Uncertainty-in-the-Loop – Sanela Jahić

As algorithms ripple through and integrate further with our work and daily lives, the future of work projections note creativity, flexibilty and innovative thinking as forecast skill demands that will be required of employees. These skills, which have traditionally also been attributed to the profession of the artist, are supposed to be among the ones most resistant to or the least exposed to automation. Many people think that what they do requires creativity, and that cannot be expressed in the form of executable code or emulated by a machine. Contemporary perception usually sees creativity as something new, divergent and original that takes people by surprise. And yet, many tasks which might entail human faculties such as intuition, empathy, and creativity, are already being outsourced to increasingly capable automated and automatising systems that just perform them in differently.

There are many ways to distill something into data points. In her recent work, Sanela Jahić converted her labour as an artist – her works, research and interests of the past 14 years – into data. As data, the features of her artworks become tables of numbers; each creative decision emerges in a row of digits. The artist then turned the decision making over to a predictive algorithm. The machine uses the dataset to sift through and identify patterns in her artistic labour in order to predict the content and aesthetics of her next artwork. The first stage of this multiyear project was presented at Aksioma in 2018 with the exhibition The Labour of Making Labour Disappear.

Artist talk with Sanela Jahić at Moderna galerija, Ljubljana, 15 January 2020

Yet such predictive power is limited in its forecast of future outcome, because it gives a selection from among ready-made choices. And even though this may be an original selection, as calculating predictor of the future it is still weighted down in the past. At every step of the way, the capability to create new choices is heavily constrained by choices that came before. In other words, conceiving artworks from the same conceptual depository is not really imagining something new, but a narrow alteration of existing inputs. To break away from the machine algorithm rolling out combinations of prior existing data, and to avoid sparking a feedback loop, the artist – in the final stage of this project – provides the machine a look at her contemporary investigations as an early window into the present disorganization of her thoughts. The algorithm then determines tomorrow’s artwork based on observations today with the past flickering in the rear–view mirror. On the basis of the predictive model, Pataka was created, a work that listens to what our voices tell machines about us.