Weapons of Math Destruction – A Data Scientist’s Guide to Disarmament

I’ve had this book on pre-order since spring and it finally arrived on Friday. I subsequently devoured it over the weekend.

Long awaited Weapons of Math Destruction by Cathy O'Neil

The book lays out a clear and compelling case for how data-driven algorithms can become — in contrast to their promise of amoral objectivism — efficient means for reproducing and even exacerbating social inequalities and injustices. From predictive policing and recidivism risk models to targeted marketing for predatory loans and for-profit universities, O’Neil explains how to recognize WMDs by 3 distinct features:

  1. The model is either hidden, or opaque to the individuals affected by its calculations, restricting any possibility of seeking recourse against – or understanding of – its results or conclusions.
  2. The model works against the subject’s interest (eg. it is unfair).
  3. The model scales, giving it the opportunity to negatively affect a very large segment of the population.

The taxonomy provides a simple framework for identifying WMDs in the wild. However, importantly for data scientists and other data practitioners, it forms a checklist (or rather an anti-checklist) to keep in mind when developing models that will be deployed into the real world. As data scientists, many of us are strongly incentivized to achieve feature 3, and doing so only makes it increasingly important to be constantly questioning the degree to which our models could fall victim to features 2 and 1.

Feature 2, as O’Neil lays out, can occur despite the best intentions of a model’s creators. This can (and does!) happen in two ways: First, when a modeler seeks to create an objective system for rating individuals (say, for acceptance to a prestigious university, or for a payday loan), the data used to build the model is already encoded with the socially constructed biases of the conditions under which it was generated. Even when attempting to exclude potentially bias-laden factors such as race or gender, this information seeps into the model nonetheless via correlations to seemingly benign variables such as zip codes or the makeup of a subject’s social connections.

Second, when the outcome of the model results in the reinforcement of the unjust conditions from which it was created, a negative feedback loop is created. Such a negative feedback loop is particularly present and pernicious in the use of recidivism risk models to guide sentencing decisions. An individual may be labeled as high risk due not to qualities of the individual himself, but his circumstances of living in a poor, high crime neighborhood. Being incarcerated based on the results of this model renders him more likely to end up back in that neighborhood, subject to continued poverty and disproportionate policing. Thus the model has set up the conditions to fulfill its own prediction.

As machine learning algorithms become more and more accurate at a variety of tasks, their inner workings become harder and harder to understand. The trend will make it increasingly difficult to avoid feature 1 of the WMD taxonomy. Current advanced techniques like deep learning are creating models that are remarkably performant, yet not fully understood by the researchers creating them, much less the individuals affected by their results. In light of this, we need to think carefully as data scientists about how to communicate these models with as much transparency as possible. How to do so remains an open question. But the internal ‘black box’ nature of these algorithms does not obviate our responsibility to disclose exactly what input data went into a given model, what assumptions were made of that data, and on what criteria the model was trained.

Overall, WMD provides an incredibly important framework for thinking about the consequences of uncritically applying data and algorithms to people’s lives. For those of us, like O’Neil herself, who make our living using mathematics to create data-driven algorithms, taking to heart the lessons contained in Weapons Of Math Destruction will be our best defense against unwittingly creating the bomb ourselves.

Speaking at DataPhilly February 2016

The next DataPhilly meetup will feature a medley of machine-learning talks, including an Intro to ML from yours truly. Check out the speakers list and be sure to RSVP. Hope to see you there!

Thursday, February 18, 2016

6:00 PM to 9:00 PM

Speakers:

  • Corey Chivers
  • Randy Olson
  • Austin Rochford

Corey Chivers (Penn Medicine)

Abstract: Corey will present a brief introduction to machine learning. In his talk he will demystify what is often seen as a dark art. Corey will describe how we “teach” machines to learn patterns from examples by breaking the process into its easy-to-understand component parts. By using examples from fields as diverse as biology, health-care, astrophysics, and NBA basketball, Corey will show how data (both big and small) is used to teach machines to predict the future so we can make better decisions.

Bio: Corey Chivers is a Senior Data Scientist at Penn Medicine where he is building machine learning systems to improve patient outcomes by providing real-time predictive applications that empower clinicians to identify at risk individuals. When he’s not pouring over data, he’s likely to be found cycling around his adoptive city of Philadelphia or blogging about all things probability and data at bayesianbiologist.com.

Randy Olson (University of Pennsylvania Institute for Biomedical Informatics):

Automating data science through tree-based pipeline optimization

Abstract: Over the past decade, data science and machine learning has grown from a mysterious art form to a staple tool across a variety of fields in business, academia, and government. In this talk, I’m going to introduce the concept of tree-based pipeline optimization for automating one of the most tedious parts of machine learning — pipeline design. All of the work presented in this talk is based on the open source Tree-based Pipeline Optimization Tool (TPOT), which is available on GitHub at https://github.com/rhiever/tpot.

