Yesterday I gave a super duper high level 12 minutes presentation
about some issues of bias in AI. I should emphasize (if it's not
clear) that this is something I am
not an expert in; most of
what I know is by reading great papers by other people (there is a
completely non-academic sample at the end of this post). This blog
post is a variant of that presentation.
Structure: most of the images below are prompts for talking
points, which are generally written below the corresponding image. I
think I managed to link all the images to the original source (let me
know if I missed one!).
Automated Decision
Making is Part of Our Lives
To me, AI is largely the study of
automated decision making,
and the investment therein has been growing at a dramatic rate.
I'm currently teaching undergraduate artificial intelligence. The
last time I taught this class was in 2012. The amount that's changed
since there is incredible. Automated decision making is now a part of
basically everyone's life, and will only be more so over time. The
investment is in the billions of dollars per year.
Things Can Go Really
Badly
If you've been paying attention to headlines even just over the
past year, the number of high stakes settings in which automated
decisions are being made is growing, and growing into areas that
dramatically affect real people's real life, their well being, their
safety, and their rights.
This includes:
This is obviously just a sample of some of the higher profile work
in this area, and while all of this is work in progress, even if
there's no impact today (hard to believe for me) it's hard to imagine
that this isn't going to be a major societal issue in the very near
future.
Three (out of many)
Source of Bias
For the remainder, I want to focus on three specific ways that
bias creeps in. The first I'll talk about more because we understand
it more, and it's closely related to work that I've done over the
past ten years or so, albeit in a different setting. These three are:
-
data collection
-
objective function
-
feedback loops
Sample Selection Bias
The standard way that machine learning works is to take some
samples from a population you care about, run it through a machine
learning algorithm, to produce a predictor.
The magic of statistics is that if you then take new samples from
that same population, then, with high probability, the predictor will
do a good job. This is true for basically all models of machine
learning.
The problem that arises is when your population samples are from a
subpopulation (or different population) for those on which you're
going to apply your predictor.
Both of my parents work in marketing research and have spent a lot
of their respective careers doing focuses groups and surveys. A few
years ago, my dad had a project working for a European company that
made skin care products. They wanted to break into the US market, and
hired him to conduct studies of what the US population is looking for
in skin care. He told them that he would need to conduct four or five
different studies to do this, which they gawked at. They wanted one
study, perhaps in the midwest (Cleveland or Chicago). The problem is
that skin care needs are very different in the southwest (moisturizer
matters) and the northwest (not so much), versus the northeast and
southeast. Doing one study in Chicago and hoping it would generalize
to Arizona and Georgia is unrealistic.
This problem is often known as
sample selection bias in the
statistics community. It also has other names, like covariate shift
and domain adaptation depending on who you talk to.
One of the most influential pieces of work in this area is the
1979 Econometrica paper by James Heckman, for which he won the 2000
Nobel Prize in economics. He's pretty happy about that! If you
haven't read
this
paper, you should: it's only about 7 pages long, it's not that
difficult, and you won't believe the footnote in the last section.
(Sorry for the clickbait, but you really should read the paper.)
There's been a ton of work in machine learning land over the past
twenty years, much of which builds on Heckman's original work. To
highlight one specific paper: Corinna Cortes is the Head of Google Research New York and has had
a number of excellent papers on this topic over the past ten years.
One in particular is
her
2013 paper in Theoretical Computer Science (with Mohri) which
provides an amazingly in depth overview and new algorithms. Also a
highly recommended read.
It's Not Just that Error
Rate Goes Up
When you move from one sample space (like the southwest) to
another (like the northeast), you should first expect error rates to
go up.
Because I wanted to run some experiments for this talk, here are
some simple adaptation numbers for predicting sentiment on Amazon
reviews (data due to
Mark
Dredze and colleagues). Here we have four domains (books, DVDs,
electronics and kitchen appliances) which you should think as
standins for the different regions of the US, or different
demographic qualifiers.
The figure shows error rates when you train on one domain
(columns) and test on another (rows). The error rates are normalized
so that we have ones on the diagonal (actual error rates are about
10%). The off-diagonal shows how much additional error you suffer due
to sample selection bias. In particular, if you're making predictions
about kitchen appliances and
don't train on kitchen appliances,
your error rate can be more than two times what it would have been.
But that's not all.
