But something remarkable happened in ICE’s New York field office not long after President Trump came into office: Even though, from 2013 into 2017, the algorithm had recommended about 47 percent of arrestees be released, after June 2017, the number plummeted to about 3 percent.

According to a lawsuit filed last week by the New York Civil Liberties Union and the legal aid organization Bronx Defenders, ICE secretly changed the algorithm to all but eliminate the release of defendants, the Intercept reported this week. That means thousands of noncitizens in New York City languished for weeks, and sometimes months, awaiting hearings. The information about the algorithmic manipulation came to light only because immigration lawyers noticed the change in outcomes, and the civil rights groups filed Freedom of Information Act lawsuits to figure out what was going on.

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Now those groups have sued ICE in federal court, saying the alteration of the algorithm denied due process to those to whom it was applied. They have asked that human decision-makers revisit all of the algorithm’s decisions that kept low-risk individuals in custody. (In theory, an official reviewed the algorithm’s recommendation, but the program’s answer was embraced some 99 percent of the time, the legal complaint says.)

Every day, government agencies rely on algorithms to make decisions that affect our rights and liberties. Algorithms help to administer federal and state benefits, determine the length of criminal sentences and interpret forensic evidence. Yet the government seldom tells the public how these algorithms work. When ICE tweaked its risk assessment tool, it certainly did not notify the tens of thousands of immigrants ensnared in Trump’s “catch and detain” policy shift. Yet it in effect had introduced a draconian policy change. The full ramifications of the change to the algorithm are likely even broader: It is used nationally, but the FOIA suit only revealed data from New York.

When the government turns to automated decision-making, transparency all too often falls by the wayside. Critics have frequently pointed out that algorithms can be biased and faulty — but so can human decision-makers. However, the opacity of decisions made by software presents a unique set of difficulties. It is not just that algorithms can, as appears to be the case here, systematically lead to unjustified outcomes. It is also that victims of the system and watchdog groups often have no way of knowing why and how the decisions are made, which forecloses accountability.

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It is hardly only ICE that is using tools like these, after all. Similar programs are deployed across many jurisdictions to assess whether an individual charged with a crime ought to be released from detention before trial. As many jurisdictions turn away from cash bail, reformers are implementing risk-based systems to determine who actually poses a danger to society — and who should go free while awaiting trial. The impulse is understandable: The goal is to standardize a process that, in the past, has too often been shaped by defendants’ poverty and judges’ inconsistent notions about crime, dangerousness and punishment. Indeed, in some cases criminal justice reformers have applauded risk assessment algorithms like ICE’s for reducing pretrial incarceration. When ICE announced its development of a risk assessment tool, some immigration advocates cheered, believing it would result in more objective and consistent decisions.

But the opacity remains a problem. Unsurprisingly, the government would prefer to keep those technologies a closely guarded secret — avoiding public scrutiny, judicial review and legislative action. ICE does not stand out in this regard. In other cases, prosecutors have argued that algorithms used to interpret forensic evidence or to determine defendants’ prison sentences amount to trade secrets — they are the property of private companies — that cannot be disclosed without violating contracts. On similar grounds, state agencies have resisted sharing information about automated decisions to fire public employees or to reduce Medicaid benefits.

Democratic oversight is critical if we are to realize the benefits of automated decision-making. To start, the government should provide much more information about when it is using these new tools, and it should disclose how the algorithms weigh all the factors they consider. Existing state and federal open records statutes (such as FOIA) provide some recourse, but not enough. (If you do not know an algorithm exists, you are unlikely to sue to find out how it works.) At a bare minimum, the government ought to be obliged to reveal such information when it uses automated decision-making to deprive individuals of liberty or property. Some plaintiffs have succeeded in arguing that the Constitution’s protections for due process requires this level of disclosure. Algorithmic transparency requirements could also be imposed through either state or federal law.

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Even this modest suggestion has proved surprisingly controversial. Law enforcement officials, for instance, have expressed concern that disclosing how automated decisions are made will allow bad actors to “game” the systems and avoid detection.