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Opening the Black Box

Cynthia Rudin is a professor of computer science and electrical and computer engineering at Duke University, and an outspoken critic of using black box algorithms for high-stakes decisions.

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Show Notes

Links to Cynthia Rudin's code can be found at users.cs.duke.edu/~cynthia/code.html, and Rebecca Wexler's fascinating story on the case of Glenn Rodriguez may be read in full at Washington Monthly.

Transcript

M. Volborth: This is Rate Of Change, a podcast from Duke Engineering dedicated to the ingenious ways that engineers are solving society's toughest problems. I'm Miranda Volborth.

M. Volborth: Algorithms run constantly in the backgrounds of our lives, monitoring our online presence, scanning our environment, predicting our behaviors, and they can affect our lives in profound ways. They're used in finance, healthcare, and even in the criminal justice system. Yet we allow them to make these high stakes decisions in secret, hidden within black boxes.

Cynthia Rudin: A black box predictive model is a mathematical formula that's too complicated for a human to understand or a formula that's proprietary, meaning that it is hidden by a company. There are a lot of problems with black boxes. Sometimes they depend on variables you don't want them to and you don't know about it, because you can't really tell what's in your black box.

M. Volborth: This is Cynthia Rudin, an Associate Professor of Computer Science and Electrical and Computer Engineering at Duke University. Cynthia runs the university's Prediction Analysis Lab and she is an expert at making projections and predictions from huge complicated data sets. Along the way, she's become interested in the potential pitfalls of black box algorithms.

M. Volborth: Where would someone like me come across black box algorithms in my day-to-day life?

Cynthia Rudin: You'd be surprised how often you you come across black box machine learning algorithms. Whenever you go to the go to the internet and you look at a website that sells products, they're using black box AI. You can see why a company that sells products wants to use black box algorithms, right? There are tons of factors to consider for whether to recommend you a product.

Cynthia Rudin: They would look at your purchase history, the number of times you've looked at the product before, what time of day you look at what season it is, what your friends are saying about it, what the weather's doing. All of these things might contribute to whether you get a recommendation to buy a pair of rain boots or to stream a new TV show.

Cynthia Rudin: And of course these algorithms are proprietary and the black box models keep it safe from competitors. The problem is that these secret algorithms aren't used just to make low-stakes decisions. People are using these algorithms for high-stakes decisions— like, key events that deeply affect people. And they think that you can just apply the same algorithms and strategy that you can for web search. Like for web search, if you get an incorrect recommendation, who cares? But there've been a lot of situations lately where bad stuff has happened because these algorithms are not transparent.

Cynthia Rudin: Like, for instance, whether or not you get denied credit. And then, also things like air quality index. It would be quite bad if an air quality index said that everything was fine outside, and it's beautiful, and your car's were covered in a layer of ash, which actually did happen during the California wildfires recently.

News reel: [It just sits there like the house guest who refuses to leave. An ominous blanket made up a vehicle, exhaust, ozone, and especially smoke from wildfires.]

News reel: [I just noticed that it's really smoky and as difficult to breathe.]

News reel: [I can just smell it and I would start sneezing and coughing really badly.]

M. Volborth: Despite clear evidence to the contrary, some widgets were telling residents in central California that the air was good. The ideal air quality for outdoor activities. Even as physicians were urging people to stay indoors. That's a big problem for people with say asthma, or other respiratory problems, but that's only one example of a black box model being used to make high-stakes decisions. There are others. Here's Cynthia Rudin again.

Cynthia Rudin: The criminal justice system has a lot of black box algorithms that are being used in it right now and they're being used for things like bail decisions and parole decisions, and that's causing a lot of problems. Like, people can be denied parole without even knowing why.

M. Volborth: Let's talk about that a little bit more. The term COMPAS comes up a lot in these conversations about the justice system. Can you explain what COMPAS is?

Cynthia Rudin: So COMPAS is a black box model for predicting recidivism, which is like whether somebody's going to be arrested within a certain period of time and COMPAS is, it's proprietary, nobody knows exactly what's inside of it and it's being used sort of widely throughout the U.S. Justice system. There was this famous case, the case of Glen Rodriguez, he was up for parole...

M. Volborth: Glenn Rodriguez, an inmate at the Eastern Correctional Facility in upstate New York, was denied parole in spite of being a model prisoner. The parole board referenced his quote, "High COMPAS risk score for prison misconduct", unquote in its denial. But Rodriguez hadn't had a disciplinary action in 10 years. Rodriguez suspected something was wrong with the model and according to Rebecca Wexler, an attorney and former Yale Public Interest Fellow at the Legal Aid Society of New York city, Rodriguez began a process of checking his own survey responses against those of other prisoners and looking at the outcomes. He suspected that on his survey, the answer to question number 19 regarding disciplinary action had been entered incorrectly.

Cynthia Rudin: Now, I don't think his case is unique. I think these scores are miscalculated all the time because if you have to enter 137 numbers into a model, you know the chances are that you're going to enter at least one of those numbers in wrong. So these miscalculations, we think, we have a lot of evidence that they appear fairly often.