These data sets are balanced: 50% positive, 50% negative. If you
train on electronics and make predictions on other domains, however,
you get different false positive/false negative rates. This shows the
number of test items predicted positively; you should expect it to be
50%, which basically is what happens in electronics and DVDs.
However, if you predict on books, you
underpredict positives;
while if you predict on kitchen, you
overpredict positives.
So not only do the error rates go up, but the way they are
exhibited chances, too. This is closely related to issues of
disparate impact, which have been studied recently by many people,
for instance by
Feldman,
Friedler, Moeller, Scheidegger and Venkatasubramanian.
What Are We Optimizing
For
One thing I've been trying to get undergrads in my AI class to
think about is what are we optimizing for, and whether the thing
that's being optimized for is what is best for us.
One of the first things you learn in a data structures class is
how to do graph search, using simple techniques like breadth first
search. In intro AI, you often learn more complex things like A*
search. A standard motivating example is how to find routes on a map,
like the planning shown above for me to drive from home to work
(which I never do because I don't have a car and it's slower than
metro anyway!).
We spend a lot of time proving optimality of algorithms in terms
of shortest path costs, for fixed costs that have been given to us by
who-knows-where. I challenged my AI class to come up with features
that one might use to construct these costs. They started with
relatively obvious things: length of that segment of road, wait time
at lights, average speed along that road, whether the road is
one-way, etc. After more pushing, they came up with other ideas, like
how much gas mileage one gets on that road (either to save the
environment or to save money), whether the area is “dangerous”
(which itself is fraught with bias), what is the quality of the road
(ill-repaired, new, etc.).
You can tell that my students are all quite well behaved. I then
asked them to be evil. Suppose you were an evil company, how might
you come up with path costs. Then you get things like: maybe
businesses have paid me to route more customers past their stores.
Maybe if you're driving the brand of car that my company owns or has
invested it, I route you along better (or worse) roads. Maybe I route
you so as to avoid billboards from competitors.
The point is: we don't know, and there's no strong reason to a
priori assume that what the underlying system is optimizing for is my
personal best interest. (I should note that I'm definitely not saying
that Google or any other company is doing any of these things: just
that we should not assume that they're not.)
A more nuanced example is that of a dating application for, e.g.,
multi-colored robots. You can think of the color as representing any
sort of demographic information you like: political leaning (as
suggested by the red/blue choice here), sexual orientation, gender,
race, religion, etc. For simplicity, let's assume there are way more
blue robots than others, and let's assume that robots are at least
somewhat homophilous: they tend to associate with other similar
robots.
If my objective function is something like “maximize number of
swipe rights,” then I'm going to want to disproportionately show
blue robots because, on average, this is going to increase my
objective function. This is especially true when I'm predicting
complex behaviors like robot attraction and love, and I don't have
nearly enough features to do anywhere near a perfect matching.
Because red robots, and robots of other colors, are more rare in my
data, my bottom line is not affected greatly by whether I do a good
job making predictions for them or not.
I highly recommend reading
Version
Control, a recent novel by Dexter Palmer. I especially recommend
it if you have, or will, teach AI. It's fantastic.
There is an interesting vignette that Palmer describes (don't
worry, no plot spoilers) in which a couple engineers build a dating
service, like Crrrcuit, but for people. In this thought exercise, the
system's objective function is to help people find true love, and
they are wildly successful. They get investors. The investors realize
that when their product succeeds, they lose business. This leads to a
more nuanced objective in which you want to match
most
people (to maintain trust), but not perfectly (to maintain
clientèle). But then, to make money, the company starts selling its
data to advertisers. And different individuals' data may be more
valuable: in particular, advertisers might be willing to pay a lot
for data from members of underrepresented groups. This provides
incentive to actively do a worse
job than usual on such clients. In the book, this thought exercise
proceeds by human reasoning, but it's pretty easy to see that if one
set up, say, a reinforcement learning algorithm for predicting
matches that had long term company profit as its objective function,
it could learn something similar and we'd have no idea that that's
what the system was doing.
Feedback Loops
Ravi Shroff recently visited the CLIP lab and talked about his work
(with Justin Rao and Shared Goel) related to stop and frisk policies in New York. The setup here is that the “stop and frisk”
rule (in 2011, over 685k people were stopped; this has subsequently been declared unconstitutional in New York) gave police
officers the right to stop people with much lower thresholds than probable cause, to try
to find contraband weapons or drugs. Shroff and colleagues focused on
weapons.
