In this paper I discuss examples of contemporary legal semantics associated with the ‘human in the loop’ using aspects of the work of Hans Blumenberg and Niklas Luhmann. Their work is relevant for at least three reasons. First, both thinkers utilised zettelkasten (slip-boxes), personally coded collections of notes and quotations written on index cards. Though inanimate, a zettelkasten has the capacity to surprise its creator with unexpected information and therefore, on Luhmann’s account, to partake in communication. This is authorship as the product of human observers capable of producing new information through observation. I suggest we can use this to reflexively observe the old cybernetic problem of the integration of psychic systems and information systems in decision-making. That distinction helps clarify the role of the observer of the legal subject of the ‘human’ differentiated from AI. Second, both closely studied the semantic shifts associated with humanity during the Enlightenment, arguing that rather than straightforwardly replacing God with homo faber, modernity radically decentred the figure of human in relation to both nature and technology. Humanity lacks ontological completeness; insofar as there is a ‘human nature’, it is one permanently compelled to reflexively adapt itself to a world/environment which it observes and from which it is constitutively differentiated. Technicization, on the other hand, is for both phenomenological; a historical mode of perception primarily understood as the coupling together of causal elements and symbols (regardless of the materiality or sophistication of those elements). The complexities of technological differentiation provoke responses both semantically but also organisationally and procedurally. This suggests that the figure of the human in relation to AI should be observed not just semantically but, crucially, for its organisational roles, internal and external to each organisation concerned. Third, both insist upon the contingency and uncontrollability of the world, and start with the axoim that their object of study – whether humanity or society – is improbable and contingent. Society is both autonomous and fragile. What is could be otherwise, or could not be at all. One consequence is that both throw into question simple causal connections between theory and practice. The link between theory and practice is historically recent, emerging in nineteenth century discourse linking science and technology. Practice can be theorised, but whether or not theory can be practiced is a question that theory cannot easily answer. If this is disquieting, it may also be liberating, for practitioners and theorists alike.

The figure of the Human-in-the-Loop has been much criticised in recent scholarship as an inadequate safeguard against both the potential and demonstrated harms of AI systems. This paper will take an historical approach to the figure, tracing its emergence in the mid-century NASA space program, and surveying the various transformations it has taken over the intervening decades, including at key inflection points such as the 1979 Three Mile Island incident. Drawing upon theories of 'afterwardsness' in the work of Jean Leplanche and Georges Didi-Huberman, this paper will argue that the Human-in-the-Loop is born as an anachronism, as a figure which is at once necessary and which yet appears too late to intervene 'adequately' in the complex industrial and post-industrial milieux in which it finds itself. What is at stake in tracing this genealogy of an anachronism is not to argue for the futility of human intervention in complex technical systems or to lament the passing of a more coherent technical agency. Instead, I suggest that it is this very 'out-of-timeliness' which is suggestive of the figure's potential to produce a creative reimagining of the terms of human autonomy in complex sociotechnical environments.

Most recommendation engines today are based on predicting user engagement, e.g. predicting whether a user will click on an item or not. However, there is potentially a large gap between engagement signals and a desired notion of "value" that is worth optimizing for. We use the framework of measurement theory to (a) confront the designer with a normative question about what the designer values, (b) provide a general latent variable model approach that can be used to operationalize the target construct and directly optimize for it, and (c) guide the designer in evaluating and revising their operationalization. We implement our approach on the Twitter platform on millions of users. In line with established approaches to assessing the validity of measurements, we perform a qualitative evaluation of how well our model captures a desired notion of "value".

Recent works on shared autonomy and assistive-AI technologies, such as assistive robotic teleoperation, seek to model and help human users with limited ability in a fixed task. However, these approaches often fail to account for humans' ability to adapt and eventually learn how to execute a control task themselves. Furthermore, in applications where it may be desirable for a human to intervene, these methods may have inhibited their ability to learn how to succeed with full self-control.We focus on the problem of assistive teaching of motor control tasks such as parking a car or landing an aircraft. Despite their ubiquitous role in humans' daily activities and occupations, motor tasks are rarely taught in a uniform way due to their high complexity and variance. We propose an AI-assisted teaching algorithm that leverages skill discovery methods from reinforcement learning (RL) literature to (i) break down any motor control task into teachable skills, (ii) construct novel drill sequences, and (iii) individualize curricula to students with different capabilities. We show that AI-assisted teaching with skills improve student performance by around 40% compared to practicing full trajectories without assistance, and practicing with individualized drills can result in up to 25% further improvement.

In their influential introduction to racial formations, Michael Omi and Howard Winant define race as an essentially visual phenomenon. They state that “race is ocular in an irreducible way. Human bodies are visually read, understood, and narrated by means of symbolic meanings and associations. Phenotypic differences are not necessarily seen or understood in the same consistent manner across time and place, but they are nevertheless operating in specific social settings” (2015, 28). In studies of media and digital culture, moreover, processes of racialisation have most often been conceptualised as operating in visual regimes and studied using visual methods. But how do we study race when its formations are primarily figured through regimes of computation that rely on structures that are, for the most part, opaque? How do we account for processes of racialisation that operate through proxies and abstractions that figure racialized bodies not as single, coherent subjects, but as shifting clusters of data? In this paper, we discuss the challenges of researching race within algorithmic culture. We argue that previous formations of race that had been dependent on visual regimes are now giving way to structures that manage bodies through largely non-visible (or invisual) processes. In this new regime, race emerges as an epiphenomenon of processes of classifying and sorting — what we call “racial formations as data formations.” This discussion is significant because it raises new theoretical, methodological, and political questions for scholars of culture and media. This paper asks: how are we supposed to think, to identify, and to confront race and racialisation when they vanish into algorithmic systems that are beyond our perception? How do post-visual regimes disrupt the ways we have traditionally studied race? And, what methods might we use to render these processes tractable — if not “visible” — for the purposes of analysis and critique?