FOIA – For Algorithms

We need to know the algorithms the government uses to make important decisions about us by Nicholas Diakopoulos.

From the post:

In criminal justice systems, credit markets, employment arenas, higher education admissions processes and even social media networks, data-driven algorithms now drive decision-making in ways that touch our economic, social and civic lives. These software systems rank, classify, associate or filter information, using human-crafted or data-induced rules that allow for consistent treatment across large populations.

But while there may be efficiency gains from these techniques, they can also harbor biases against disadvantaged groups or reinforce structural discrimination. In terms of criminal justice, for example, is it fair to make judgments on an individual’s parole based on statistical tendencies measured across a wide group of people? Could discrimination arise from applying a statistical model developed for one state’s population to another, demographically different population?

The public needs to understand the bias and power of algorithms used in the public sphere, including by government agencies. An effort I am involved with, called algorithmic accountability, seeks to make the influences of those sorts of systems clearer and more widely understood.

Existing transparency techniques, when applied to algorithms, could enable people to monitor, audit and criticize how those systems are functioning – or not, as the case may be. Unfortunately, government agencies seem unprepared for inquiries about algorithms and their uses in decisions that significantly affect both individuals and the public at large.

Nicholas makes a great case for Freedom of Information Act (FOIA) legislation being improved to explicitly include algorithms used by government or on its behalf.

I include “on its behalf” because as Nicholas documents, some states have learned the trick of having algorithms held by vendors, thus making them “proprietary.”

If you can’t see the algorithms behind data results, there is no meaningful transparency.

Demand meaningful transparency!

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