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Law for Computer Scientists and Other Folk$

Mireille Hildebrandt

Print publication date: 2020

Print ISBN-13: 9780198860877

Published to Oxford Scholarship Online: July 2020

DOI: 10.1093/oso/9780198860877.001.0001

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Closure: On Ethics, Code, and Law

Closure: On Ethics, Code, and Law

(p.283) 11 Closure: On Ethics, Code, and Law
Law for Computer Scientists and Other Folk

Mireille Hildebrandt

Oxford University Press

Abstract and Keywords

This concluding chapter investigates the distinction between law, code, and ethics, as well as their interrelationship and their interaction. It is intended for those interested in the nexus of law and ethics, in the light of code- and data-driven information and communication infrastructures (ICIs). One of the main differences between law and ethics is that law provides closure whereas ethics remains in the realm of reflection as it lacks the force of law. However, a second difference turns the previous statement inside out: whereas law and the rule of law introduce checks and balances and demand democratic participation (at least in constitutional democracies), ethics may be decided by tech developers or behind the closed doors of the board room of corporate business enterprise. It can thus obtain the force of technology. Paradoxically, once ethics gains the force of technology, the space for the practice of ethics is reduced.

Keywords:   ethics, code, law, ICIs, Rule of Law, technology, democracy, force of law, force of technology

This—final—chapter investigates the distinction between law, code, and ethics, their interrelationship and their interaction. It is a bonus chapter for those interested in the nexus of law and ethics, in the light of code- and data-driven information and communication infrastructures (ICIs).

In the introduction to Chapter 10 we have encountered MIT’s ‘moral machine’ thought experiment, which aimed to ‘mine’ opinions on the ethics of choices that self-driving cars may have to make.1 I have qualified the experiment as befitting a ‘naive’ type of utilitarianism. In this chapter, I explain the assumptions that underlie the framing of the problem of ‘moral machines’ and discuss other traditional ways of framing ethical dilemmas. This is necessary because they are part of our common sense and thus often serve as the hidden premises of ‘ethics in AI’ and similar attempts to ‘do good’ when developing code- or data-driven systems. Such hidden assumptions play an important role even if one is not aware of them, and they must therefore be called out.

This would imply that it is no longer law but also technology that provides closure, though not by way of democratically legitimated legislation. Instead, closure is provided by ethics, as embodied in the black box of R&D, the board rooms of Big Tech, and communities of developers that write and maintain (p.284) open source code or DLTs. Though the latter are not a black box for those knowledgeable on the technical side, they are black boxes for those who cannot read the code.

For a proper understanding of the role of ethics, code, and law in technology development we need to move beyond analytical distinctions. As demonstrated in Chapter 2, there is a special relationship between ethics and the Rule of Law, which implies that law and ethics interact. The example I will use throughout this chapter is not about the ethical dilemmas of driverless cars, but the question of algorithmic fairness (which obviously also regards decisions made by those who build the code for driverless cars). This will confront the force of law with the force of technology, requiring a new type of interaction between lawyers and computer scientists on how to ensure that ‘ethical design’ does not overrule the checks and balances of the rule of law. In that sense, some of the notions presented in Chapter 10 will resurface when discussing the relationship between code and law.

In the context of this chapter, I use the term ethics to refer both to morality (acting in a morally justified way) and to moral philosophy (inquiring into the types of moral justification one could develop). This also means that, for the purposes of this chapter, I use ‘ethical’ and ‘moral’ as synonymous.

11.1 Distinctions between Law, Code, and Ethics

Though it may be tempting to invent an ethics for the onlife world, as if it does not matter what centuries of investigation into moral philosophy have brought us, this easily results in getting caught up in hidden assumptions. For instance, the MIT thought experiment is presented as if it has nothing to do with scholarly debates on the different schools of moral philosophy, but its framing of the problem rests on a specific variant of utilitarianism and incorporates a number of assumptions that are taken for granted without closer inspection. (p.285)

11.1.1 Utilitarianism and methodological individualism

Utilitarianism is focused on the consequences of our actions. For that reason, it is often equated with consequentialism. Utilitarianism is, however, a particular type of consequentialism, based on ‘methodological individualism’. This means that individual choices are assumed to be independent, such that collective choice is nothing other than the aggregate of individual choice. This is a highly contentious position, as individual choice is dependent on the anticipation of another’s choice and in part constituted by choice architectures that are in turn dependent on ICIs and informed by power relationships.

