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Code for How good is 85%? A survey tool to connect classifier evaluation to acceptability of accuracy

This work is licensed under a Creative Commons Attribution 4.0 International License Matthew Kay ([] (, Shwetak N. Patel ([] (, and Julie A. Kientz ([] (

This repository contains analysis code from:

Kay, Matthew, Patel, Shwetak N., and Kientz, Julie A. How Good is 85%? A Survey Tool to Connect Classifier Evaluation to Acceptability of Accuracy. CHI 2015 (upcoming).

It is intended to allow others to adopt our tool for generating surveys and modelling acceptability of accuracy. It is currently a work-in-progress. If you have any questions, please email Matthew Kay (above). Also, if you've done something cool with this work, we'd love to hear from you!

Libraries needed for these analyses


Plus some helper functions in util.R:


Example model

Fitting the model

Example code for fitting a model can be found in src/application_ui-regression.R. Since fitting the model takes some time, for the purposes of this example we will just load the fitted model, which has been saved in src/output/acceptability_ui-model-small-final.RData:


Parameter estimates

Let's plot some posterior parameter estimates. First, we extract the sample estimates for b0, b, and alpha:

params = extract_samples(best_model_chain, cbind(b0, b, alpha)[application])

This gives us a pretty simple table of estimates. We can look at the first couple of entries to get an idea of its structure:

.sample application b0 b alpha
1 alarm_police -15.65 18.49 0.5639
2 alarm_police -16.58 20.07 0.5377
3 alarm_police -17.05 19.83 0.4827
4 alarm_police -16.86 19.22 0.5002
5 alarm_police -16.2 18.64 0.4509
6 alarm_police -16.18 17.97 0.5414

Now we'll plot each parameter in turn, with some useful reference lines:

ggposterior(params, aes(x=application, y=b)) +
    geom_hline(yintercept=0, lty="dashed")

plot of chunk params_plot

ggposterior(params, aes(x=application, y=b0)) +
    geom_hline(yintercept=0, lty="dashed")

plot of chunk params_plot

ggposterior(params, aes(x=application, y=alpha)) +
    geom_hline(yintercept=0.5, lty="dashed") +
    ylim(0, 1)

plot of chunk params_plot

Differences in alpha

We can also examine the posterior difference in alpha between each condition. First we extract the samples for alpha, this time in a wide format to facilitate comparison:

alpha = extract_samples(best_model_chain, alpha[application] | application)

Again, let's see the first couple of entries to get an idea of its structure:

.sample alarm_police alarm_text_message electricity location
1 0.5639 0.2988 0.5119 0.3859
2 0.5377 0.3567 0.4585 0.577
3 0.4827 0.3135 0.4513 0.4802
4 0.5002 0.3772 0.4969 0.5148
5 0.4509 0.2896 0.4851 0.5001
6 0.5414 0.3805 0.5025 0.5161

Now, for every pair of applications, let's get the posterior distribution of their difference in alpha:

alpha_comparisons = ldply(combn(levels(df$application), 2, simplify=FALSE), 
    function(applications) {
            applications = paste(applications, collapse=" - "), 
            alpha_difference = alpha[[applications[[1]]]] - alpha[[applications[[2]]]]

Which looks like this:

applications alpha_difference
alarm_police - alarm_text_message 0.2651
alarm_police - alarm_text_message 0.181
alarm_police - alarm_text_message 0.1692
alarm_police - alarm_text_message 0.123
alarm_police - alarm_text_message 0.1614
alarm_police - alarm_text_message 0.1609

Finally, we can plot the estimated differences:

ggposterior(alpha_comparisons, aes(x=applications, y=alpha_difference)) + 
    geom_hline(yintercept=0, lty="dashed") +
    ylim(-0.5, 0.5)

plot of chunk alpha_comparison_plot

Citing this work

Please cite the CHI paper above.


Should you encounter any issues with this code, contact Matthew Kay ( If you have found a bug, please file it here with minimal code to reproduce the issue.


Code for generating surveys and fitting models of acceptability of accuracy




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