Introduction
Artificial intelligence technologies (AITs) have gained increasing media attention, in particular since the roll-out of so-called generative forms of AI systems, such as ChatGPT that produce text based on probabilistic prediction of “characters, words, or sentences in a sequence” (Tarnoff, 2023). In response, the political and popular mood appears to fluctuate between a techno-optimist infatuation with the purported dramatic advances of ‘AI’ and its societal risks, including worrying failures. The growing number of applications based on AI technologies result in major governance demands for national, regional, and international policymakers to address (Paul, 2022). The emerging governance and regulation regime of AI technologies takes various forms, with both general and policy-field specific approaches being sought by public and private actors – or a combination thereof (Veale et al., 2023). AI governance outcomes sought after may be of a formal type, such as legislation, but also include informal principles and suggestions. Internationally, governance processes associated with, chiefly, public actors have already produced outputs, such as UNESCO’s Recommendation on the Ethics of AI (2019) and the OECD’s Recommendation on AI (2019). Another noteworthy example is the Group of Governmental Experts (GGE) on emerging technologies in the area of lethal autonomous weapon systems (LAWS). Working in the context of the United Nations Convention in Certain Conventional Weapon Systems (CCW), the GGE on LAWS has provided a forum for states to discuss challenges associated with integrating AI technologies into weapon systems since 2017. On a regional scale, the European Union (EU) has been developing its AI Act since 2021, which looks set to become the first set of regional, legally enforceable rules specifically pertaining to AI technologies. Further, the Council of Europe’s Committee on AI is working to develop a legal instrument with a drafting deadline in November 2023.
These diverse governance efforts offer scholars of norm research in International Relations (IR) manifold opportunities to study forums and processes where actors publicly express what kind of (governance) challenges they associate with AI technologies and begin to frame new norms, for example the EU’s vision of human-centric AI that is central to the AI Act (e.g. Amariles & Baquero, 2023; Carmel & Paul, 2022; Lukowicz, 2019). Norm research in IR has traditionally conceptualised norms as closely linked to international law. Early constructivist scholars, who introduced the empirical study of norms to the discipline, saw the institutionalisation of norms into soft or hard law as simultaneously the goal and the endpoint of norm emergence. Building on that, such scholarship focused on the specifics of state compliance with legally institutionalized norms via processes of diffusion, internalisation, and socialisation (Finnemore & Sikkink, 1998; Keck & Sikkink, 1998; Risse et al., 1999).
Later work in critical norm research has accommodated the inherent flexibility of norms by emphasising enacted ‘meaning-in-use’ (Wiener, 2009; Wiener, 2017) across regions and contexts, focusing therefore on the much more dynamic and interactive processes of localisation, implementation, and contestation (Acharya, 2009; Acharya, 2004; Betts & Orchard, 2014; Deitelhoff & Zimmermann, 2018; Drubel & Mende, 2023; Wiener, 2014; Zimmermann, 2017). Importantly, this line of research challenges a supposed linearity of norm development, as well as the questions of who has agency therein and whose agency counts (Bucher, 2014; True & Wiener, 2019; Wiener, 2018). This scholarship also explores the very production of normativity in contestation processes (Hofferberth & Weber, 2015; True & Wiener, 2019; Wiener, 2018), understanding contestation as a pivotal, norm-generative societal practice (Orchard & Wiener, 2022, 54). These studies often connect to soft or hard legally institutionalised norms or the public-deliberative process of drafting such norms and have produced many valuable insights.
But what such scholarship arguably does not account for is how the slow-moving nature of public-deliberative governance processes interacts with the, at times, fast pace of technological development, especially in (but not limited to) the field of ‘AI’. Even though several AI governance initiatives have yielded preliminary results in the shape of soft international law (e.g. sets of principles and non-binding resolutions), a majority of them are slow to move forward and often need re-adjusting to keep in step with technological change. While the EU’s AI Act has benefited from regulatory momentum, it still moves slowly through the EU’s institutions with more than two years passing between the presentation of its initial draft by the European Commission in April 2021 to its adoption by the European Parliament in June 2023. The GGE on LAWS remains at a discussion rather than at a negotiation stage and insiders have invariably described its pace as glacial or even tectonic (Régimbal, 2023). At the same time, applications utilising AI technologies are spreading across various aspects social life from medical care (Obermeyer et al., 2019) and hiring decisions (Ajunwa et al., 2016) to predictive policing (Lum & Isaac, 2016) and the approval of welfare beneficiaries (Eubanks, 2019). This development raises crucial regulatory questions because of the societal harms that such automated and algorithmic ‘solutions’ can produce, often reinforcing existing power asymmetries and hierarchies (Birhane, 2021b). Partly due to a perceived lack of expertise, partly due to concerns about stifling innovation (see UK Government, 2023), the governance of AI is cumbersome and dominated by the input provided by a narrow set of dominant stakeholders, including major tech corporations (Holland Michel, 2023, 4; Veale et al., 2023).
