Debugging the Criminal Justice System
Carleton College Comps Senior Project | 2019-2020
![](img/Fairness.png)
Defining Fairness
In order to develop more fair algorithms, we began by defining what "fair" means in the context of risk assessment algorithms.
Read More![](img/Clustering.png)
Hierarchical Clustering
Clustering is different from our other algorithms because it is not a binary classifier. Instead of predicting if a person will recidivate, it groups data by similarity as seen above. Our clusters had no correlation to COMPAS score or recidivism.
See the Code![](img/DecisionTree.png)
Decision Trees
Above you can see the top levels of our decision tree. Using ID3 or decision achieved the second higest level of accuracy. It also did best a minmizing false positives, suggesting it is the most fair of the algorithms we looked at.
See the Code![](img/ANNresults.png)
Neural Network
Our Artificial Neural Network (ANN) achieved the highest level of accuracy. However, it was second to decision trees in minimizing false positives.
See the Code![](img/Github.png)
Visit Our GitHub
Visit our GitHub repository to see our data, results, fairness measurements, and more algorithms!
Go to GitHub