Pre-trial algorithms deserve a fresh look, study suggests
By: Logan Koepke
Tags: Machine Learning, Criminal Justice, Criminal Justice Reform, Algorithms

What if a predictive tool used at the moment of arraignment could simultaneously reduce the number of people sent to jail before trial, reduce crime, and also reduce racial disparities in incarceration? Such a tool could be a game changer in criminal justice reform. There’s good reason to be skeptical, however: Several high-profile analyses of COMPAS — a popular, commercially available “risk assessment” tool — have claimed that the tool unfairly labels black defendants as higher risk, and it does so more often than it overestimates the risk posed by white defendants.

But a new large scale study — Human Decisions and Machine Predictions — demonstrates that it’s possible to build a predictive tool that simultaneously accomplishes three desirable goals: reducing pre-trial detention rates, reducing re-arrest rates of those released pending trial, and reducing racial disparities in which defendants are jailed. Policymakers across the ideological spectrum should be interested in this result and encourage further study.

The scenario: Before trial, judges make predictions about flight risk and public safety

After an arrest, a defendant appears at a bail hearing before a judge. The judge then has an important decision to make: the judge can release the defendant, release the defendant under certain conditions (like paying a bail amount or requiring that the defendant be monitored), or detain the defendant without the possibility of release. When making these decisions, judges generally weigh two concerns: flight risk and public safety. A judge would ask herself: Would the defendant fail to appear in court? Would he commit another crime if released? Would he be a threat to the public? All of these assessments rely on prediction.

There’s been a long-standing debate about whether pre-trial detention, cash bail, or predictions of a defendant’s future dangerousness are ever appropriate, and whether they’re unconstitutional forms of punishment. In fact, these questions were vigorously debated and litigated during the bail reforms of the 1960’s, 70’s, and 80’s.

But this paper focuses on a more incremental and quantitatively driven question: how might our existing pre-trial decision-making process — based on making predictions about a person’s flight risk and future dangerousness — be made more equitable and fair?

What the researchers did

The researchers examined a dataset of more than 550,000 defendants who were arrested in New York City between 2008 and 2013.

The researchers built a model using the data about released defendants because these data contain both case characteristics (like the current charge, the details of the defendant’s criminal history, and the defendant’s age) and case outcomes (such as whether a defendant failed to appear or was re-arrested). Once they fit the model on the training data, the researchers predicted the risk for each defendant in their test data. Then they compared their risk predictions against the real decisions that judges made.

Judges often detain low-risk defendants and release the riskiest

Perhaps unsurprisingly, judges often make mistaken predictions. The researchers found that though judges correctly release most individuals predicted to below-risk, among “the [predicted] riskiest 1% of released defendants . . . the judge[s] release them at a rate of fully 48.5%.” Judges, it seems, were treating many individuals predicted to be very high risk as if they were low risk.

One explanation might be that judges set strict bars for pre-trial detention to ensure that only the very riskiest defendants are detained. But the researchers’ simulations found another surprising result: Judges who set a higher bar for detention actually detained defendants across the predicted risk distribution and chose to “to jail many low-risk defendants ahead of those with higher predicted risk.” Indeed, “[judges] instead seem to be mis-ranking defendants when deciding whom to detain.”

Together, the researchers found that judges not only release some of the riskiest individuals, but they also over-detain many less risky ones.

Should we put the algorithm in charge?

Given these results, how could algorithmic predictions help inform pre-trial decision-making?

The researchers propose an “auto pilot” scenario for bail decisions where “a full re-ranking policy . . . let[s] the risk tool rank-order all defendants by predicted risk and make recommendations for all of the jail and release decisions.” Today, we know what it’s like when human judges make bail decisions on their own. But what if just an algorithm made bail decisions? How would things change? (In reality, it’s unlikely we’d ever fully automate bail decision-making and take judges out of the process. But the point of the paper is to demonstrate the scope of what’s possible.)

What the “auto pilot” scenario would look like

In order for an auto-pilot re-ranking simulation to work, the researchers have to confront some difficult counterfactuals. For example, a re-ranking simulation will necessarily shuffle some decisions: some people who judges actually released will be detained by the simulation, and some people who judges detained will be released. Evaluating the effect of the first case is easy to understand — another person is detained, which increases the detention rate. But it’s difficult to evaluate the second case: What would individuals — actually detained in real life, but released in the simulation — do? It’s this unknowable, alternate reality that plagues a lot of pre-trial research.

But the researchers devised a clever workaround. They “assign[ed] the same crime outcomes to . . . observationally equivalent defendants whom the judges actually released.” Essentially, they impute what will occur in their simulation based on what a similarly situated released individuals actually did. (Obviously, this requires some big assumptions.)

Turning back to the simulation, first, the researchers used their algorithm to predict each defendant’s failure-to-appear risk. (We’re focused on failure to appear, because, unlike the vast majority of states, New York state law prohibits judges from making discretionary decisions to deny bail based on public safety concerns. They can only consider flight risk.) Next, they rank-ordered all defendants by their predicted risk. Going forward, keep this incredibly simplified bar in mind:

The algorithm simply decides to detain those defendants that are predicted to be the riskiest — meaning those who are most likely to fail to appear. So, the tool would start detaining on the right side and work to the left.

Here are the results of their full re-ranking “auto pilot” approach based on NYC data:

If the algorithm’s release rule matches the rate at which judges presently release defendants, their algorithm is able to reduce the failure to appear rate by 24.7 percent.

If the failure to appear rate is held constant, the algorithm could reduce the jail detention rate from 26.4 percent to 15.4 percent — a 42 percent reduction.

That’s an improvement, but as the researchers note, we don’t care about these rates in the abstract. We care whether or not these decisions are fair and lead to a more equitable, racially just system.

Policy constraints could help ensure racial equity

The researchers then showed that it’s also possible to explicitly constrain the algorithm to “ensure no increase in racial disparities in…jail with very little impact on the algorithm’s performance in reducing crime.”