In the section on pragmatism (section 11.1.4), I will clarify the dependencies between means and ends as part of the framing problem that is inherent in any debate on ethics and AI. Though pragmatism also ‘thinks in terms of’ consequences, it does not assume the separation between means and ends that is assumed in utilitarianism.

For the sake of brevity, I discuss four intersecting types of utilitarianism, inevitably leaving many nuances aside: act- and -rule-utilitarianism, and maximum and average utilitarianism. All four emphasize that ethical choice must be made on the basis of the utility it generates. That is why utilitarianism feeds on cost-benefit assessments that in turn nourish a utilitarian calculus; it forms (p.286) the hidden assumption of risk assessment as a viable way to cope with the impact of new technologies. Because people may not agree on what constitutes utility, the consequences are usually discussed in terms of preferences or well-being rather than utility. That, however, raises the question of whether these preferences are given or framed, depending on the choice architecture presented by the environment. Well-being raises similar questions, because well-being is not necessarily an objective function of ethical choices (different individuals, groups, cultures, and societies may define well-being in contrasting and even incompatible ways). Therefore, I will stick to the concept of utility, taking note that it is the vanishing point of utilitarianism and in many ways a black box.

I can now explain why I believe that MIT’s ‘moral machine’ experiment rests on a ‘naive’ type of utilitarianism. Either it aims to unearth the moral preferences of website visitors as to the desirable consequences of a series of particular acts, in which case all the problematic assumptions (p.287) of act-utilitarianism apply. Or it aims to uncover the moral preferences of website visitors as to the type of rules that should inform the behaviour of autonomous vehicles, with regard to a specified act. In that case act-utilitarianism is conflated with rule-utilitarianism, because the whole idea of rule-utilitarianism is to achieve guidance at a higher level of abstraction (not case-based but rule-based).

The researchers could object that their study is just an objective data-driven investigation into the moral preferences of 40 million webvisitors, and should not be confused with an ethical inquiry. They might assert that the study does not endorse any theory of ethics and does not contain any bias towards utilitarianism. Philosopher of science Karl Popper would respond that cognition and even perception is not possible without an underlying theory that frames the issues under investigation. In this case, the methodological individualism that underpins utilitarianism clearly frames the experiment and configures the kind of choices webvisitors are presented with. These choices are then qualified as their given preferences, and treated as independent variables that can be correlated with, for example, ‘cultural traits’, ‘economic predictors’, and ‘geographical proximity’. As Michel Callon and John Law wrote, quantification (numerical data) is necessarily preceded by qualification (grouping specific instances under the same heading of a specific variable or feature). Though there is nothing wrong with such qualification, we need to become aware of the definitional choices they imply, and the framing issues they generate. Below I will give an example of assessing algorithmic fairness in a way that calls out these choices and shows some of their implications (section 11.1.5).

Here, it suffices to highlight that both types of utilitarianism would ultimately require a way to measure and maybe even weigh preferences (would, e.g. a preference to save white folk over coloured folk ‘count’ at all?). Usually, these kinds of preferences are agent-dependent, because my choice for a behavioural rule or action may depend on whether I am in the car or outside. It is entirely unclear how webvisitors developed their preferences, which makes the whole experiment a rather hazardous attempt to contribute to an informed debate on the ethics of self-driving cars. To seriously understand ethically relevant preferences, we should impose a veil of ignorance, requiring us to decide without knowing whether we will be the victim or not. However, this may bring rule-utilitarianism rather close to deontological imperatives, since the reasons that inform my agent-independent choice may differ from those that inform my agent-dependent choice, which introduces a moral criterion that is not part of the notions of either utility, act, or rule.

(p.288) Let us now turn to algorithmic fairness, inquiring how it would fare under various types of utilitarianism. The problem is that neither maximum nor average utility would solve the problem of the disparate impact of various types of bias in machine learning. In the aggregate, unfair bias may increase utility (whether maximized or on average), but some categories of individual persons may find that their preferences are ignored or diminished. Clearly, fairness is a moral criterion that cannot easily be fitted into the logic of either act- or rule-utilitarianism.

11.1.2 Deontological reasoning: respect for human autonomy

Deontological reasoning is about people doing the right thing for the right reason, without taking into account the effects. Deontological reasoning is about duties, not about consequences, and can be traced back to Kant’s categorical imperative. Kant distinguished between a hypothetical imperative, which makes a decision depend on the consequences it is expected to generate (often assessed from the perspective of one’s personal interest), and the categorical imperative, which makes a decision depend on the moral justification it involves (notably respecting the autonomy of others).

