to fairness. The first way is to think about the underlying causal mechanisms that might lead to otherwise innocent features leading to biased outcomes: that is, how might zip code correlate with racial identity for example. The second way, which is closer to what I had in mind, is to think about the behavior of an algorithm causally: the use of these inputs or this algorithm *caused* a particular decision to be made. This second idea is not as far-fetched as it seems:
some work in the database community has looked at trying to find which tuples "caused" a certain output to be generated from a query.
If you think it's not important to understand causality as it comes to automated methods,
you might not want to drive a self-driving car or fly a plane. But as Solon suggested in the discussion, one way of getting around causality is to think about negligence with respect to algorithms: can we design reasonable best practices for predictive tools and argue that a failure to use these methods is negligence ? The legal ramifications of these idea have been explored in the context of robotics (
article, and
response) but more work is yet to be done.
...back to narratives
Another comment by Nick D, again connecting to the journalism perspective: narratives and story telling are a powerful way to explain the results of data mining. I haven't talked much about interpretability, which is an important part of the larger discussion of transparency and accountability. But one way to communicate the results of (say) a fairness audit would be to provide a human-interpretable linkage between the problematic attributes being used for prediction and the protected attribute. For more on this, see Michael Nielsen's very timely new
Quanta article on machine-generated explanations.
It's clear from all the discussion that there's a lot of work to be done and a small but active community of people interested in pushing these issues forward. Fairness, and algorithmic bias, are
hot topics in the news nowadays, and it's a good time to take advantage of this burst of interest.