This essay intervenes in the debate about governing AI by reflecting on governance through international norms, which we define as understandings of appropriateness (Bode & Huelss, 2022, 12).1 We put forward two analytical additions to norm research in the discipline of IR, and, in particular, how norms emerge in IR, with the ambition of shaping a research programme on practical normativity/normality. (1) We focus on how, often operational, practices of designing and using AI technologies that are performed by actors at sites outside of the public eye become sources of normative substance on ‘AI’. This departs from a present focus in IR norm research on practices that are observably performed in public spaces, either in the form of “deliberation”,2 i.e. how actors publicly discuss, consider, and verbalise norms, or via behavioural contestation (Stimmer & Wisken, 2019; True & Wiener, 2019). But norms are not only constituted via practices associated with the public and/or deliberative space where they may transition into law, but also unfold in operational practices of design and use that not subject to public debate, discursive codification, or public performance. This argument builds on scholarship at the theoretical interface of IR norm research and international practice theories (IPT) (Bode & Karlsrud, 2019; Bode, 2023b; Gadinger, 2022; Laurence, 2018; Pratt, 2022; Ralph & Gifkins, 2017; Wiener, 2022), but also draws from critical security studies, as well as science and technology studies (STS). Approaches that merge IPT and norm research typically emphasize the relational, unstable dimension of norms, evidenced by their preference for concepts such as “normative configuration” or “normative substance” as alternatives to the seemingly well-bounded ‘norm’ (Pratt, 2020; Pratt, 2022; Qiao-Franco & Bode, 2023; Bode, forthcoming).
(2) We consider how practices of designing and using AI technologies shape normative substance in an interplay between normativity, i.e. notions of moral duty and justice, and normality, i.e. notions of the typical and average (Huelss, 2017). IR norm research that focuses on publicly performed practices often, implicitly rather than explicitly, emphasises the dimension of normativity. By contrast, the dimension of normality and the relationship between the two concepts have only received little dedicated attention (Gholiagha et al., 2021; Huelss, 2020). However, both aspects are important to perceive and understand “previously obscured patterns and processes” (Bernstein & Laurence, 2022, 77) in the relationship between norms and practices, especially when it comes to AI technologies.
These arguments are abductively informed by our empirical study and analysis of military applications of AI, in particular, of how AI and autonomous technologies have become incorporated into weapon systems. This development is typically connected to the catch-all term autonomous weapon systems (AWS) as systems that can ‘make’ targeting ‘decisions’ without immediate human intervention.3 Targeting algorithms, for example, introduce forms of non-human centred ‘decision-making’.
