1 Introduction

The promise of artificial intelligence (AI) has brought reverberating impacts throughout aspects of everyday life, from chatbots to education to transportation, but nowhere has it been felt as decisively—or controversially—as in warfare. The advancement of Lethal Autonomous Weapons Systems (LAWS) has been a strategic goal of militaries for decades, driven by the hope of deploying agents that are more agile, precise, and capable than human soldiers, all while removing human combatants from immediate physical risk. However, the ethical implications of delegating the use of lethal force to non-human entities are frequently called into question.

In “Killer Robots,” one of the seminal papers on the ethics of LAWS, Robert Sparrow seeks to identify “who we should hold responsible when an autonomous weapon system is involved in an atrocity of the sort that would normally be described as a war crime” (Sparrow 62). Sparrow constructs a trilemma suggesting neither the programmer, the commanding officer, nor the machine itself can be held morally responsible for the actions of a truly autonomous system. He concludes that because a fundamental condition of jus in bello (the conduct of war) is the ability to locate responsibility for the death of enemies, the deployment of LAWS is ethically impermissible.

In this paper, I argue that Sparrow’s conclusion rests on a conceptual conflation of functional autonomy with moral agency. Specifically, I posit that AI, regardless of its computational sophistication, lacks the capacity for moral intent. By distinguishing strictly between functional autonomy (the ability to operate without human intervention) and moral agency (the capacity for moral reasoning), we can resolve Sparrow’s responsibility gap. Sparrow assumes that because a system can operate independently of human intervention, it enters the sphere of moral accountability. I challenge this premise. I argue that autonomy in a machine is a description of its capabilities—its ability to execute complex instructions in dynamic environments—whereas moral agency requires an internal qualitative state (such as the capacity to care or suffer) that defines moral intent.

By distinguishing strictly between these two categories, we resolve Sparrow’s responsibility gap. Even if an AI becomes fully autonomous in its decision-making loops, it remains an instrument devoid of moral will. Therefore, the failure of such a system is not a moral transgression by the machine, but a failure of safety engineering or deployment. I argue that the gap Sparrow identifies is not a moral vacuum, but a legal and regulatory challenge best addressed through the frameworks of negligence and risk, drawing on analogies to pre-autonomous weaponry and autonomous civilian vehicles.

Finally, using the risk-based frameworks proposed by Thomas Simpson and Vincent Müller, alongside the critique of human decision-making by Aziz Huq, I argue that if AI systems reach a certain degree of safety and capability superior to human operators, there is an ethical case for their adoption. The demand for “human in the loop” often prioritizes the psychological need for a scapegoat over the substantive goal of minimizing harm. Ultimately, if LAWS is calibrated to reduce civilian casualties, the absence of a moral agent to “blame” is an acceptable trade-off for a safer reality of warfare.

2 Sparrow’s Trilemma and Moral Intent

Sparrow’s central argument relies on a specific scenario: an autonomous weapons system commits an act that, were it performed by a human, would be a war crime (e.g., bombing a surrendering unit). Sparrow argues that for war to remain a moral enterprise rather than mere slaughter, someone must be responsible. He then systematically attempts to absolve the three most likely parties—the programmer, the commanding officer, and the machine—from blame, creating a “responsibility gap.”

However, this trilemma rests on a conflation of functional autonomy with moral capacity. Sparrow treats the autonomous machine as an entity capable of usurping the moral agency of its creators, much like a child growing into an adult. This anthropomorphization of artificial intelligence systems obscures the reality of the sophisticated, albeit machine, computation happening underneath.

2.1 The Absence of Moral Intent

It is crucial to define the ontological status of current and near-future AI. We train AI on vast amounts of data, asking it to mimic human decision making, language, speech, and more. As training scales increase, emergent capabilities that were not thought possible of binary machines have appeared in deep learning systems, from complex “chains of thought” before generating responses to superhuman capabilities in some areas. Ultimately, current and near-future AI systems have no way of thinking for themselves¹ in the way that humans do²: all of their decisions or generations are based on their training data.

