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The Dutch Tax Authority Was Felled by AI—What Comes Next?

European regulation hopes to rein in ill-behaving algorithms

4 min read
A woman at the front of a crowd stands at a microphone, looking at a man in glasses who stands alone.

Prime Minister Mark Rutte talks to parents before being interrogated by the parliamentary interrogation committee regarding the child-care allowance on 26 November 2020 in The Hague, Netherlands.

Niels Wenstedt/BSR Agency/Getty Images

Until recently, it wasn’t possible to say that AI had a hand in forcing a government to resign. But that’s precisely what happened in the Netherlands in January 2021, when the incumbent cabinet resigned over the so-called kinderopvangtoeslagaffaire: the childcare benefits affair.

When a family in the Netherlands sought to claim their government childcare allowance, they needed to file a claim with the Dutch tax authority. Those claims passed through the gauntlet of a self-learning algorithm, initially deployed in 2013. In the tax authority’s workflow, the algorithm would first vet claims for signs of fraud, and humans would scrutinize those claims it flagged as high risk.

In reality, the algorithm developed a pattern of falsely labeling claims as fraudulent, and harried civil servants rubber-stamped the fraud labels. So, for years, the tax authority baselessly ordered thousands of families to pay back their claims, pushing many into onerous debt and destroying lives in the process.

“When there is disparate impact, there needs to be societal discussion around this, whether this is fair. We need to define what ‘fair’ is,” says Yong Suk Lee, a professor of technology, economy, and global affairs at the University of Notre Dame, in the United States. “But that process did not exist.”

Postmortems of the affair showed evidence of bias. Many of the victims had lower incomes, and a disproportionate number had ethnic minority or immigrant backgrounds. The model saw not being a Dutch citizen as a risk factor.

“The performance of the model, of the algorithm, needs to be transparent or published by different groups,” says Lee. That includes things like what the model’s accuracy rate is like, he adds.

The tax authority’s algorithm evaded such scrutiny; it was an opaque black box, with no transparency into its inner workings. For those affected, it could be nigh impossible to tell exactly why they had been flagged. And they lacked any sort of due process or recourse to fall back upon.

“The government had more faith in its flawed algorithm than in its own citizens, and the civil servants working on the files simply divested themselves of moral and legal responsibility by pointing to the algorithm,” says Nathalie Smuha, a technology legal scholar at KU Leuven, in Belgium.

As the dust settles, it’s clear that the affair will do little to halt the spread of AI in governments—60 countries already have national AI initiatives. Private-sector companies no doubt see opportunity in helping the public sector. For all of them, the tale of the Dutch algorithm—deployed in an E.U. country with strong regulations, rule of law, and relatively accountable institutions—serves as a warning.

“If even within these favorable circumstances, such a dangerously erroneous system can be deployed over such a long time frame, one has to worry about what the situation is like in other, less regulated jurisdictions,” says Lewin Schmitt, a predoctoral policy researcher at the Institut Barcelona d’Estudis Internacionals, in Spain.

So, what might stop future wayward AI implementations from causing harm?

In the Netherlands, the same four parties that were in government prior to the resignation have now returned to government. Their solution is to bring all public-facing AI—both in government and in the private sector—under the eye of a regulator in the country’s data authority, which a government minister says would ensure that humans are kept in the loop.

On a larger scale, some policy wonks place their hope in the European Parliament’s AI Act, which puts public-sector AI under tighter scrutiny. In its current form, the AI Act would ban some applications, such as government social-credit systems and law enforcement use of face recognition, outright.

Something like the tax authority’s algorithm would abide, but due to its public-facing role in government functions, the AI Act would have marked it a high-risk system. That means that a broad set of regulations would apply, including a risk-management system, human oversight, and a mandate to remove bias from the data involved.

The tale of the Dutch algorithm—deployed in an E.U. country with strong regulations, rule of law, and relatively accountable institutions—serves as a warning.

“If the AI Act had been put in place five years ago, I think we would have spotted [the tax algorithm] back then,” says Nicolas Moës, an AI policy researcher in Brussels for the Future Society think tank.

Moës believes that the AI Act provides a more concrete scheme for enforcement than its overseas counterparts, such as the one that recently took effect in China—which focuses less on public-sector use and more on reining in private companies’ use of customers’ data—and proposed U.S. regulations that are currently floating in the legislative ether.

“The E.U. AI Act is really kind of policing the entire space, while others are still kind of tackling just one facet of the issue, very softly dealing with just one issue,” says Moës.

Lobbyists and legislators are still busy hammering the AI Act into its final form, but not everyone believes that the act—even if it’s tightened—will go far enough.

“We see that even the [General Data Protection Regulation], which came into force in 2018, is still not properly being implemented,” says Smuha. “The law can only take you so far. To make public-sector AI work, we also need education.”

That, she says, will need to come through properly informing civil servants of an AI implementation’s capabilities, limitations, and societal impacts. In particular, she believes that civil servants must be able to question its output, regardless of whatever temporal or organizational pressures they might face.

“It’s not just about making sure the AI system is ethical, legal, and robust; it’s also about making sure that the public service in which the AI system [operates] is organized in a way that allows for critical reflection,” she says.

The Conversation (2)
Evariste Galois10 May, 2022

There is much unanswered in this article. What was the nature of the fraud? Why couldn't the civil servants verify it? The article says that noncitizens were flagged as being at a higher risk for fraud, but does not say if there was actually a correlation between noncitizens and fraud.

Moshe Waisberg10 May, 2022
INDV

govt. laws will almost always lag behind technologies' advancements

Will AI Steal Submarines’ Stealth?

Better detection will make the oceans transparent—and perhaps doom mutually assured destruction

11 min read
A photo of a submarine in the water under a partly cloudy sky.

The Virginia-class fast attack submarine USS Virginia cruises through the Mediterranean in 2010. Back then, it could effectively disappear just by diving.

U.S. Navy

Submarines are valued primarily for their ability to hide. The assurance that submarines would likely survive the first missile strike in a nuclear war and thus be able to respond by launching missiles in a second strike is key to the strategy of deterrence known as mutually assured destruction. Any new technology that might render the oceans effectively transparent, making it trivial to spot lurking submarines, could thus undermine the peace of the world. For nearly a century, naval engineers have striven to develop ever-faster, ever-quieter submarines. But they have worked just as hard at advancing a wide array of radar, sonar, and other technologies designed to detect, target, and eliminate enemy submarines.

The balance seemed to turn with the emergence of nuclear-powered submarines in the early 1960s. In a 2015 study for the Center for Strategic and Budgetary Assessment, Bryan Clark, a naval specialist now at the Hudson Institute, noted that the ability of these boats to remain submerged for long periods of time made them “nearly impossible to find with radar and active sonar.” But even these stealthy submarines produce subtle, very-low-frequency noises that can be picked up from far away by networks of acoustic hydrophone arrays mounted to the seafloor.

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