Before we proceed, let us define what we mean by ‘AI’ and associated terminology. We use the term ‘AI’ to refer to a broad area of scientific study that encompasses techniques like machine learning as well as research areas like computer vision or natural language processing. AI is the effort “to create machines or things that can do more than what is programmed into them” (Gebru, 2023). We supplement the term ‘AI’ regularly with that of technologies, thus leading to the acronym AITs. In this, we are inspired by the critical public policy scholarship of Emma Carmel and Regine Paul who use AITs to highlight the complex, contingent, and variable insertion of ‘AI’ into the societal domain (Carmel & Paul, 2022; Paul, 2022). As a result of its frequent, ambiguous usage, ‘AI’ is a highly divisive term that is prone to numerous uncertainties (Afina, 2022; Holland Michel, 2023; Tucker, 2022). Problematically, the term ‘AI’ may, for example, lead us to consider machines with intelligence as equal to humans, thereby potentially triggering irrelevant sets of questions (Gunkel, 2023). As Whittaker critically contends, the publicly reported ‘advances’ in AI research are not the result of technological breakthroughs but “the product of significantly concentrated data and compute resources that reside in the hands of a few large tech corporations” (2021, 51). Further, we speak of both AI and autonomy as these are closely related and arguably function along a trajectory of increasing technological complexity. Autonomy can be defined as the “ability of a machine to do perform a task without human input” (Scharre & Horowitz, 2015, 5). As such, a weapon system may, for example, integrate autonomous or AI technologies to perform different kinds of functions ranging from intelligence analysis to mobility and targeting, with targeting functions having, by far, garnered most attention (Boulanin & Verbruggen, 2017, 19–35). Finally, we follow Virginia Dignum’s definition of an algorithm as “a set of instructions, such as computer code, that carries out some commands” (2019, 3). Algorithms are used to ‘find’ patterns in datasets and “specify how to transform a given set of inputs into a desired output” (Mackenzie, 2015, 43).
The remainder of this essay reviews our two analytical arguments outlined above and reflects on how they can advance a new research agenda of practical normativity/normality for the specific purpose of studying the impact of AI technologies on normative substance in international relations. We structure the two subsequent sections to include both a review of how far literature on practical normativity/normality has come, followed by an expanded discussion of our arguments on how operational design and use at sites beyond the public eye shape normative substance, as well as normativity/normality dynamics.
Practical normativity/normality I: practices of developing and using AI technologies as sources of norms
Sociology and political theory have long captured practices, broadly understood as patterned interactions in social context (Leander, 2008, 18), as the micro-level building blocks of the social world. As “nexuses of doings and sayings” (Schatzki, 2012, 14), practices make norms. On the ontological level, practices and norms are therefore inseparable, since action-relevant normative substance only manifests in practices (Bode & Huelss, 2018; Gadinger, 2022; Neumann, 2002, 200; Wiener, 2009; Wiener, 2018). This insight also applies to cases where a legal-institutionalized norm already exists that supposedly guides practices through its constitutive quality. The practical implementation of a norm always contains an element of improvisation and with it the potential to change its meaning (Bode & Karlsrud, 2019; Huelss, 2017; Hofferberth & Weber, 2015; Pratt, 2022; Wiener, 2018). In principle, dynamic practices can thus create new norms, maintain, and stabilize existing norms, and change the substance of existing norms. Indeed, this process may increase a norm’s legitimacy (Wiener, 2018).
How practices maintain, stabilise, or change the substance of existing norms has already been well-covered in IR norm research as part of different sub-agendas on the resilience, robustness, and erosion of norms, focusing on deliberation, communication, and public-discursive exchange (Barbé & Badell, 2023; Deitelhoff & Zimmermann, 2020; Lantis & Wunderlich, 2018). From the start, IR norm research has directed attention to how norms emerge in deliberative, international forums and therefore, conceptualized normative content as produced in public debate and international negotiation. What matters in this process are language-based strategies of presenting an argument such as framing, salience, and grafting (Acharya, 2004; Keck & Sikkink, 1998; Rosert, 2019). Indeed, “states are very verbal entities” (Hansen, 2006, 21). As Bower argues, “multilateral negotiations serve as focal points in the crystallization of emergent norms” (2015, 355).
Further, both localisation and contestation scholarship has invested much attention into offering deeply theorised thinking of how practices generate and shape normative substance. Contestation research, in particular, departs significantly from models considering norm emergence as sequentially distinct from the diffusion of norms, acknowledging instead the “norm-generative power that materialises through contestation” (Wiener, 2018, 11). That power is vested in the public space and is often, although not necessarily, connected to public-deliberative processes. It is thus crucial whether and to what extent actors have access to the forums where the constitutive debate about norms happens, thereby being able to ensure that their voices are heard (see Wiener, 2018). In broader IR norm research, this public-communicative access has also manifested in agency-oriented concepts such as norm entrepreneurs who “attempt to convince a critical mass of states […] to embrace a new norm” (Finnemore & Sikkink, 1998, 895). The reverse concept of “norm antipreneurs” (Bloomfield, 2016) likewise describes agents working through public-deliberative means.