To the extent that futurists predict systems capable of recursive self-improvement without human oversight, one might argue for the eventual emergence of genuine moral agency. However, this theoretical possibility does not undermine the argument regarding LAWS; rather, it shifts the category of the debate entirely. As Sparrow himself observes, if a machine were to achieve the status of a moral patient, the primary military justification for its deployment—reducing the risk to moral patients (human soldiers)—would be nullified. The machine would no longer be a disposable weapon, but a conscript with its own rights. Therefore, the ethical debate concerning LAWS qua weapons systems necessarily presupposes that they remain non-sentient instruments. Consequently, this paper proceeds on the premise that these systems, regardless of their capability, remain sophisticated tools devoid of moral agency.

The reason the debate over LAWS reaches an impasse regarding accountability is that critics such as Sparrow attempt to locate moral responsibility within a system that lacks moral intent. In criminal law, a crime usually requires both an actus reus (guilty act) and mens rea (guilty mind).

Consider a human soldier who bombs non-combatants because of their skin color. We can identify this soldier as racist, as they made a decision to cause immense harm based on a corrupt moral calculus. They possess moral intent. Now, consider a LAW that bombs the same group because its training data contained a bias that associated a certain visual demographic with combatant status. The outcome is identical, but the internal process is radically different. The LAW did not make an “immoral” decision; it executed a command that was derived from negligent programming, selection of datasets, or safety checks that were not rigorous enough.

If a precision-guided missile suffers a guidance system failure and strikes a civilian building instead of a military target, no one charges the missile—or its operator—with a war crime in the sense of intentional slaughter. It would likely be treated as a tragic accident or a failure of due diligence. This is acknowledged by Sparrow, who says “accidents do happen, and even more so in war. However, these accidents represent regrettable, if inevitable, failures to live up to principles of justice in war fighting” (67). The debate over LAWS is clouded because the system’s autonomy emulates agency. When Sparrow wrote “Killer Robots” before deep learning existed, he hypothesized that “autonomy and moral responsibility go hand in hand” (65).

However, the development of modern deep learning systems has empirically demonstrated that this coupling is false. We have discovered that functional autonomy—the ability to operate independently in dynamic environments—can expand indefinitely without ever generating moral agency. The error lies in conflating the selection of means with the selection of ends. As Simpson and Müller note, true moral autonomy involves the “ability of a system to decide on its own ends” (Simpson & Müller 312)³. Deep learning systems, no matter how complex, are fundamentally optimization engines. They do not choose their own ends; they optimize for a reward function or objective parameter set by human designers. An AI like AlphaGo might make a move (a means) that its creators could never have predicted to win a game, but it has no capacity to question whether it should play Go or whether winning is a worthy goal (the end). Its “autonomy” is merely an expansion of its capability to solve problems, not an emergence of will. Once we strip away the “humanness” of autonomy, then the repercussions for LAWS failures fall squarely into the realm of negligence, resolving the gap Sparrow feared.

2.2 Addressing the Child Soldier

Sparrow’s most powerful use of analogy to support his argument against LAWS is that of child soldiers. He argues that a commander who sends a child into battle creates a dilemma: the child is not fully responsible, but the commander cannot control the child’s autonomous actions. As the child does not understand the full moral dimensions of what they do, they are not appropriate objects of punishment. However, their autonomy ensures that those who order them into action do not control them.

My issue here lies not with the accountability in the case of a child soldier, but the analogy to AI. It is true that the programmer or commander cannot control AI’s every action. The purpose of general learning in agents is to be able to both encounter and train behavior in all contexts that are expected, as well as to generalize when the context was not trained on. However, the issue here lies with the range of what a child can do. The reason a child soldier becomes more dangerous than an adult soldier is because commanders can communicate the same intent, but they cannot be sure that the child will understand and execute the order without unintentionally doing something bad (e.g. killing and torturing civilians).

A child that engages in a moral wrong without knowing is not responsible because they lack a fully developed moral compass. However, an AI that engages in what would humanly be considered a moral wrong cannot be its own fault because it executed that action in accordance with its most probable actions given context. On a machine level, the LAWS presumably wrongfully kills a noncombatant because it misidentified them as a combatant, or was prioritizing something else over the life of the noncombatant. As a machine, it is possible (and perhaps necessary) to implement rule-based instructions that cannot be overridden such as not killing anyone who is identified as a non-combatant; neglecting to add that rule would result in responsibility of the programmer. Training a “soldier/civilian identification” network that is overly faulty, which could lead to mislabeling civilians as soldiers and attacking them, would also be neglect on the part of the programmer, or neglect on the part of the commanding officer for still allowing such a system to be deployed. In more complex and unexpected scenarios where behavior is typically less predictable, AI is typically trained to mimic a set of human-curated values rather than operate on more unformed values: the risk present, then, is not a product of the autonomy of the AI, but rather the degree of care in programming the system and the degree of caution in deploying the system.

