Algorithm auditing: Get started

Introduction

Welcome to the ‘Algorithm auditing’ vignette of the jfa package. This page provides a guide to the functions in the package that are designed to facilitate the audit of algorithms and predictive models. In particular, these functions implement techniques for determining an appropriate fairness measure and subsequently calculating and testing algorithmic fairness using this measure. The package also allows users to specify a prior probability distribution to conduct Bayesian algorithm auditing using these functions.

Functions and intended usage

Below you can find an explanation of the available algorithm auditing functions in jfa.

fairness_selection()
model_fairness()

Selecting a fairness measure

The fairness_selection() function is designed to help auditors to select the appropariate fairness metric to perform algorithm auditing. The questions within the decision-making workflow are based on observable data characteristics, the properties of fairness measures and the information required for their calculation. However, these questions are posed to the user in an easily understandable manner, requiring no statistical background or in-depth knowledge of the fairness measures. The function returns an object that can be used with associated print() and plot() methods.

For additional details about this function, please refer to the function documentation on the package website.

Example usage:

# Workflow leading to predictive rate parity
fairness_selection(q1 = 1, q2 = 1, q3 = 1, q4 = 1)

## 
##  Fairness Measure for Model Evaluation
## 
## Selected fairness measure: Predictive Rate Parity 
## 
## Based on:
##   Answer to question 1 (q1): Yes (1)
##   Answer to question 2 (q2): Correct classification (1)
##   Answer to question 3 (q3): Correct classification of the positive class (1)
##   Answer to question 4 (q4): False Positive (1)

Testing algorithmic fairness

The model_fairness() function is designed to evaluate fairness in algorithmic decision-making systems. It does this by computing and testing the equality of various model-agnostic fairness metrics between protected classes, based on a set of true labels and the predictions of an algorithm. The ratio of these metrics between an unprivileged protected class and a privileged protected class is referred to as parity, which quantifies relative fairness in the algorithm’s predictions. Available parity metrics include predictive rate parity, proportional parity, accuracy parity, false negative rate parity, false positive rate parity, true positive rate parity, negative predicted value parity, specificity parity, and demographic parity (Friedler et al., 2019; Pessach & Shmueli, 2022). The function returns an object that can be used with the associated summary() and plot() methods.

For additional details about this function, please refer to the function documentation on the package website.

Example usage:

# Compare predictive rate parity
x <- model_fairness(
  data = compas,
  protected = "Ethnicity",
  target = "TwoYrRecidivism",
  predictions = "Predicted",
  privileged = "Caucasian",
  positive = "yes",
  metric = "prp"
)
summary(x)

## 
##  Classical Algorithmic Fairness Test Summary
## 
## Options:
##   Confidence level:    0.95 
##   Fairness metric:     Predictive rate parity (Equalized odds)
##   Model type:          Binary classification
##   Privileged group:    Caucasian
##   Positive class:      yes 
## 
## Data:
##   Sample size:         6172 
##   Unprivileged groups: 5 
## 
## Results:
##   X-squared:           18.799 
##   Degrees of freedom:  5 
##   p-value:             0.0020951 
## 
## Comparisons to privileged (P) group:
##                                     Precision                    Parity
##   Caucasian (P)    0.57738 [0.53902, 0.61506]                         -
##   African_American  0.66525 [0.6434, 0.68658]   1.1522 [1.1143, 1.1891]
##   Asian               0.5 [0.067586, 0.93241] 0.86598 [0.11706, 1.6149]
##   Hispanic          0.5906 [0.50715, 0.67038]  1.0229 [0.87836, 1.1611]
##   Native_American      0.6 [0.14663, 0.94726]  1.0392 [0.25396, 1.6406]
##   Other            0.61176 [0.49988, 0.71562]  1.0596 [0.86578, 1.2394]
##                                    Odds ratio    p-value
##   Caucasian (P)                             -          -
##   African_American    1.4543 [1.2087, 1.7491] 5.4523e-05
##   Asian            0.73231 [0.052801, 10.156]          1
##   Hispanic           1.0559 [0.72564, 1.5432]    0.78393
##   Native_American     1.0978 [0.1249, 13.228]          1
##   Other               1.1532 [0.7105, 1.8933]     0.5621
## 
## Model performance:
##                    Support  Accuracy Precision    Recall  F1 score
##   Caucasian           2103 0.6585830 0.5773810 0.4720195 0.5194110
##   African_American    3175 0.6724409 0.6652475 0.7525587 0.7062147
##   Asian                 31 0.7419355 0.5000000 0.2500000 0.3333333
##   Hispanic             509 0.6817289 0.5906040 0.4656085 0.5207101
##   Native_American       11 0.6363636 0.6000000 0.6000000 0.6000000
##   Other                343 0.6938776 0.6117647 0.4193548 0.4976077

Benchmarks

To ensure the accuracy of statistical results, jfa employs automated unit tests that regularly validate the output from the package against the following established benchmarks in the area of algorithm auditing:

fairness (R package version 1.2.2)

Cheat sheet

The cheat sheet below will help you get started with jfa’s algorithm audit functionality. A pdf version can be downloaded here.

cheatsheet-algorithm

References

Friedler, S. A., Scheidegger, C., Venkatasubramanian, S., Choudhary, S., Hamilton, E. P., & Roth, D. (2019). A comparative study of fairness-enhancing interventions in machine learning. Proceedings of the Conference on Fairness, Accountability, and Transparency. https://doi.org/10.1145/3287560.3287589

Kozodoi, N., & V. Varga, T. (2021). Fairness: Algorithmic fairness metrics. https://CRAN.R-project.org/package=fairness

Pessach, D., & Shmueli, E. (2022). A review on fairness in machine learning. ACM Computing Surveys, 55(3), 1–44. https://doi.org/10.1145/3494672

Koen Derks