Wiener’s theoretical understanding of contestation explicitly goes beyond deliberation: “while mostly expressed through language not all modes of contestation involve discourse expressis verbis” (2014, 1). She includes explicit forms of contestation, such as contention, but also implicit forms, such as the absence of practices (neglect) and practices that contradict a certain reading of a norm (negation, disregard) (Wiener, 2014, 7). In later conceptual and empirical work, contestation scholarship has begun to explore the behavioural dimension of contestation as “contestation by means of action that affects implementation” (Stimmer & Wisken, 2019). Yet, interestingly, even the types of practices that animate behavioural contestation are performed in and attached to some form of public space (e.g. True & Wiener, 2019, 560, 563, 570). Therefore, they become directly observable.
This (methodological) move shows that current contestation scholarship despite recognising the norm-generative power of practices does not engage comprehensively with theorisations of practice offered by IPT. IPT considers practices, often habitual and routine, as the micro building blocks of all that is social. Many of these practices are not directly observable by or accessible to researchers because their performance happens outside the public space and does not leave discursive trails. As an analytical programme, IPTs has therefore been deeply invested in methodologies, praxiographies, to make these practices visible via, for example, participant observation or interviews (Adler-Nissen, 2016; Bueger, 2014; Nicolini, 2013; Pouliot, 2016).
Our arguments add to this existing body of knowledge in two ways: we broaden analytical attention (1) by expanding the types of practices whose performance generates/shapes/sustains normative substance beyond practices that are, in some shape or form, publicly performed, voiced, framed, or deliberated; and (2) by expanding the social sites where such practices are performed to sites outside of or hidden from the public eye. This practical normativity/normality is particularly well-suited to understanding the impact on normative substance that AI technologies in and beyond the military can unfold (Bode, 2023b).
A closer look at technologies in the military domain offers useful analytical inspiration for this line of thinking. Military technologies and weapon systems are often developed and deployed over many years before they even become the subject of public debate, leading to the public-deliberative emergence of new norms that can/should regulate existing practices (Bode, 2023b). Anti-personnel mines, for example, have been used since World War II on a wide scale, but public discussion of the practices associated with their use only began in the 1970s under the CCW, culminating in the 1997 Ottawa Treaty that banned anti-personnel landmines. A time lag between the development and deployment of military technologies and a public debate about their use is typical, as the further examples of submarines, chemical weapons, nuclear weapons, or cluster munitions show.
But even without practices performed in a public or deliberative space, other types of practices create, shape, sustain, and change normative substance. In the cases described above, these are practices associated with designing, training personnel for, and using military technologies. Practices of design, for example, anchor normative substance in AI technologies. Here, we follow constructivist STS insights, in understanding technology as fundamentally social and thus as a reservoir of social practices (Bijker & Law, 2010). These assumptions contrast with a positivist conceptualization of technology development as part of objective-neutral processes of experimentation and problem-solving. By contrast, STS thinking allows us to conceptualise the insertion of the material in the social. This perspective re-thinks existing dualisms, such as discourse vs. materiality, in favour of a “process ontology” that understands practices as relationally co-constituted by the social and the material (Schouten & Mayer, 2016, 311). It also reverses a conventional separation between social actors, on the one hand, and technologies as materiality, on the other hand, in favour of an interactive perspective on how these interact in performing technological practices (see also section 2). STS thinking therefore allow us to question the distinction between technological factuality and value-based, normative decisions.
In the design, development, and use processes of AI technologies and algorithmic systems, practices can constitute/shape normative substance in at least three ways: (1) All AI technologies are based on the generation of computer models that ‘translate’ complex social activities into a problem to be solved and set a planned goal for this purpose (Gillespie, 2014). This process is vaguely understood to be objectivist, but instead actively encodes values and therefore types of normative substance into AI technologies. As Abeba Birhane and colleagues have found in their survey of 100 highly cited machine learning papers, values such as ‘performance’ and ‘efficiency’ significantly outrank considerations of societal need or negative potential (2021). In AWS, civil society organisations have investigated how the generation of computer models entails the construction of target profiles, “the set of conditions under which such a system will apply force” (Moyes, 2019, 1). Reviewing such target profiles demonstrates how they implicitly encode normative choices taken by the designers/developers in the AI or autonomous technologies that animate them. Current target profiles are basically particular patterns of sensor data – in the form of acoustic signatures, radar signatures, heat-shapes, or visual information – that are interpreted as a target. As such, target profiles will contain both intended and unintended targets, because a broad target profile reduces the number of false negative results that the military actors using the AWS want to avoid (Moyes, 2019, 4–5). In this way, target profiles shape normative substance at the stage of design practices in choosing instrumental parameters such as acceptable error rates for determining targets (Crawford, 2021, 96–97).