Although there is a sense in which AI is analogous to a child in that they can commit moral wrongdoings⁴ without moral intent themselves, the difference is that AI is much more controllable and actions that cause harm can be completely restricted in high-risk, out-of-distribution situations. A child may encounter many scenarios that they haven’t seen before and commit moral wrongdoings because their moral compass is not fully developed, but an AI is meant to have seen all possible scenarios given by the data already available and the compass by which it operates on mimics a human-curated set of values, with strict safeguards also an option.

2.3 Re-evaluating the Programmer

Sparrow argues that programmers cannot be held responsible because “the possibility that an autonomous system will make choices other than those predicted and encouraged by its programmers is inherent in the claim that it is autonomous” (Sparrow 70). He draws an analogy to parents, who are not responsible for the crimes of their adult children.

First, assume that a LAWS does not self-evolve. Then, the AI would never evolve beyond the instruction of its “parent,” the programmer. In this case, my argument against the child soldier analogy encapsulates this as well. However, Sparrow also mentions the possibility of an autonomous system that learns from its own experiences, which adds a degree of freedom between the action and the programmer.

If a LAWS becomes self-evolving, the responsibility to craft such a feature and be able to monitor robot behavior, though, should be the job of the programmer. The added risk of deploying a self-evolving machine that has less safety guarantee than one that maintains the policies that were trained into it should then lie with the commanding officer, if they choose to deploy it.

2.4 Re-evaluating the Commanding Officer

In the domain of non-autonomous weapons, it is precedent that when the weapons kill people other than their intended target, the commanding officer is still responsible for the deaths caused. Then, the argument for the commanding officer taking responsibility is on the basis that the risk of the machine going awry is accepted when the decision is made to send it out to action.

Sparrow argues that this is a “peculiar way to treat what are advertised as ‘smart’ weapons,” as it implies there is no fundamental moral difference between them and more ordinary ‘dumb’ weapons (Sparrow 70).

The issue here is that Sparrow conflates autonomy and ability with being ‘smart’. In recent years, ChatGPT and other frontier large language models have shown that they are very capable and in some ways autonomous, able to generate repositories of code, search the web, and synthesize it all into paragraphs or images. But I argue that few people would consider these LLMs as ‘smart’ and distinguish them from more typical ways to query information on the web, such as Google search. In part, this is because they fail at certain tasks humans would find trivial, such as claiming 9.11 is greater than 9.9, or miscounting the number of r’s in the word ‘strawberry’. Ultimately, as these models and killer robots are just instances of probabilistic modeling that are on a larger scale than simpler algorithms, there should be no reason to consider them as a higher class of being than ‘dumb’ weapons or ‘dumb’ search tools, as long as they do not reach moral patiency. As such, the commander should be responsible for the risk profile of these weapons, as long as the fault did not originate from the programmer.

2.5 Re-evaluating the Machine

Sparrow notes that machines cannot take on moral responsibility because they cannot suffer. Any “punishment,” whether physical, emotional, or monetary, would be meaningless to a robot without moral patiency. Sparrow uses this to widen the responsibility gap, claiming that autonomous machines should have the responsibility of human adults but cannot take the responsibility itself. I argue that this cements its status as similar to a ‘dumb’ weapon, but one that is probabilistically trained to imitate actions and values curated by humans over a wider space rather than explicitly encoding actions expected by humans in a more limited space.

2.6 Revisiting the Premises

How do we reach Sparrow’s premise of a robot committing what would be considered a war crime? Because a situation in which AI kills a civilian with intent is impossible, the machine is never the locus of moral responsibility. It is always an instrument. I argue that a war crime is dependent on intent. Given two equally catastrophic outcomes committed by humans, one with intent and one without, the first would be a war crime, and the second would be an extreme case of negligence. Both should be unacceptable, but as Sparrow acknowledged, human war has accidents that are regrettable but inevitable that do not live up to the standards of jus in bello. We do not hold a gun morally responsible for a murder, nor a target-seeking missile: we hold the people who built it or used it responsible. The “autonomy” of the LAWS is simply a more complex version of the machine learning that powers semi-autonomous weapons. It remains software. Therefore, there is no moral responsibility that should be attributed that is lost; the responsibility is just transferred into human negligence, or accidents in the system that are regrettable.