They considered the following model: a
police officer sees someone behaving strangely, and decide that they
want to stop and frisk that person. Before doing so, they enter a few
values into their computer, and the computer either gives a thumbs up
(go ahead and stop) or a thumbs down (let them live their life). One
question was: can we cut down on the number of stops (good for
individuals) while still finding most contraband weapons (good for
society)?
In this figure, we can see that
if the system thumbs downed 90% of stops (and therefore only 10% of
people that police would have
stopped get stopped), they are still able to recover about 50% of the
weapons. With stopping only
about 1/3 of individuals, they are able to recover 75% of weapons.
This is a massive
reduction in privacy violations while still successfully keeping the
majority of weapons off the streets.
(Side note: you might worry about sample selection bias here,
because the models are trained on people that the policy did actually
stop. Shroff and colleagues get around this by the assumption I
stated before: the model is only run on people who policy have
already decided are suspicious and would have stopped and frisked
anyway.)
The question is: what happens if and when such a system is
deployed in practice?
The issue is that policy officers, like humans in general, are not
stationary entities. Their behavior changes over time, and it's
reasonable to assume that their behavior would change when they get
this new system. They might feed more people into the system (in
“hopes” of thumbs up) or feed fewer people into the system
(having learned that the system is going to thumbs down them anyway).
This is similar to how the sorts of queries people issue against web
search engines change over time, partially because we learn to use
the systems more effectively, and learn what to not consider asking a
search engine to do for us because we know it will fail.
Now, once we've (hypothetically) deployed this system, it's
collecting its own data, which is going to be fundamentally different
from the data is was originally trained one. It can continually
adapt, but we need good technology for doing this that takes into
account the human behavior of the officers.
Wrap Up and Discussion
There are many things that I didn't
touch on above that I think are nonetheless really important. Some
examples:
-
All the example “failure”
cases I showed above have to do with race or (binary) gender. There
are other things to consider, like sexual orientation, religion,
political views, disabilities, family and child status, first
language, etc. I tried and failed to find examples of such things,
and would appreciate pointers. For instance, I can easily imagine
that speech recognition error rates skyrocket when working for users
with speech impairments, or with non-standard accents, or who speak
a dialect of English that not the “status quo academic English.”
I can also imagine that visual tracking of people might fail badly
on people with motor impairments or who use a wheelchair.
-
I am particularly concerned
about less “visible” issues because we might not even know. The
standard example here is: could a social media platform sway an
election by reminding people who (it believes) belong to a
particular political party to vote? How would we even know?
-
We need to start thinking about
qualifying our research better with respect to the populations we
expect it to work on. When we pick a problem to work on, who is
being served? When we pick a dataset to work on, who is being left
out? A silly example is the curation of older datasets for object
detection in computer vision, which (I understand) decided on which
objects to focus on by asking five year old relatives of the
researchers constructing the datasets to name all the objects they
could see. As a result of socio-economic status (among other
things), mouse means the thing that attaches to your computer, not
the cute furry animal. More generally, when we say we've “solved”
task X, does this really mean task X or does this mean task X for
some specific population that we haven't even thought to identify
(i.e., “people like me” aka the
white
guys problem)? And does “getting more data” really solve the
problem---is more data always good data?
-
I'm at least as concerned with
machine-in-the-loop decision making as fully automated decision
making. Just because a human makes the final decision doesn't mean
that the system cannot bias that human. For complex decisions, a
system (think even just web search!) has to provide you with
information that helps you decide, but what guarantees do we have
that that information isn't going to be biased, either
unintentionally or even intentionally. (I've also heard that, e.g.,
in predicting recidivism, machine-in-the-loop predictions are worse
than fully automated decisions, presumably because of some human
bias we don't understand.)
If you've read this far, I hope you've
found some things to think about. If you want more to read, here are
some people whose work I like, who tweet about these topics, and for
whom you can citation chase to find other cool work. It's a highly
biased list.
-
Joanna Bryson (
@j2bryson), who
has been doing great work in ethics/AI for a long time and whose
work on bias in language has given me tons of food for thought.
-
-
-
Sorelle Friedler (
@kdphd), a
former PhD student here at UMD!, has done some of the initial work
on learning without disparate impact.
-
-
Hanna Wallach (
@hannawallach) is
the first name I think of for machine learning and computational
social science, and has recently been working in the area of
fairness.