As the technology used in AWS grows in complexity, targeting profiles could become adaptive rather than based on fixed, encoded goals set by human designers (Moyes, 2019, 4). Using AI technologies, the system may suggest novel targets gleaned, for example, based on correlating multiple current forms of sensor data it has access to as part of a networked system with previous sensor data. When it comes to military decision support, systems that enable precisely these practices have already been used on the battlefield. As public sources reported, Ukrainian military commanders use the “AI Platform for Defense” (AIP), an AI-assisted software produced by Palantir, to provide them with “likely scenarios for the course of the war and […] spit out automated field moves” (Fischer, 2023; see also Hudson & Khudov, 2023; Ignatius, 2022). Thus, such design practices encode value-based decisions that are not disclosed into the targeting programming.
(2) The composition of the data sets used to program the algorithms that sustain AI technologies may contain data that does not cover the problem to be solved in a representative way, i.e. it may contain bias (Sharkey, 2018). It is a well-known adage that an AI system can only be as good as the data it is trained on. Critical studies or audits of prominent image datasets, such as Image Net, have therefore demonstrated that AI technologies replicate and augment racial and gender stereotypes and prejudices (Bolukbasi et al., 2016; Chandler, 2021; Crawford, 2021, 96–97; Hooker, 2021). Well-known examples include systematically excluding women in results for occupations or associating women with particular types of occupations only, and systems performing best in classifying light-skinned males as human (Benjamin, 2019; Buolamwini & Gebru, 2018; Birhane, 2021a). Such datasets therefore include assumed patterns of normality that become the basis for and amplified by the AI technologies that use them as training material.
(3) Practices of using systems integrating AI technologies, including weapon systems, are typically based on certain, not openly stated assumptions, including about human-machine interactions. The literature on autonomous systems refers to such assumptions as myths (Bradshaw et al., 2013; Johnson et al., 2014), while policy literature on AI talks about “technological myths” (Natale & Ballatore, 2020) or simply common policy assumptions that need recalibrating (Holland Michel, 2023). A persistent myth of autonomous systems is the “erroneous idea that once achieved, full autonomy obviates the need for human-machine collaboration” (Johnson et al., 2014, 58). However, the use of weapon systems integrating autonomous technologies, such as the Patriot missile defence systems, shows the opposite: introducing autonomous and AI technologies increases the complexity of tasks to be performed by human operators and demands more, not less, “human expertise and adaptative capacity” (Johnson et al., 2014, 84). Yet, such assumptions continue to shape use practices of AWS, such as the level of trust placed in system outputs or the involvement of the human operator in the decision-making loop of weapon systems (Bode & Watts, 2021; Bode & Watts, 2023), with clear effects on normative substance.
In summary, a focus on the operational practices of designing, training for, and using AI technologies enables an analytical view on modalities and sites where norms can emerge outside of the public space and a form of public debate. This perspective can diversify how and where we can localize norms on AI. Conceiving of practical normativity/normality beyond the public space thus offers a potentially far-reaching review of how normative space is constituted. Although we draw these insights inductively from analysing autonomous and AI technologies, they point to a broader empirical phenomenon: the initial emergence of norms in operational practices, which become objects of public deliberation or observable in the public space only at a later point in time or do not become a part of public debate at all (Bode, 2023b).
Of course, we should not fail to recognise that such practices are still performed within a potentially dense normative structure. State actors who develop and use weapon system integrating AI and autonomous technologies, for example, do not operate in a normative vacuum but within the principles and prohibitions of international law, for example international humanitarian law (IHL). But international law provides practices with a thoroughly ambiguous, sometimes contradictory, and potentially enabling normative basis (Hurd, 2017; Koskenniemi, 2011; Wiener, 2009). Based on existing international law, there can be very different judgments as to which practices are ‘appropriate’, and states therefore can have considerable practical and interpretative leeway (Bode, 2023a).