Bio: Randy Olson is an artificial intelligence researcher at the University of Pennsylvania Institute for Biomedical Informatics, where he develops state-of-the-art machine learning algorithms to solve biomedical problems. He regularly writes about his latest adventures in data science at RandalOlson.com/blog, and tweets about the latest data science news at http://twitter.com/randal_olson.

Austin Rochford (Monetate):

Abstract: Bayesian optimization is a technique for finding the extrema of functions which are expensive, difficult, or time-consuming to evaluate. It has many applications to optimizing the hyperparameters of machine learning models, optimizing the inputs to real-world experiments and processes, etc. This talk will introduce the Gaussian process approach to Bayesian optimization, with sample code in Python.

Bio: Austin Rochford is a Data Scientist at Monetate. He is a former mathematician who is interested in Bayesian nonparametrics, multilevel models, probabilistic programming, and efficient Bayesian computation.

R style default plot for Pandas DataFrame

The default plot method for dataframes in R is to show each numeric variable in a pair-wise scatter plot. I find this to be a really useful first look at a dataset, both to see correlations and joint distributions between variables, but also to quickly diagnose potential strangeness like bands of repeating values or outliers.

From what I can tell, there are no builtins in the python data ecosystem (numpy, pandas, matplotlib) for this so I coded up a function to emulate the R behaviour. You can get it in this gist (feedback welcomed).

Here’s an example of it in action showing derived time-series features (12 hour rates of change) for some clinical variables.

plot_correlogram(df)

ts_features

One datavis for you, ten for me

Over the years of my graduate studies I made a lot of plots. I mean tonnes. To get an extremely conservative estimate I grep’ed for every instance of “plot\(” in all of the many R scripts I wrote over the past five years.


find . -iname "*.R" -print0 | xargs -L1 -0 egrep -r "plot(" | wc -l

2922

The actual number is very likely orders of magnitude larger as 1) many of these plot statements are in loops, 2) it doesn’t capture how many times I may have ran a given script, 3) it doesn’t look at previous versions, 4) plot is not the only command to generate figures in R (eg hist), and 5) early in my graduate career I mainly used gnuplot and near the end I was using more and more matplotlib. But even at this lower bound, that’s nearly 3,000 plots. A quick look at the TOC of my thesis reveals a grand total of 33 figures. Were all the rest a waste? (Hint: No.)

The overwhelming majority of the plots that I created served a very different function than these final, publication-ready figures. Generally, visualizations are either:

  • A) Communication between you and data, or
  • B) Communication between you and someone else, through data.

These two modes serve very different purposes and can require taking different approaches in their creation. Visualizations in the first mode need only be quick and dirty. You can often forget about all that nice axis labeling, optimal color contrast, and whiz-bang interactivity. As per my estimates above, this made up at the very least 10:1 of visuals created. The important thing is that, in this mode, you already have all of the context. You know what the variables are, you know what the colors, shapes, sizes, and layouts mean – after all, you just coded it. The beauty of this is that you can iterate on these plots very quickly. The conversation between you and the data can dialogue back and forth as you intrepidly explore and shine your light into all of it’s dark little corners.

In the second mode, you are telling a story to someone else. Much more thought and care needs to be placed on ensuring that the whole story is being told with the visualization. It is all too easy to produce something that makes sense to you, but is completely unintelligible to your intended audience. I’ve learned the hard way that this kind of visual should always be test-driven by someone who, ideally, is a member of your intended audience. When you are as steeped in the data as you most likely are, your mind will fill in any missing pieces of the story – something your audience won’t do.

In my new role as part of the Data Science team at Penn Medicine, I’ll be making more and more data visualizations in the second mode. A little less talking to myself with data, and a little more communicating with others through data. I’ll be sharing some of my experiences, tools, wins, and disasters here. Stay tuned!

Plot with ggplot2, interact, collaborate, and share online

Editor’s note: This is a guest post by Marianne Corvellec from Plotly. This post is based on an interactive Notebook (click to view) she presented at the R User Conference on July 1st, 2014.

