Recoding America: Why Government Is Failing in the Digital Age and How We Can Do Better

by Jennifer Pahlka

The temporal, organizational, structural, and cultural gaps between policy and tech teams, and between tech teams and the users of that tech, make it hard to try out strategies, learn what works, resolve ambiguities, and readjust. Instead of active collaboration and co-learning, implementing government policy through digital technology resembles a game of telephone, in which each party in sequence fumbles the translation a bit until, many stakeholders later, the message is mangled beyond recognition.

We can’t afford this downward spiral of poor service leading to alienation and decreased political participation, which in turn lead to poorer service. The implementation crisis threatens our democracy.

“A revolution doesn’t happen when society adopts new technologies—it happens when society adopts new behaviors.”14 The same is true of government. Although government must adapt to an increasingly digital world, the heart of the adaptation isn’t mobile apps, cloud computing, or even artificial intelligence. It is a willingness to put the needs of government’s many users ahead of the needs of the bureaucracy, and to learn while doing.

there is no easy way to explain to angry constituents and the people who represent them—whose personal experience with seamless online services gives them certain expectations about how technology should work—why this task is so hard.

When we speak of “legacy systems” in government, it does not mean simply that they are old. It means that we are grappling with the legacy of decades of competing interests, power struggles, creative work-arounds, and make-dos that are opportune at the time but unmanageable in the long run.

archaeological tech layers are an expression of archaeological policy layers. The tech gets complex because the program and the policy governing it are complex. And like the tech, the policy is complex in part because it always accrues but is rarely reduced or reconciled.

You can have systems that do every possible thing policymakers can think of to ensure “program integrity” (in other words, making sure no one is getting a dollar more than they should) or you can have systems that scale. You can’t have both. And most of what policymakers do to ensure program integrity ends up costing far more in administration than the program saves on paying out benefits.

When systems or organizations don’t work the way you think they should, it is generally not because the people in them are stupid or evil. It is because they are operating according to structures and incentives that aren’t obvious from the outside.

Government’s obsession with requirements—voluminous, detailed requirements that can take so long to compile the software is obsolete before it’s even bid out—stems from a delusion that it’s possible to make a work plan so specific that it requires no further decision-making. You hand it off and the developers just do exactly what they’re told. Why not let those developers choose the best tool or platform for the job? In part, because they sit at the bottom of the waterfall.

Is the APA just the cost of a representative democracy that relies on checks and balances? Rubin says no, instead charging both the law itself and the way it continues to be applied with something akin to myopia or laziness. “Courts expand the adjudicatory implications of the statute because of their own familiarity with the adjudicatory process,” he writes. “The judicial model persists because of its familiarity, combined with the unfamiliarity of more modern administrative alternatives. In its persistence, however, it smuggles incremental … adversarial approaches into a comprehensive law-creating context, where they encumber the process”—without improving the outcome.

The environmental movement got really good at stopping bad things from happening but less adept at enabling, and speeding up, good things. Now the habit of suing has itself become a great threat to our environment.

Law scholar Larry Kramer, describing what he calls “popular constitutionalism,” explains that courts were never considered the final say on interpretation of the Constitution until the late twentieth century.5 The system was originally designed to have play between the views of the different branches of government and ultimately to allow the public to choose the winning interpretation.

As scholars have demonstrated, litigation changes agencies over time.6 To begin with, the more they are sued, the more legal staff they must invest in. But more importantly, courts tend to hold agencies accountable not for their outcomes but rather for their fidelity to procedures, compounding the accountability trap the bureaucracy is already stuck in. The more an agency is exposed to the courts, the more everyone there, not just the legal staff, must care about, pay attention to, and layer on procedures when they are required to make any decisions—in

When there are important human and societal outcomes on the line, we can’t afford to fail just because lawmakers didn’t understand the difference between a provision that would take five hours to implement and one that would take five years. There is no need to sign up members of Congress and their staffs for programming classes, although the tech in question is quite accessible. Much of what’s most useful in government today is not part of this brave new world of machine learning and artificial intelligence that many of us are understandably wary about. If you recall if-then statements from your freshman-year comp sci class, you can understand the basics of what the Clear My Record algorithm does. Writing policy with these considerations in mind isn’t a matter of staying on the cutting edge of computing, or even being able to question Mark Zuckerberg in a hearing without embarrassment. It’s just a matter of inviting different voices into the conversation and finding a common language with them. That common language is neither tech-speak nor legalese. It focuses on people—understanding their needs and testing our assumptions about them.