Final Results
The Criminal Justice System: Making Sure Big Data Doesn’t Lead to Biases
Advisor: Layla Oesper
Background
The meteoric rise of big data of all forms has the potential to revolutionize how many important decisions are made. One area of public life where such data has the potential to greatly impact such decisions processes is in the criminal justice system. However, care must be taken to ensure that decisions based on data are not perpetuating human biases. For example, consider the following two real world scenarios.
A 2016 bombshell article by ProPublica analyzed how an algorithm called COMPAS was being used in the criminal justice system. In short, this algorithm was used to determine the likelihood that a defendant would become a repeat offender, and this score was being used by judges when determining sentences. The ProPublica article described how “the formula was particularly likely to falsely flag black defendants as future criminals, wrongly labeling them this way at almost twice the rate as white defendants.”
On the other hand, data can also help to reveal biases within the criminal justice system. Analysis of New York City’s stop-and-frisk practices revealed that most of the time a person was stopped there was no need for further action. Furthermore, a large majority of the individuals stopped were either black or Hispanic. Analysis of this large dataset to identify factors that are correlated with further police action, but do not perpetuate biases, could be very useful.
The project
In this project we are going to analyze the COMPAS dataset and the stop-and-frisk dataset. In particular, we are going to focus on how these datasets can be used for “bias-free” classification. Classification takes a set of data points (e.g. defendants) and assigns a label (or a class) to each data point (e.g. low risk, medium risk, high risk). Specifically, you will:
- Download and become familiar with the COMPAS dataset and the stop-and-frisk dataset. This may include a basic analysis of what is in these datasets, how they were created, and how this type of information is currently used (or may be used) in the criminal justice system.
- Study existing literature on previous analysis of these datasets. In particular, you will focus on analysis that aims to reduce or identify bias in the classification process. To do this you will have to examine what exactly bias means in classification and how an approach can be modified to be “fair”.
- Implement and apply “fair” classification algorithms to both the COMPAS and the stop-and-frisk datasets. You may either attempt to reproduce previous analysis done on these datasets, or apply other classification methods that you learned about.
- Analyze the results of your implemented methods and be able to discuss the potential impacts of the result if incorporated into the criminal justice system.
- (If time) Write an opinion paper, based on your analysis, for how big data can be more fairly used within the criminal justice system. You may need to do additional analysis and compare different approaches for this paper.
Recommended experience
Interest in how big data analysis can be incorporated in a “fair manner” into the criminal justice system. Experience with machine learning or classification algorithms (as seen in CS32X courses) may be helpful, but is not required.
References/inspiration
Below are a few papers about existing work analyzing these datasets. These are only intended to provide you a minimal start for your literature search - they are certainly not the only nor necessarily the best sources for ideas. You will be finding and reading many additional papers and articles!
- Julia Angwin, Jeff Larson, Surya Mattu and Lauren Kirchner, ProPublica, “Machine Bias - There’s software used across the country to predict future criminals. And it’s biased against blacks.” ProPublica, May 23, 2016.
- Green B. “Fair” Risk Assessments: A Precarious Approach for Criminal Justice Reform. 5th Workshop on Fairness, Accountability, and Transparency in Machine Learning (FAT/ML 2018). 2018.
- Goel, Sharad, Justin M. Rao, and Ravi Shroff. "Precinct or prejudice? Understanding racial disparities in New York City’s stop-and-frisk policy." The Annals of Applied Statistics 10.1 (2016): 365-394.
Meeting Times
Monday/Wednesday 3:10pm - 4:10pm (6a) for Fall and Winter