Practical normativity/normality II: the dynamic interplay between what is normative and what is normal
Through an emphasis on how practices of design, of training personnel for, and of using AI technologies performed at sites out of the public eye can become sources of normative substance, the interplay of normativity and normality also comes into focus. Practices can constitute ideas about which oughtness, justice, and which moral duties apply (normativity) - but they can also make something appear normal or average behaviour (normality). So far, norm research in IR has only theorised the normality dimension of norms in a limited way and has, as a result, not examined how it interacts with normativity. But we can draw inspiration from IR scholarship beyond norm research. Critical security studies, such as scholarship associated with the so-called Paris School, for example, consider the emergence of normality within practices of exclusion and regulation (C.A.S.E Collective, 2006). Here, understandings of the ‘normal’ arise in a process that compares, weighs, calculates and executes resulting in and based on identifying the ‘average’.
This work builds on Michel Foucault who offered an insightful theorisation of how social norms emerge beyond the law in his lectures on the History of Governmentality between 1977 and 1978. Foucault draws attention to the fact that the average is often considered the norm, but that this ‘norm’ is not identical with legal normativity (Huelss, 2017). To distinguish between the two processes and deviating from everyday language terms, he referred to the effect of legal norms as a process of normation, whereas a process of normalization refers to how norms emerge in the derivation from the average such as the statical normal distribution (Bell curve).
Foucault’s thinking offers interesting links to international practice theories as the process of entrenching and spreading certain practices over time can lead to them becoming routine and therefore normalise practices that can become the norm outside of formal codification or acts of norm creation. Making something normal, normalising practices, is related to notions of “the common, the ordinary, the standard, the conventional, the regular” (Cryle & Stephens, 2017, 1). However, perceptions of normal behaviour also overlap with understandings of appropriateness (normativity). What counts as “appropriate” behaviour is therefore not only defined in public deliberation, but also generated by practices in various forms. This is particularly relevant when understandings of the substance of deliberative norms collide with the normativity of practices. The normativity constructed in practices can be more impactful than what is discursively established. The question is here whether norms normalize practices by shaping what is appropriate in a given situation. Or whether practices rather normalize norms by shaping their substance based on what is established as normal.
In the context of AWS, practices associated with the design and use of AI technologies, can ‘normalize’ in at least two ways: (1) the algorithmic definition of parameters and target profiles at the point of design determines what counts as ‘normal’ and what counts as ‘abnormal’ or anomaly. This encoded definition represents a clear limitation of what is conceivable as normal and abnormal in the future. AI technologies based on the technique of machine learning are particularly important to consider here, for example object recognition as part of the image-processing of computer vision. In recent years, the US military launched projects with significant financial budgets to conduct relevant research in this direction through collaborating with tech companies. Project “Maven”, initially carried out in cooperation with Google, should be mentioned here as a prime example (see Suchman, 2020).
When examining patterns of normalisation, the use of ‘unsupervised learning’ applications in the context of machine learning is noteworthy. Here, algorithms aim to identify patterns of interest in the data that represent an anomaly rather than searching for patterns that it has been trained on (Aradau & Blanke, 2018). A widely discussed empirical example of unsupervised learning in the military context was the US National Security Agency's (NSA) Skynet programme, which strives to detect movement patterns of mobile phone users. Controversially, this data could also serve to support target identification for drone strikes. However, public reporting showed that the system mis-identified prominent Al-Jazeera journalist Ahmad Muaffaq Zaidan in Pakistan as a member of terrorist organizations such as Al Qaeda and therefore as a potential target (Currier et al., 2015; Robbins, 2016). The travel and movement patterns associated with his investigative work apparently generated an anomalous or suspicious movement profile (Aradau & Blanke, 2018; Bode & Huelss, 2021; Huelss, 2020). The processing and use of electronic data is a common practice in the US military, as confirmed by the National Security Agency (NSA). The former director of the NSA and CIA, retired US Air Force General Michael Hayden, explicitly underlined: “We kill people based on metadata” (cited in Scahill & Greenwald, 2014). Here, we see the prevalence of electronic normality detection over deliberative normativity considerations. In other words, what machine-learning applications identify as an anomalous data pattern has very direct consequences for life and death decisions with high normative salience. Identifying a data anomaly as something abnormal that stands outside of normativity and needs to be normalized, brought in line with the normal, or rather eliminated, is central to this process that animates how technological practices may shape norms. While the kill decision is not automated in scenarios such as the Skynet programme tests, the degree of human control is decisive for the extent of machine induced norm emergence and research shows that human control is often severely limited (Bode & Watts, 2021; Bode & Watts, 2023; Bode, 2023b).
