Our cognitive functions are increasingly being outsourced to computational algorithms, simultaneously enhancing our decision-making capabilities and manipulating our behavior. Digital spaces, where information is more accessible and more affordable than ever before, provide us with insights and data for us to use at will. Nowadays, a simple Google search can take on the role of financial advisor, lawyer, or even doctor. But the information we find online is silently sorted, ordered and presented by algorithms that delve through our digital data traces for the most relevant, most ‘likable’ media to feed us. In many ways, this unseen curation is a welcome convenience; sifting through and reasoning with seemingly endless online data and information is an unrealistic task for any human. Nevertheless, we begin to forfeit cognitive autonomy each time we delegate information gathering and evaluation to algorithms, in turn restricting our thinking to what the algorithms deem appropriate.
Interacting with these algorithms allows us to make sense of and participate in the flows of data constantly constructing the ways we work and live. Algorithmic decision-making — that is, automated analytics deployed for the purpose of informing better, data-driven decisions — epitomizes this phenomenon. And while a world directed by algorithms presents countless opportunities for optimizing the human experience, it also calls for reflection on the human-algorithm relationship upon which we now rely.
As our views on data shift from empiricism to ideology, from datafication to dataism, it is easy to get caught in the fervor. Countless articles call for transparency, accountability, and privacy in the roll out of algorithmic practices. These are of course noble (and often necessary) ideals — for example, data watchdog committees and legislative safeguards can ensure responsible development and implementation. Yet, many of these sweeping calls for oversight implicitly rest on unfounded assumptions about the socio-political impacts of algorithms. In turn, we wind up with a number of a priori hypotheses about how algorithms will affect society — and thus, claims about the steps we must take to regulate them — which are often premised on misguided assumptions.
For one, the conventional data dogma has warped and distorted the concept of the algorithm into some kind of agential, all-knowing, impossible-to-comprehend being. This misconception suggests that an algorithm possesses authoritative power in itself, when in reality any influence the algorithm may project is the result of human design and legitimization (Beer, 2017). In other words, as the role of algorithms evolves to a semi-mythical (perhaps deified) status from Silicon Valley to Wall Street, it is often forgotten that algorithms are a product of human effort, and subject to human control.