From streaming platforms, to social media, and online shopping, consumer experience is increasingly subject to algorithmic customization. Artificial intelligence tools are deployed to sift through troves of consumer-generated online data in order to predict future market behavior and allow businesses to provide ever-more personalized content to individual users. Content providers deploy complex algorithms that learn to anticipate our preferences and, judging on our behalf, make suggestions for future consumption -be it music, film, fashion, or simply more scrollable content. The algorithm promises to render a certain type of aesthetic expertise obsolete. Our choices are increasingly subject to algorithmic processes, which seek to quantify our consumption patterns and to provide an ostensibly “objective” platform for the judgment of taste. The datafication of taste preferences carries a promise of not only objectivity, but also accuracy of prediction, to the point of complete individuation: to each their own indeed, readily available at the touch of one’s screen.
In this paper I ask how might AI reconfigure the role of tastemakers as it assumes the role of critic, curator, or expert? Furthermore, how might the sharing of taste transform in the face of algorithmic analysis and infinite, individualized, customization? Might big data, in its quest to quantify experience, finally challenge the age-old adage and manage to truly, literally account for taste in pure mathematical fashion?
