{"id":30020,"date":"2018-11-12T21:54:06","date_gmt":"2018-11-13T02:54:06","guid":{"rendered":"https:\/\/digital.hbs.edu\/platform-rctom\/submission\/applications-of-machine-learning-in-the-us-criminal-justice-system\/"},"modified":"2018-11-12T21:54:06","modified_gmt":"2018-11-13T02:54:06","slug":"applications-of-machine-learning-in-the-us-criminal-justice-system","status":"publish","type":"hck-submission","link":"https:\/\/d3.harvard.edu\/platform-rctom\/submission\/applications-of-machine-learning-in-the-us-criminal-justice-system\/","title":{"rendered":"Applications of Machine Learning in the US Criminal Justice System"},"content":{"rendered":"

Applications of Machine Learning in the US Criminal Justice System<\/p>\n

 <\/p>\n

The criminal justice system ostensibly protects all Americans and enforces laws in our communities to allow all \u201claw-abiding\u201d individuals to flourish equally. But in practice, its impacts are far from equal. There is a broad base of evidence demonstrating racial bias in how the criminal justice system is applied today and has been applied through the history of our country [1]. \u00a0From the drug war, the application of \u201cStop-and-Frisk\u201d policies, to the use of the death penalty: the criminal justice system produces disparate outcomes for racial minorities.<\/p>\n

Human judgement has its limitations and we are all impacted by our own biases. By augmenting human decision making with \u201cunbiased\u201d machine learning algorithms, the hope is that we may be able to change the way that the criminal justice system is applied so it is more fair and equitable [2]. \u00a0The need for improvement is widely recognized and the potential that machine learning can play in that process is great, but experimenting in processes where human lives are concerned is fraught with difficulty.<\/p>\n

 <\/p>\n

To start to address this issue and its potential, jurisdictions around the country have started to experiment with how machine learning can be used to inform and help solve some of the most complex problems in the criminal justice process. \u00a0The development of these technologies has largely been outsourced, co-developed with the private sector, and then used by law enforcement or court systems [3][4].<\/p>\n

One example of this is how the system COMPAS has been used in Broward County, Florida in pre-trial hearings. The COMPAS system employs a 137-question questionnaire to apply a \u201crisk score\u201d to individuals.\u00a0 This risk score is then used by judges in pre-trial hearings to determine whether an individual can be released or needs to be held in jail until their trial [4]. The motivation behind this software is clear: if machine learning algorithms can accurately predict who is likely to commit additional crimes, taking sentencing decisions out of the hands of humans and their biases, the criminal justice system might be applied more fairly.\u00a0 And, by keeping people out of jail who are unlikely to commit future crimes we can reduce the overall cost of our penal system [4].<\/p>\n

This is, however, predicated on the ability of the algorithm to get this process right.\u00a0 An investigation of the system found that the program was resulting in significant racial disparities: the formula was almost twice as likely to falsely flag black defendants as future criminals and white defendants were more likely to be mislabeled as low risk [4][5].\u00a0 The system was designed to eliminate human bias using machines, but the outcomes continued to perpetuate the same biases we see today.<\/p>\n

\"\"<\/a>
Outcomes from COMPAS use in Broward County [4]<\/figcaption><\/figure>\n

Moving forward there two important steps public organizations can take to improving the use of machine learning in the criminal justice system.<\/p>\n

The first step is promoting transparency.\u00a0 Today, like COMPAS, the algorithms used are largely proprietary with no transparency in how they function \u2013 they work as \u201cBlack Boxes\u201d [3].\u00a0 This makes it impossible to audit where the logic in the system fails when it produces undesired outcomes.\u00a0 To improve how these algorithms work, they need to be open to critique. [2]<\/p>\n

The second step these organizations should take is the democratization of criminal justice data.\u00a0 While much of the data is in theory available to the public, creating an aggregated database of criminal outcomes and risk factors with which these algorithms can be trained and evaluated would help in the acceleration of their development [2].<\/p>\n

Many would argue that throughout its history our society has failed to enforce our criminal laws fairly and without bias.\u00a0 There seems to be opportunity to help eliminate bias by replacing or at least aiding human decision making with algorithms, but this is by no means a straightforward process and there are important consequences to consider.<\/p>\n

 <\/p>\n

In addition, here are a couple additional questions worth considering as we move forward in this domain:<\/p>\n