3 Adopting AI Systems with Risk

Once we dismiss the requirement for the machine to possess moral agency, the question shifts from “who do we blame” to “is this safer?” This moves the debate of whether or not an AI system should be implemented into territory explored by Simspon and Müller in “Just War and Robots’ Killings” regarding just war and engineering tolerance.

3.1 Robots and Jus in Bello

Jus in bello prohibits the intentional targeting of civilians and requires proportionality between military benefit and civilian collateral damage. Sparrow argues that without a moral agent to be held responsible, these principles are violated. But perhaps jus in bello would prefer safer systems and fewer deaths over the satisfaction of assigning blame.

The purpose of the laws of war is to minimize suffering and protect non-combatants. If we can demonstrate that a LAW is statistically less likely to kill a civilian than a human soldier—who is prone to fear, rage, fatigue, and vengeance—then the moral imperative shifts. Despite the requirements of jus in bello, violations and accidents are inevitable, with or without intent. Simpson and Müller argue that if the “risk” of a robot violating just war principles is lower than the “risk” of a human soldier doing so, then deployment is not only permissible but potentially obligatory (Simpson & Müller 316).

If a civilian dies by a LAWS, it is akin to a malfunction or “collateral damage” in the truest sense—a tragedy without a specific malice. While this is emotionally unsatisfying to the victim’s family, it does not violate the core tenets of jus in bello, which acknowledges that unintended civilian death is a terrible but inevitable reality of conflict. If the “malfunction rate” of a robot is lower than the “accident rate” and “atrocity rate” of human soldiers, then a robot would better adhere to the spirit of jus in bello on a systematic scale.

3.2 Autonomy Around Us

To understand how responsibility functions without moral intent, we must look beyond the battlefield to the civilian sphere, where autonomous systems are becoming commonplace. In “Just War and Robots’ Killings,” Thomas Simpson and Vincent Müller offer a framework of “engineering tolerance” that is essential for decoupling autonomy from moral agency. They note that in civil engineering, if a bridge collapses due to a freak 1-in-500-year storm, it is possible that no one is responsible, as long as the engineer met the standard of care. Every engineered system has a “tolerance level”—a specification of the conditions under which it must function reliably.

To extend this to autonomous systems, if a system is released with potential for error, the programmer or operator is liable not for the specific “choice” the machine made. However, if the potential for error is higher than the tolerance level of the conditions of the deployment (which should be zero and realistically is minimal), then the programmer would be liable for not programming a machine to such standards, or the commanding officer would be reliable for deploying the system in an environment exceeding its specifications.

This logic applies directly to autonomous vehicles, such as Waymo, which offer a prominent real-world parallel to LAWS. Waymo, the world’s first autonomous ride-hailing service, operates systems that replace a human driver, controlling actions such as steering, acceleration, and braking. To distinguish between the creator and the deployer, consider two entities: WaymoTech, which programs the self-driving cars, and WaymoBusiness, which operates the fleet.

Suppose a car driven by an autonomous system strikes and kills a pedestrian in a situation where a human driver would have been at fault. Under Sparrow’s framework, this scenario presents a crisis of accountability: the programmer did not code the specific impact, and the operator did not steer the wheel. However, through the lens of Simpson and Müller, the crisis dissolves. We do not need to locate a moral agent who “intended” to hit the pedestrian. Instead, we assess the system against its engineering tolerance.

It is generally accepted that if the Waymo Driver causes harm that a human driver would have been liable for, the corporation is liable. This liability is determined by the parameters of risk established prior to deployment. If WaymoTech indicated that the software struggles in heavy rain, but WaymoBusiness deployed the fleet during a storm anyway, the responsibility falls on the operator for deploying the system outside its safe tolerance level. If the programmer failed to program the vehicle to recognize pedestrians with a standard degree of reliability—falling below the agreed-upon safety threshold—the responsibility falls on the programmer for negligence.

