Katia Schwerzmann

The concept of alignment has undergone a remarkable rise in recent years to take center stage in the ethics of artificial intelligence. There are now numerous philosophical studies of the values that should be used in this ethical framework as well as a technical literature operationalizing these values in machine learning models. This article takes a step back to address a more basic set of critical questions: Where has the ethical imperative of alignment come from? What is the ethical logic of alignment—how, exactly, does it propose to regulate machines’ and peoples’ conduct? And what are the social and political implications of this ethics? After discussing the logical and normative implications of the term itself—in what sense alignment can have an ethical meaning—we undertake a four-part “anatomy” of alignment in contemporary large language models (LLMs): first, a relatively technical sense in sequence modeling; second, a more normative sense relating to how outputs of pre-trained models are ethically evaluated; a third sense where external values are introduced using fine-tuning techniques to manage undesired model behaviors; and fourth sense, where alignment is given extreme ethical stakes in philosophical discussions of existential risks. We find that the ethics of alignment is fundamentally concerned with the problem of control, with unintended model behaviors that arise from divergences between training objectives and the normative expectations that govern the contexts in which they are used. Alignment serves to bridge the gap between what we call an “is” normativity, of statistical patterns identified by models and an “ought” normativity where values are technically introduced in models to steer them away from undesired behaviors. By problematizing control, the ethics of alignment weakens capacities to both make more substantive ethical judgments and also political decisions about how to live with AI.

This paper reframes the issue of appropriation, extraction, and dispossession through AI—an assemblage of machine learning models trained on big data—in terms of enclosure and foreclosure. While enclosures are the product of a well-studied set of operations pertaining to both the constitution of the sovereign State and the primitive accumulation of capital, here, I want to recover an older form of the enclosure operation to then contrast it with foreclosure to better understand the effects of current algorithmic rationality. I argue that the act of enclosing is to be understood as a set of fundamental operations that consist in producing structural distinctions between inside and outside, inclusion and exclusion—whether by drawing lines on a map, constructing border walls, or by algorithmically categorizing and (mis)recognizing people and things. Tracking the transformation of an enclosure-logic into one of foreclosure, I show how current AI perpetuates and at the same time expends forms of extraction, exclusion, and dispossession toward a totalizing horizon. For, while the outside is essential and constitutive to the logic of enclosure, foreclosure, by contrast, is characterized by the “totalizing desire” to not leave anything out of it. The totalizing desire to move beyond the logic of the enclosure forecloses that the enclosure has just gotten bigger and that the operations of exclusion have been masked and displaced, making them harder to contest.

This paper reframes the issue of appropriation, extraction, and dispossession through AI—an assemblage of machine learning models trained on big data—in terms of enclosure and foreclosure. While enclosures are the product of a well-studied set of operations pertaining to both the constitution of the sovereign State and the primitive accumulation of capital, here, I want to recover an older form of the enclosure operation to then contrast it with foreclosure to better understand the effects of current algorithmic rationality. I argue that the act of enclosing is to be understood as a set of fundamental operations that consist in producing structural distinctions between inside and outside, inclusion and exclusion—whether by drawing lines on a map, constructing border walls, or by algorithmically categorizing and (mis)recognizing people and things. Tracking the transformation of an enclosure-logic into one of foreclosure, I show how current AI perpetuates and at the same time expends forms of extraction, exclusion, and dispossession toward a totalizing horizon. For, while the outside is essential and constitutive to the logic of enclosure, foreclosure, by contrast, is characterized by the “totalizing desire” to not leave anything out of it. The totalizing desire to move beyond the logic of the enclosure forecloses that the enclosure has just gotten bigger and that the operations of exclusion have been masked and displaced, making them harder to contest.

In this article, I show why it is necessary to abolish the use of predictive algorithms in the US criminal justice system at sentencing. After presenting the functioning of these algorithms in their context of emergence, I offer three arguments to demonstrate why their abolition is imperative. First, I show that sentencing based on predictive algorithms induces a process of rewriting the temporality of the judged individual, flattening their life into a present inescapably doomed by its past. Second, I demonstrate that recursive processes, comprising predictive algorithms and the decisions based on their predictions, systematically suppress outliers and progressively transform reality to match predictions. In my third and final argument, I show that decisions made on the basis of predictive algorithms actively perform a biopolitical understanding of justice as management and modulation of risks. In such a framework, justice becomes a means to maintain a perverse social homeostasis that systematically exposes disenfranchised Black and Brown populations to risk.

Graphische Markierungen bilden das Fundament unseres epistemischen und ästhetischen Umgangs mit der Welt. Die medialen Umwälzungen im digitalen Zeitalter haben die Relevanz des Graphischen sogar noch potenziert. Die "Theorie des graphischen Feldes", die an der Kreuzung von Philosophie, Ästhetik und Medientheorie verortet ist, bietet eine neue Reflexion über den Akt des Markierens in einer Zeit, die als jene der Entmaterialisierung von Information und Medien gilt. Über die Opposition zwischen medialer Transparenz und ästhetischer Opazität hinaus, auf die die neueren philosophischen Diskurse über das Graphische gemeinhin verweisen, geht es in dieser Theorie darum, die Körperlichkeit des Graphischen bis in dessen digitalisierte Form aufzuspüren. Das Graphische entsteht somit aus dem Kontakt dreier Körper: dem einschreibenden Körper, dem Trägerkörper und dem Markierungskörper. Die Untersuchung der berührenden Interaktion dieser Körper mündet in eine Theorie des graphischen Feldes, die es erlaubt, sich einerseits für die eigene Theoriebildung in den existierenden philosophischen Diskursen zu orientieren und andererseits graphische Phänomene zu kartographieren.

The sensory dimension of writing, which is never fully neutralised in the process of semiosis, remains aporetic in Derrida’s philosophy. I show how Barthes’ observations on pseudo-writing lead to his understanding of writing as a gesture, opening up post-structuralism to the body as absolutely non-repeatable, as the opposite of semiosis. The examination of Barthes’ account of the relationship between writing and the body leads to an aesthetic of physical responsiveness, which challenges the distinction between work, creator and viewer. In this regard ‘seeing’ can neither be reduced to the infinite decoding of signs nor to an immediate perception, but rather can be understood as the playful contact with signs. Barthes’ notion of writing contributes to overcoming the limits of Derrida’s account of the sensory quality of writing without jeopardiz- ing the philosophical gain of deconstruction.