Balkinization: Automating Authority? Algorithmic Practices, Knowledge, and Journalistic Professionalism

Automating Authority? Algorithmic Practices, Knowledge, and Journalistic Professionalism

Guest Blogger

Matt Carlson

For the Unlocking the Black Box conference -- April 2, 2016 at Yale Law School

It is either ironic or proving a point that my first step in writing a paper about algorithms was to consult the algorithms of Google Scholar. Nonetheless, serendipitous things happen when you take a deep dive into repositories of academic work. In this instance, my search turned up a computer science paper with a rather instrumental title, “A front-page news-selection algorithm based on topic modelling using raw text.” This was not at all surprising; searching for research on algorithms regularly results in a mishmash of material arrayed across a spectrum marked by sociological studies comprising what Tarleton Gillespie and Nick Seaver call “critical algorithm studies” on one end and hosts of technical studies focused on the construction and operation of algorithms on the other.

This particular article fell in the latter camp, but luckily I stopped to read it. I can’t speak to its technical value or procedures, but it was the opening paragraph that caught my attention:

The front page of a news aggregator, like Google News or Yahoo! News, is the showcase where readers expect to see significant news articles. With human-editor-based news aggregators, the burden of reading several news articles and selecting important ones is a challenging task. Editors may select worthless news unintentionally, or even according to their own points of view. As a result, intelligent algorithms that allow news aggregators to process news and select significant ones, need to be developed.

On the surface, this is a vague statement used to justify the subsequent development of an algorithm taking a unique approach to sorting stories into a finite list for the front-page of a hypothetical news site. It is hardly a full-blown argument, let alone a manifesto. But this paragraph is also pregnant with assumptions about what journalism is, how it works, and how algorithms can be introduced to make it work better. In this sense, it is an ideology, a way of abstracting the world and formulating a particular set of values that in turn drives concordant actions. And to the extent that it is expressed so unproblematically and definitively, it deserves a second look.

If we take a broader view, it becomes clear that the issue at hand is really one involving professional judgment. Professional authority entails the ability to control knowledge in particular domains. Without the blunt powers associated with the state, professional authority must rely on legitimating this knowledge as socially beneficial. Doing so allows professions to mark off particular social spaces, with the promise that this control is backed by a sense of social responsibility.

For journalists, this schematic of professional authority quickly runs into problems. Although journalists possess skills and expertise, their output – the news – is not the esoteric professional knowledge associated with, say, medicine, but a prosaic discourse tasked with being understandable to large swaths of society. News language is largely ordinary language. Faced with this dissimilarity, journalists justify their authority through their skill in making judgments about what is important. News stories are stylized retellings of events in the world while news products – a broadcast, a newspaper, a Web site – are carefully ordered assemblages of texts that give meaning to the world. All of this rests on claims that journalists know what’s important and should be trusted with these decisions.

What my paper for this conference explores is how the growing use of algorithms complicates this function – if not supplants it entirely. Algorithms that select news, from recommendation engines to the algorithms that construct the newsfeed of a social media site, render their own judgment of what’s important, often idiosyncratically based on audience behavior. More recently, software combining natural language processing and artificial intelligence makes it possible for machines to write stories themselves, diminishing the human to the role of the initial programmer.

This is all made more complicated by the resilience of objectivity as a guiding norm for journalists. Even as objectivity as an absolute ideal has lost its luster, it continues to provide an argument validating a way of approaching the news as a legitimate form of social knowledge. Algorithms muddle this argument in that they too are attached to a form of mechanical objectivity presumed to be impervious to the subjective faults of human beings. Consider again the opening paragraph pasted above: the authors identify human failings that can be replaced by a computer for the benefit of all. They even see it as an imperative that this happen. This is a problematic assumption in many ways, but it is also a powerful technocratic one.

This tension returns us to professional judgment. Journalism is not a mechanical craft but a creative and variable one. Journalists must make decisions about what the public should know. The ample scrutiny journalists receive may be taken as evidence that journalists often make flawed judgments—depending on the subjective views of critics. But this discourse is also an indication that these judgments matter and are worth contesting.

In the end, when we think about algorithmic accountability, we need to be sure we are looking not just at the practices that are developing, but also at how these practices are reflected in broader discourses through which we think about what constitutes authoritative knowledge in society. The replacement of the news judgment of a human by an algorithm, to put it most bluntly, indicates larger fissures in how we recognize what legitimate knowledge practices look like, and how we think they should look. These developments have become visible with journalism, but this issue increasingly extends across the professions and into larger questions of who makes knowledge.

Matt Carlson is associate professor of communication at Saint Louis University. He can be reached at mcarls10 at slu.edu

Labels: Black Box

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