Algorithmic Accountability Reporting: On the Investigation of Black Boxes

New report by by Nicholas Diakopoulos: “The past three years have seen a small profusion of websites, perhaps as many as 80, spring up to capitalize on the high interest that mug shot photos generate online.1 Mug shots are public record, artifacts of an arrest, and these websites collect, organize, and optimize the photos so that they’re found more easily online. Proponents of such sites argue that the public has a right to know if their neighbor, romantic date, or colleague has an arrest record. Still, mug shots are not proof of conviction; they don’t signal guilt.

Having one online is likely to result in a reputational blemish; having that photo ranked as the first result when someone searches for your name on Google turns that blemish into a garish reputational wound, festering in facile accessibility. Some of these websites are exploiting this, charging peo- ple to remove their photo from the site so that it doesn’t appear in online searches. It’s reputational blackmail. And remember, these people aren’t necessarily guilty of anything.

To crack down on the practice, states like Oregon, Georgia, and Utah have passed laws requiring these sites to take down the photos if the person’s record has been cleared. Some credit card companies have stopped processing payments for the seediest of the sites. Clearly both legal and market forces can help curtail this activity, but there’s another way to deal with the issue too: algorithms. Indeed, Google recently launched updates to its ranking algorithm that down-weight results from mug shot websites, basically treating them more as spam than as legitimate information sources.2 With a single knock of the algorithmic gavel, Google declared such sites illegitimate.

At the turn of the millennium, 14 years ago, Lawrence Lessig taught us that “code is law”—that the architecture of systems, and the code and algorithms that run them, can be powerful influences on liberty.3 We’re living in a world now where algorithms adjudicate more and more consequential decisions in our lives. It’s not just search engines either; it’s everything from online review systems to educational evaluations, the operation of markets to how political campaigns are run, and even how social services like welfare and public safety are managed. Algorithms, driven by vast troves of data, are the new power brokers in society.

As the mug shots example suggests, algorithmic power isn’t necessarily detrimental to people; it can also act as a positive force. The intent here is not to demonize algorithms, but to recognize that they operate with biases like the rest of us.4 And they can make mistakes. What we generally lack as a public is clarity about how algorithms exercise their power over us. With that clarity comes an increased ability to publicly debate and dialogue the merits of any particular algorithmic power. While legal codes are available for us to read, algorithmic codes are more opaque, hidden behind layers of technical complexity. How can we characterize the power that various algorithms may exert on us? And how can we better understand when algo- rithms might be wronging us? What should be the role of journalists in holding that power to account?

In the next section I discuss what algorithms are and how they encode power. I then describe the idea of algorithmic accountability, first examining how algorithms problematize and sometimes stand in tension with transparency. Next, I describe how reverse engineering can provide an alternative way to characterize algorithmic power by delineating a conceptual model that captures different investigative scenarios based on reverse engineering algorithms’ input-output relationships. I then provide a number of illustrative cases and methodological details on how algorithmic accountability reporting might be realized in practice. I conclude with a discussion about broader issues of human resources, legality, ethics, and transparency.”

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