The key insight here is that functional autonomy does not necessitate moral agency, nor does it require a moral agent to be in control of every micro-second decision. Although the company is not responsible for the specific “decision” of the car to turn (as the car makes no moral choice), they are responsible for introducing the risk of an automated system into the public sphere. An entity is held accountable for the accident because they negligently did not ensure the software was well-calibrated, or negligently ignored the operational guidelines. This requires a shift in category, viewing the problem not from a position of control—which implies directing a specific action—but from a position of risk management. If the system operates within a tolerance level that is safer than the human baseline, individual accidents become matters of liability and insurance rather than moral retribution. This sets the stage for the crucial ethical question: not “who is to blame,” but how this risk is distributed across the population.

3.3 Distributing Risk

If we accept that the ethical deployment of AI depends on risk management rather than the location of a moral agent, we must address how that risk is distributed. Simpson and Müller argue that the introduction of autonomous agents is permissible if it satisfies James Lenman’s “Precaution Thesis,” which roughly demands that we minimize the risk of harm to each affected person. In the context of war, they argue that if the risk posed by a killer robot to non-combatants is lower than the risk posed by human soldiers, then deployment is justified (Simpson & Müller 313).

However, a critical challenge lies in the transition to this state of safety. I argue that while a “final state” AI may be safer than humans, AI requires a “training state.” Just as Waymo vehicles required millions of miles of testing with human oversight⁵ to become safe in limited areas, LAWS and other systems would require operational data to lower their error rates. This raises a distributive justice concern: is it ethical to put populations at risk during this testing phase? If a system is safer for the deployer (removing their soldiers from risk) but transfers risk to civilians during its calibration phase, this creates an asymmetry that may violate the principles of jus in bello, and that doesn’t satisfy the Precaution Thesis.

This is where the distinction between capability and safety becomes paramount. Even if the technology is capable of selecting targets, it must be rigorously tested to ensure it meets a strict tolerance level regarding civilian discrimination. Waymo is a good example of this, having tested for years in limited areas, with human oversight and software development in co-existence. Even with mature software, location-specific testing requires human testing to ensure the maximum possible safety of self-driving vehicles, which are meant to be the safe and secure future for driving. Furthermore, this distribution of risk requires some form of public consent or acknowledgement. In the civilian sector, we accept the risk of autonomous vehicles because we anticipate a reduction in aggregate traffic fatalities. In warfare, the “consent” of the enemy civilian population is impossible to obtain, but the international community must establish an agreed-upon risk level—a standard of reliability that LAWS must meet before deployment.

Furthermore, we must address the “outcome-based concerns” identified by Karen Yeung, specifically bias that generates injustice (Yeung 32). A major risk of deep learning systems is that they may target specific demographics disproportionately due to biases in training data—for example, associating military combatant status with military-age males of a specific skin color, or applicant tracker systems scoring gender and racial minorities lower because they are historically underrepresented. While this is a horrific outcome, it is, again, a failure of data selection and programming, not a moral failing of the machine. The responsibility lies in ensuring the training data is representative and the “tolerance level” for bias is near zero. If we cannot ensure that risk is distributed evenly and that the system does not discriminate based on protected characteristics, the system has failed its safety checks and should not be deployed.

4 Moral vs Legal Responsibility

By shifting the focus to negligence, risk, and engineering tolerance, we solve Sparrow’s responsibility gap. We resolve the gap by analogizing AI to previous technologies and moving away from the anthropocentric expectation of a “guilty mind.” What remains is legal responsibility (liability), not moral responsibility (blameworthiness).

This leads to the strongest counter-argument against my position, one argued by Karen Yeung in “Why Worry about Decision-Making by Machine?” Yeung argues that there is a distinct human need for moral accountability. She identifies “process-based concerns,” such as the dehumanization of decision-making, suggesting that humans have a right to a “relational dimension” in critical decisions (Yeung 30). In the context of harm caused by autonomous systems, this suggests that victims (or their families) have a need to blame a moral agent—someone who could have done otherwise—rather than simply receiving compensation from a negligent corporation or government. People want someone to blame; thinking about the statistical “good of all” does not satisfy the specific grievance of the individual victim.