Plotly is a platform for making, editing, and sharing graphs. If you are used to making plots with ggplot2, you can call ggplotly() to make your plots interactive, web-based, and collaborative. For example, see plot.ly/~ggplot2examples/211, shown below and in this Notebook. Notice the hover text!

img1

0. Get started

Visit http://plot.ly. Here, you’ll find a GUI that lets you create graphs from data you enter manually, or upload as a spreadsheet (or CSV file). From there you can edit graphs! Change between types (from bar charts to scatter charts), change colors and formatting, add fits and annotations, try other themes…

img2

Our R API lets you use Plotly with R. Once you have your R visualization in Plotly, you can use the web interface to edit it, or to extract its data. Install and load package “plotly” in your favourite R environment. For a quick start, follow: https://plot.ly/ggplot2/getting-started/

Go social! Like, share, comment, fork and edit plots… Export them, embed them in your website. Collaboration has never been so sweet!

img3

Not ready to publish? Set detailed permissions for who can view and who can edit your project.

img4

1. Make a (static) plot with ggplot2

Baseball data is the best! Let’s plot a histogram of batting averages. I downloaded data here.

Load the CSV file of interest, take a look at the data, subset at will:


library(RCurl)

online_data <-
 getURL("https://raw.githubusercontent.com/mkcor/baseball-notebook/master/Batting.csv")

batting_table <-
 read.csv(textConnection(online_data))

head(batting_table)

summary(batting_table)

batting_table <- 
 subset(batting_table, yearID >= 2004)

The batting average is defined by the number of hits divided by at bats:

batting_table$Avg <- 
 with(batting_table, H / AB)

You may want to explore the distribution of your new variable as follows:


library(ggplot2)
ggplot(data=batting_table)
 + geom_histogram(aes(Avg), binwidth=0.05)

# Let's filter out entries where players were at bat less than 10 times.

batting_table <- 
 subset(batting_table, AB >= 10)
hist <-
 ggplot(data=batting_table) + geom_histogram(aes(Avg),
 binwidth=0.05)
hist

We have created a basic histogram; let us share it, so we can get input from others!

2. Save your R plot to plot.ly

# Install the latest version 
# of the “plotly” package and load it

library(devtools)
install_github("ropensci/plotly")
library(plotly)

# Open a Plotly connection

py <-
 plotly("ggplot2examples", "3gazttckd7")

Use your own credentials if you prefer. You can sign up for a Plotly account online.

Now call the `ggplotly()` method:


collab_hist <-
 py$ggplotly(hist)

And boom!

img5

You get a nice interactive version of your plot! Go ahead and hover…

Your plot lives at this URL (`collab_hist$response$url`) alongside the data. How great is that?!

If you wanted to keep your project private, you would use your own credentials and specify:

py <- plotly()

py$ggplotly(hist,
 kwargs=list(filename="private_project",
 world_readable=FALSE))

3. Edit your plot online

 

Now let us click “Fork and edit”. You (and whoever you’ve added as a collaborator) can make edits in the GUI. For instance, you can run a Gaussian fit on this distribution:

img6

You can give a title, edit the legend, add notes, etc.

img7

You can add annotations in a very flexible way, controlling what the arrow and text look like:

img8

When you’re happy with the changes, click “Share” to get your plot’s URL.

If you append a supported extension to the URL, Plotly will translate your plot into that format. Use this to export static images, embed your graph as an iframe, or translate the code between languages. Supported file types include:

Isn’t life wonderful?

4. Retrieve your plot.ly plot in R

The JSON file specifies your plot completely (it contains all the data and layout info). You can view it as your plot’s DNA. The R file (https://plot.ly/~mkcor/305.r) is a conversion of this JSON into a nested list in R. So we can interact with it by programming in R!

Access a plot which lives on plot.ly with the well-named method `get_figure()`:

enhanc_hist <-
 py$get_figure("mkcor", 305)

Take a look:

str(enhanc_hist)

# Data for second trace
enhanc_hist$data[[2]]

The second trace is a vertical line at 0.300 named “Good”. Say we get more ambitious and we want to show a vertical line at 0.350 named “Very Good”. We overwrite old values with our new values:

enhanc_hist$data[[2]]$name <- "VeryGood"
enhanc_hist$data[[2]]$x[[1]] <- 0.35
enhanc_hist$data[[2]]$x[[2]] <- 0.35

Send this new plot back to plot.ly!

enhanc_hist2 <-
 py$plotly(enhanc_hist$data, 
 kwargs=list(layout=enhanc_hist$layout))

enhanc_hist2$url

Visit the above URL (`enhanc_hist2$url`).

How do you like this workflow? Let us know!

Tutorials are at plot.ly/learn. You can see more examples and documentatation at plot.ly/ggplot2 and plot.ly/r. Our gallery has the following examples:

img9

Acknowledgments

This presentation benefited tremendously from comments by Matt Sundquist and Xavier Saint-Mleux.

Plotly’s R API is part of rOpenSci. It is under active development; you can find it on GitHub. Your thoughts, issues, and pull requests are always welcome!

