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Algorithms have developed into somewhat of a modern myth. On the one hand, they have been depicted as powerful entities that rule, sort, govern, shape, or otherwise control our lives. On the other hand, their alleged obscurity and inscrutability make it difficult to understand what exactly is at stake. What sustains their image as powerful yet inscrutable entities? And how to think about the politics and governance of something that is so difficult to grasp? This editorial essay provides a critical backdrop for the special issue, treating algorithms not only as computational artifacts but also as sensitizing devices that can help us rethink some entrenched assumptions about agency, transparency, and normativity.

Internet governance is a difficult horse to catch. Far from being a coherent field of study, it presents itself as scattered across a range of disciplinary approaches that come with distinct theoretical, methodological and analytical preoccupations. In this paper, we critically review existing literatures on governance of, on and through the internet and draw attention to the ways in which they help perform the worlds in which they have their place. Retelling the case of the 'Twitter Joke Trial', we highlight the contingent and at times conflicting roles attributed to people, technologies and institutions, as well as the concerns that come with these. Rather than striving for a coherent definition of 'internet governance', we draw on recent work in science and technology studies to show that acknowledging the performativity and multiplicity of different modes of governance can open up a productive line of inquiry into the recursive relationship between governance research and practice.

While search engines are discursively framed as automated, self-governing machines, they remain dependent on human laborers. This behind-the-screen workforce includes not only programmers and engineers but also categories of workers that have drawn far less attention. The latter include search quality raters, contingent workers tasked with supplying a "human check" on search algorithms. To explore the human–machine entanglements in search engine evaluation, we draw upon 21 in-depth interviews with raters located across the globe; we supplement our interview data with an analysis of worker training documents. Our findings reveal that despite Google's stated efforts to make algorithmic systems more sensitive to human subtlety, raters experience their work in ways that are—paradoxically—algorithmic. Indeed, workers framed their experiences through regimes of standardization, discipline, and invisibility. We conclude by discussing the broader implications of our analysis, including worker dehumanization and the exacerbation of power hierarchies in the global, on-demand economy.