The term kangaroo court comes from the notion of justice proceeding by leaps, like a kangaroo, jumping over intentionally ignoring evidence that would be in favor of the defendant. It also refers to the pouch of kangaroo, meaning the court is in someone’s pocket.

There are double standards in European justice.  The standard of immunity is enjoyed by kleptocrats and their kith and kin.  The standard of persecution is inflicted on hoi polloi.

No company is above the law, but some firms are allowed much more time in complying with it. Obeying the law is non-negotiable, theoretically. The very existence of the compliance profession, however, implies that the reality is far more complex. Even more intriguing is the fact that, despite having splashed out on compliance like never before post-2008, banks in the United States and Europe had to pay US$65 billion in fines in 2014 alone. Many of the 2014 penalties pertained to violations committed well after the financial crisis by institutions deemed too big to fail. Financial behemoths apparently can afford to take a more wayward path to full compliance.

The financial sector is not unique in this respect. Even public organizations have been known to drag their feet on compliance, sometimes spectacularly – as in the early 20th century when municipalities in the U.S. delayed adopting civil service reform for more than 35 years.

Compliance should not be considered a matter of course. Both when and how it happens is highly dependent on the strategic positioning of the relevant actors. No person or organization is above the law, but the level of urgency with which they react to changes in the law may depend on what they believe they can get away with.

In order for compliance officers to have influence, they must first have both senior management support and visibility at the board level. Otherwise, they run the risk of being shut out of the power-based calculus that governs the public-private sector chessboard.

Courts nationwide are making greater use of computer algorithms. Simple, statistically informed decision rules can dramatically improve judicial determinations. But algorithms are not a complete fix. Algorithms are good at narrowly estimating risk, but they can’t set policy. They can’t tell you how many people to detain, or whether we should end money bail altogether, as some cities have done. They can’t tell you how much to invest in pretrial services or what those services should be. And they can’t incorporate every factor in every case, so we still need humans to make the final decision.

Algorithms are mostly used in two ways: to estimate a defendant’s flight risk, and to assess his or her threat to public safety. For example, based on a variety of factors, like age and criminal history, these algorithms rate a defendant’s likelihood to re-offend, usually on a scale from 1 to 10. Judges use these risk scores to help decide which defendants to release and which to detain pending trial.

Computers are good at estimating the likelihood of an event given structured information, like a defendant’s criminal history. Algorithms can pick out which pieces of information matter and which should be ignored to generate accurate estimates of risk.

In theory, judges try to do the same thing, but it’s easy for people to focus on the wrong factors and let implicit biases creep in. And some judges are just tougher than others, so there isn’t a consistent standard. If you’re assigned to a strict judge rather than a lenient one, you might get different results.

By using an algorithm, you could detain half as many defendants without increasing the number who fail to appear at trial. A lot of people who pose very little risk are being needlessly detained. There’s a huge social and financial cost to that.

Defining fairness is a complicated and still open problem. I doubt we’ll ever reach consensus, but there are a few common ways to think about it.

Some say an algorithm is fair if it doesn’t consider sensitive attributes, like race or gender. But even if you don’t explicitly consider such attributes, that information is usually baked into other factors, like place of residence or income. Yale law professor Ian Ayres has persuasively argued that in some situations it’s even unfair not to consider race when making decisions.

Others say an algorithm is fair only if its impact is the same on all race groups. For example, if more blacks than whites are rated high-risk by the algorithm, people in this camp would call that unfair.

Related to this idea of impact, some define fairness in terms of error rates. Algorithms seek to predict which defendants are most likely to commit new offenses, or to “recidivate.” One can look back at these predictions and ask how often the algorithms were wrong: How often did the algorithm classify blacks and whites as high-risk of re-offending when in fact those defendants did not go on to commit any new crimes? If black non-recidivists are more likely to be classified as high-risk than white non-recidivists, that would be unfair by this measure.

A preferred definition of fairness is that equally risky defendants are treated equally, regardless of race. For example, if the available information indicates that a white defendant and a black defendant both have a 30 percent chance of committing a violent crime, both defendants are either released or both are detained. To me this definition makes intuitive sense, and we show that there are strong legal and policy arguments supporting it.

The other popular definitions of fairness have significant shortcomings. For instance, consider an algorithm that disproportionately classifies black defendants as high-risk. We wouldn’t automatically call such disparate impacts unfair. For a variety of complex social and economic reasons, black defendants on average might be riskier than whites, in which case we would expect detention rates to reflect those differences.

The same is true for disparate error rates in estimating recidivism. It’s objectively harder to correctly classify black defendants than white defendants. That’s because a disproportionate number of black defendants have about even odds of reoffending, based on their prior criminal records. These defendants are not clearly going to commit a crime, but also are not clearly not going to commit a crime. Because it’s hard to predict the behavior of such defendants, that drives up error rates for blacks as a group. As with unequal detention rates, we wouldn’t call unequal error rates inherently unfair.

Perhaps the biggest misconception is that we should worry more about decisions made by algorithms than those made by humans. Many of the fairness issues ascribed to algorithms apply equally to human judges. And some problems, like inconsistency, afflict humans more than computers.

It’s also common to conflate disparate impact with discrimination. We’ve argued that algorithms which many people, including legal experts, would consider fair necessarily lead to racial disparities. It’s easy to latch onto these disparities as evidence of bias, but that misses the complexity of the problem.

Another big misconception is that algorithms are inherently unfair because they are based on imperfect data. Bad data is a serious issue which we shouldn’t ignore, but algorithms and humans can only use the information that’s available. Fairness must be viewed in context.

Algorithms will almost certainly play an increasingly prominent role in criminal justice. In cities where pretrial risk assessment tools have been deployed, fewer defendants are detained with little to no decrease in public safety.

To gain wider support and adoption, these algorithms need to be developed with more transparency. The leading risk assessment tools are often built under a veil of secrecy, which understandably sows misunderstanding and distrust. Algorithms have important limitations, but they can also dramatically improve the equity of decisions in our criminal justice system.


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