Online R and Plotly Graphs: Canadian and U.S. Maps, Old Faithful with Multiple Axes, & Overlaid Histograms

Guest post by Matt Sundquist of plot.ly.

Plotly is a social graphing and analytics platform. Plotly’s R library lets you make and share publication-quality graphs online. Your work belongs to you, you control privacy and sharing, and public use is free (like GitHub). We are in beta, and would love your feedback, thoughts, and advice.

1. Installing Plotly

Let’s install Plotly. Our documentation has more details.

install.packages("devtools")
library("devtools")
devtools::install_github("R-api","plotly")

Then signup online or like this:

library(plotly)
response = signup (username = 'yourusername', email= 'youremail')


Thanks for signing up to plotly! Your username is: MattSundquist Your temporary password is: pw. You use this to log into your plotly account at https://plot.ly/plot. Your API key is: “API_Key”. You use this to access your plotly account through the API.

2. Canadian Population Bubble Chart

Our first graph was made at a Montreal R Meetup by Plotly’s own Chris Parmer. We’ll be using the maps package. You may need to load it:

install.packages("maps")

Then:

library(plotly)
p <- plotly(username="MattSundquist", key="4om2jxmhmn")
library(maps)
data(canada.cities)
trace1 <- list(x=map(regions="canada")$x,
  y=map(regions="canada")$y)

trace2 <- list(x= canada.cities$long,
  y=canada.cities$lat,
  text=canada.cities$name,
  type="scatter",
  mode="markers",
  marker=list(
    "size"=sqrt(canada.cities$pop/max(canada.cities$pop))*100,
    "opacity"=0.5)
  )

response <- p$plotly(trace1,trace2)
url <- response$url
filename <- response$filename
browseURL(response$url)

In our graph, the bubble size represents the city population size. Shown below is the GUI, where you can annotate, select colors, analyze and add data, style traces, place your legend, change fonts, and more.

map1

Editing from the GUI, we make a styled version. You can zoom in and hover on the points to find out about the cities. Want to make one for another country? We’d love to see it.

map2

And, here is said meetup, in action:

plotly_mtlRmeetup

You can also add in usa and us.cities:

map3

3. Old Faithful and Multiple Axes

Ben Chartoff’s graph shows the correlation between a bimodal eruption time and a bimodal distribution of eruption length. The key series are: a histogram scale of probability, Eruption Time scale in minutes, and a scatterplot showing points within each bin on the x axis. The graph was made with this gist.

old_faithful

4. Plotting Two Histograms Together

Suppose you are studying correlations in two series (Popular Stack Overflow ?). You want to find overlap. You can plot two histograms together, one for each series. The overlapping sections are the darker orange, automatically rendered if you set barmode to ‘overlay’.

library(plotly)
p <- plotly(username="Username", key="API_KEY")

x0 <- rnorm(500)
x1 <- rnorm(500)+1

data0 <- list(x=x0,
  name = "Series One",
  type='histogramx',
  opacity = 0.8)

data1 <- list(x=x1,
  name = "Series Two",
  type='histogramx',
  opacity = 0.8)

layout <- list(
  xaxis = list(
  ticks = "",
  gridcolor = "white",zerolinecolor = "white",
  linecolor = "white"
 ),
 yaxis = list(
  ticks = "",
  gridcolor = "white",
  zerolinecolor = "white",
  linecolor = "white"
 ),
 barmode='overlay',
 # style background color. You can set the alpha by adding an a.
 plot_bgcolor = 'rgba(249,249,251,.85)'
)

response <- p$plotly(data0, data1, kwargs=list(layout=layout))
url <- response$url
filename <- response$filename
browseURL(response$url)

plotly5

5. Plotting y1 and y2 in the Same Plot

Plotting two lines or graph types in Plotly is straightforward. Here we show y1 and y2 together (Popular SO ?). 

library(plotly)
p <- plotly(username="Username", key="API_KEY")

# enter data
x <- seq(-2, 2, 0.05)
y1 <- pnorm(x)
y2 <- pnorm(x,1,1)

# format, listing y1 as your y.
First <- list(
  x = x,
  y = y1,
  type = 'scatter',
  mode = 'lines',
  marker = list(
   color = 'rgb(0, 0, 255)',
   opacity = 0.5)
  )

# format again, listing y2 as your y.
Second <- list(
  x = x,
  y = y2,
  type = 'scatter',
  mode = 'lines',
  opacity = 0.8,
  marker = list(
   color = 'rgb(255, 0, 0)')
  )

plotly6

And a shot of the Plotly gallery, as seen at the Montreal meetup. Happy plotting!

plotly_mtlRmeetup2