However, Aziz Huq counters this by questioning the value of this human intervention. In “A Right to a Human Decision,” Huq argues that human decision-makers are often flawed, biased, and inconsistent. He proposes that what we should value is not a human decision, but a well-calibrated decision (Huq 619). I align with Huq in this regard, and this preference is supported by applying John Rawls’ framework of the “Veil of Ignorance.” If we were to design the rules of warfare from the Original Position—without knowing if we would be the commander deploying the weapon or the civilian living in the conflict zone—we would rationally prioritize the system that maximizes survival. Faced with the choice between a human soldier, who offers moral accountability but a higher error rate due to panic or rage, and a well-calibrated machine, which offers no moral accountability but a significantly lower probability of unjust death, a rational agent would choose the machine. The desire for a human to blame is, ultimately, a desire for a scapegoat that only arises after the tragedy has occurred. From an ex ante perspective, the right to life supersedes the procedural right to a human decision-maker.

What we fundamentally want is for bad things to happen less frequently to everyone. If a “well-calibrated machine” can reduce the incidence of civilian casualties, prioritizing a “right to a human decision” becomes a defense of the right to be killed by a fallible human rather than saved by a precise machine. Legal responsibility—holding the state or commander liable for negligence—ensures restitution and incentivizes better safety standards. Insisting on moral responsibility in the face of superior safety statistics is a form of moral vanity that privileges the “humanity” of the killer over the life of the victim.

5 Conclusion

A lot of discourse surrounding Lethal Autonomous Weapons Systems has been centered on the “Responsibility Gap.” Robert Sparrow and others argue that because we cannot punish a machine, we cannot use it. This argument relies on the false premise that AI possesses a form of agency comparable to humans, and that justice requires a specific kind of moral retribution.

I have argued that AI lacks moral intent. It is a sophisticated tool, not a moral agent. Consequently, the harms it causes are malfunctions and failures of negligence, not war crimes requiring mens rea. By shifting the framework from moral responsibility to legal liability and risk management, we dissolve the trilemma. The programmer is responsible for the system’s safety parameters; the commander is responsible for the risk of deployment.

Once we view AI as a machine and tool just like any other technological advancement, we use Simpson and Müller’s risk framework as well as the autonomous vehicles industry to look at the requirements for adopting AI systems over humans. Waymo is an exemplary example of limiting risk and prioritizing safety while making impressive scientific advancements. As the adoption of AI systems in many industries becomes more practical due to advances in language modeling, we must consider the impact and biases these systems have. Although they can have a lower overall risk, deep learning systems are often subject to algorithmic bias, making the risk for certain groups of people higher than the human standard. I believe this is unacceptable and view the rights of the people affected by these systems as extremely important to consider, but often neglected.

Finally, I address the counterargument to a theoretical safe AI system that should be implemented, which is that moral accountability no longer exists in this system, a concern voiced in Karen Yeung’s writing. I use Aziz Huq’s “A Right to a Human Decision” to counter this, claiming that insisting on moral responsibility over superior safety statistics is a form of vanity that is not just.

It is important to note that acknowledging the possibility of ethical LAWS does not constitute an endorsement of them. There are valid reasons why technologists may refuse to build them, such as a refusal to contribute to the machinery of death. However, this is a separate ethical stance from Sparrow’s claim that they are inherently unethical due to a responsibility gap. There is almost always human error in war. With AI and LAWS, we have the theoretical potential to program systems to a tolerance level where such errors are so rare that the question of jus in bello violations becomes a marginal issue rather than a systemic failing. By focusing on risk and adopting safe, well-calibrated AI, we can move towards a future where the laws of war are upheld not by the fragile conscience of human soldiers, but by the rigid safety of code.

References

Huq, Aziz Z. “A Right to a Human Decision.” Virginia Law Review, vol. 106, 2020, pp. 611-688. JSTOR, https://www.jstor.org/stable/27074704. Simpson, Thomas W., and Vincent C. Müller. “Just War and Robots’ Killings.” The Philosophical Quarterly, vol. 66, no. 263, 2015, pp. 302-322. Oxford Academic, https://academic.oup.com/pq/article/66/263/302/2460979. Sparrow, Robert. “Killer Robots.” Journal of Applied Philosophy, vol. 24, no. 1, 2007, pp. 62-77. JSTOR, http://www.jstor.org/stable/24355087. Yeung, Karen. “Why Worry about Decision-Making by Machine?” Algorithmic Regulation, edited by Karen Yeung and Martin Lodge, Oxford University Press, 2019, pp. 21-48. Oxford Academic, https://academic.oup.com/book/35048/chapter/298945700.