(2) Practices associated with the design and use of AI technologies can also ‘normalize’ by informing and influencing human actors as part of human-machine interaction. Regardless of the extent to which the “machine” makes autonomous decisions, the repeated, collective execution of a similar range of practices based on human-machine interaction over a period of time can come to be considered “normal”, increasingly “acceptable”, perhaps even desirable. In these situations, we may find an interactive exchange between normality and normativity, in which incrementally changing, collectively held understandings of appropriateness develop.
As these illustrations show, the relationship between normality and normativity remains a research-analytical challenge and requires further, detailed studies to show the possible trajectories of transition from the normal to the normative. These trajectories do not only concern how the normality of practices can influence legally codified norms as a postulated expression of normativity and make inroads into deliberation. They also raise more fundamental questions about the extent to which long-established, legally codified norms are informed and shaped by normality. “Fundamental norms” (Wiener, 2007), such as human rights, for example, often align with dominant practices in certain cultures. Such practices typically preceding a formal, legal codification. Given such possible dynamics, we could reconsider whether the effect of codified norms and the importance of deliberation are overemphasized in conventional, constructivist norm research – if such norms may correspond to normal, average behaviour anyway.
Returning to the empirical field of AI technologies, we hold that the emergence of a new, post-human actor quality in practice is one of the great challenges that contemporary norm research encounters. Distributed forms of agency between humans and ‘decision-making’ technology that arise here contrast with the established analytical categories of human actor and normative structure. Studies at the intersection of STS studies and the “new materialism” have begun to take up this question of agency (Coole & Frost, 2010; Coole, 2013). A detailed discussion of this themes is beyond the scope of this essay, but we want to note how ‘distributed agency’ emerges in the interaction of people and the material. This has conceptual similarities to Latour’s “actant” (2005), but differs in defining agency capacities more narrowly (Coole, 2013, 456–61). This form of agency does not replace human actors but is vested in the emergence of new systems where people and technologies have agency capacities in a material and a relational sense.
The conventional view of agency and structure in norm research might no longer do justice to these technological changes since the influential premises of structuration theory assume a logical and temporal separation of both dimensions. But what if practices performed by AI technologies or as part of human-machine interaction override public, human agency-based deliberations of normative substance? As discussed above, machine learning may lead to the production of normative substance based on data patterns, even if such algorithmic ‘decision-making’ is no longer comprehensible and verifiable by human actors. The introduction of algorithmic ‘decision-making’ is also an onslaught on the human ability to doubt (Amoore, 2019). The neat outputs of AI that are based on a vast number of complex calculations are ultimately impossible to question in the logic of technology. Especially in the military context, but also beyond it, factors such as speed, lack of time, and high data load mean that it is no longer possible for human operator who are in-the-loop of weapon systems to meaningfully control the prompts offered by a system integrating autonomous or AI technologies (Bode & Watts, 2021; Bode & Watts, 2023).
What is more, how machines process information, identity anomalies, produce assessment about what constitutes an ‘appropriate’ action, and execute these actions collapses an ideal-type image of control and deliberation. In other words, systems integrating AI or autonomous technologies – regardless of whether a human is monitoring the system (in-the-loop or on-the-loop) – can create normative substance through analysing sensor input at negligible time intervals, detecting deviations from “the normal”, and reacting to this deviation within the given parameters, for example by destroying the target. Therefore, machines are becoming de facto agents who create, maintain, or change forms of normative substance through performing practices. This resulting blend between practical normality/ normativity is a new dimension that should receive more attention in IR norm research. It may also result in fundamentally reconsidering the stability of norms over time: practical normality/normativity emerges in highly dynamic processes that no longer correspond to the idea of a changeable but largely stable normative structure.