M. Volborth: What are the most important factors out of those 137?

Cynthia Rudin: Well, I'm not sure exactly, because the model's not transparent, so I can't figure that out. One of the questions they ask in the COMPAS scoring sheet is, "Well, has your mother ever been arrested?" And why does the company need to know that? Right? I'm guessing that doesn't help you predict recidivism and in fact I'm quite sure that it doesn't help them predict recidivism, because our models can predict recidivism just as well as they can, and we don't have whether or not anybody's mother was arrested.

M. Volborth: What do you mean your models?

Cynthia Rudin: Well, we develop a lot of transparent models in my lab, so models that are really easy to double check and really easy to evaluate. These models, they don't even require a calculator to compute. You can just compute them in your head. So we developed a model to compete with COMPAS from a dataset that comes from Broward County, Florida. And the model--I'm going to tell you the whole model, it's a machine learning model. It's going to sound like a rule of thumb, but it's actually a machine learning model and it's just as accurate as COMPAS for predicting whether somebody's going to be arrested within two years.

Cynthia Rudin: Okay, so here's the model. If your age is 18 to 20 years old, or if you're 21 to 22 years old and have two to three prior offenses, or if you have at least three prior offenses, then you're likely to be arrested in the next two years, and otherwise not. Okay? That's the whole machine learning model, not a rule of thumb, that's the whole model.

M. Volborth: And that's just as accurate as the model with 137 factors?

Cynthia Rudin: According to our data from Florida.

M. Volborth: So why, if your tiny model works just as well and it provides transparency, why is the black box algorithm being used?

Cynthia Rudin: Yeah, that's a really good question. It's complicated. So there's sort of a widespread belief that because a model is a black box, it's more accurate. And that as far as I can tell is wrong. Right? I've worked on many different applications in medical care, in energy, in credit risk, and in criminal recidivism, and we've never found an application where we really needed a black box. We could always use an interpretable model for a high stakes decision problem.

Cynthia Rudin: But I think part of it is also the history of the field of machine learning. In 2012 there was a big splash where these deep learning algorithms did really well for a benchmark computer vision task and so since that time, people really thought you needed... They said, "Oh great, we can do image classification better than we can do it before", and then people got really excited about these black box algorithms and then they started applying them to these high stakes decisions without really thinking about the ramifications about doing that.

Cynthia Rudin: Very often we don't train our computer scientists in the right topics. We don't train them in basic statistics for instance. We don't train them in ethics, so they don't know. They developed this technology without worrying about what it's used for and that's a problem.

M. Volborth: So that benchmark tasks in machine learning kind of triggered a wave of black box applications. What about a benchmark task for interpretable transparent models?

Cynthia Rudin: There hasn't been a real benchmark in interpretability, but there's been one in explainability which is related. So explainability scares me. Explainability is where you're supposed to take a black box model and explain it. Okay? So there was a competition in 2018 the company FICO, The Fair Issac Corporation, provided a dataset to just really freely available and then people could compete to provide... They wanted everyone to provide a black box and then explain it afterward.

Cynthia Rudin: So my team got a hold of this data set and we looked at it and we said, "I don't think you need a black box for this data set." So what we did was we completely violated the rules and provided a model that was interpretable, fully interpretable, so no black box at all. And we sent our model off to the judges. I think they had trouble evaluating the competition--like, the organizers of the competition didn't know how to evaluate explainability or interpretability. But we did get an email a few weeks ago saying that FICO was awarding us with the FICO recognition prize for our work on the challenge, so we were thrilled about that.

M. Volborth: Are things looking up or looking down for opening up the black box?

Cynthia Rudin: Well, at the moment I would say things are looking down. In fact California for instance now is going to a no-bail system where an algorithm makes decisions, and I believe that they're thinking about using COMPAS for it. I mean, these algorithms--there are a lot of companies that make profits off of selling these black box models, and these companies have been fairly successful in getting their models to penetrate the justice system.

Cynthia Rudin: So I would say that at the moment the black boxes are succeeding, but I'm hoping that if we get the word out there that these transparent models are just as accurate as the black box models, that people will start using the transparent ones instead.

M. Volborth: How can researchers like you at universities like Duke help?

Cynthia Rudin: Well, if the criminal justice system wants a model for recidivism prediction, they should be turning to academics to create that model, or to to charities, rather than a company. Because we can do it just as well, and we'll give it away for free.

M. Volborth: What's the outcome you're hoping for?

Cynthia Rudin: I'm hoping that people realize the risks in explainable models and that they don't actually need black boxes at all. They can use models that are completely interpretable.

Cynthia Rudin: I would like to see a system in which no black box model is used for a high stakes decision unless there is no equally accurate, interpretable model. Now, I'm pretty sure that for all of the high stakes problems I've worked done, there's no need for a black box model at all in that case. So I'm hoping that if we govern interpretability before black boxes, then we will have only interpretable models.

M. Volborth: Rudin's team made it's competition entry for the FICO challenge publicly available. Anyone who wants to can go onto the website and explore the model through its web interface to understand how it works. She also makes all her code available. So if you're a machine learning expert listening to this podcast, you can get her code and use it to build interpretable models for your own datasets. You can find that address in our show notes.

M. Volborth: Thanks for listening. Please subscribe for updates from Duke Engineering, and if you learned something from this podcast, please share it with others.

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