Kant formulated different versions of the categorical imperative. I am quoting them here from the renowned Stanford Encyclopedia of Philosophy, to give the reader a taste of the complexities that deontological reasoning may involve, making it seemingly less amenable to computational translation than a utilitarian calculus (though the problem of defining utility creates the same kinds of problems). Note that the emphasis on individual moral autonomy does not depend on the methodological individualism of utilitarianism, as the maxims to be discussed do not depend on an aggregate utility, but on the extent to which a maxim implies that everyone’s autonomy is respected.

1. act only in accordance with that maxim through which you can at the same time will that it become a universal law.

According to the Stanford Encyclopedia of Philosophy this implies:

First, formulate a maxim that enshrines your reason for acting as you propose.

Second, recast that maxim as a universal law of nature governing all rational agents, and so as holding that all must, by natural law, act as you yourself propose to act in these circumstances. (p.289)

Third, consider whether your maxim is even conceivable in a world governed by this law of nature. If it is, then,

fourth, ask yourself whether you would, or could, rationally will to act on your maxim in such a world.

If you could, then your action is morally permissible.

2. we should never act in such a way that we treat humanity, whether in ourselves or in others, as a means only but always as an end in itself.

According to the Stanford Encyclopedia of Philosophy this implies:

First, the Humanity Formula does not rule out using people as means to our ends.

Second, it is not human beings per se but the ‘humanity’ in human beings that we must treat as an end in itself.

Third, the idea of an end has three senses for Kant, two positive senses and a negative sense.

Finally, Kant’s Humanity Formula requires ‘respect’ for the humanity in persons.

3. the Idea of the will of every rational being as a will that legislates universal law.

According to the Stanford Encyclopedia of Philosophy this implies:

in this case we focus on our status as universal law givers rather than universal law followers.

This is of course the source of the very dignity of humanity Kant speaks of in the second formulation.

A rational will that is merely bound by universal laws could act accordingly from natural and non-moral motives, such as self-interest.

But in order to be a legislator of universal laws, such contingent motives, motives that rational agents such as ourselves may or may not have, must be set aside.

4. act in accordance with the maxims of a member giving universal laws for a merely possible kingdom of ends.

According to the Stanford Encyclopedia of Philosophy this implies:

it requires that we conform our actions to the laws of an ideal moral legislature,

that this legislature lays down universal laws, binding all rational wills including our own, and (p.290)

that those laws are of ‘a merely possible kingdom’ each of whose members equally possesses this status as legislator of universal laws, and hence must be treated always as an end in itself. The intuitive idea behind this formulation is that our fundamental moral obligation is to act only on principles which could earn acceptance by a community of fully rational agents each of whom have an equal share in legislating these principles for their community.

Clearly, the assumption of a rational universal consensus is problematic, not because people have different interests (the veil of ignorance solves that problem), but because people have different ideas about the value of such interests and about their ranking (e.g. preferring community over liberty, or equality over community). We shall return to this when discussing pragmatism.

How would algorithmic fairness fit with the framework of deontological reasoning? One way to approach this would be to ask whether bias in algorithmic decision-making systems violates the autonomy of some human agents, while respecting the autonomy of others. The inequality goes to the heart of the matter, since the categorical imperative does not allow more or less respect for a person’s autonomy; either it is respected, or it is not respected. From the perspective of Kant, autonomy is not respected if there is no universal rule that justifies disparate treatment. To assess whether this is the case we need to ask whether different treatment would be consented to if one had no idea whether one would benefit or lose out due the algorithm.


Rawls basically combines two types of justice as fairness in his maximin principle: distributive and corrective justice. We will return to this when discussing justice (section 11.2.1).

There may be a preliminary matter that is even more to the point here: can the automated application of an algorithm ever be respectful of the autonomy of those subject to its decisions? Could it be that algorithms necessarily use people only as a means and cannot ever respect their autonomy, due to the nature of machinic decision-making? This is a pivotal question and I believe that the answer depends on a number of factors that relate to the extent to which human oversight and human intervention are ruled out. I would not categorically reject algorithmic decision-making, because one can argue that abstaining from its usage could result in invisible unfair treatment by human beings (whether deliberate or unintended). One could argue—in that case—that abstaining from algorithmic decision-making shows disrespect for the autonomy of those subject to the decision.

11.1.3 Virtue ethics: perceiving the good and doing what is right

Rule-utilitarianism and deontological reasoning based on the categorial imperative seek ethical guidance in abstract rules that should be applicable independent of the personal characteristics or inclinations of the acting agent. Virtue ethics is less impressed with abstract justification, as it is focused on the moral character developed by the actor. This is not a matter of agent-dependent reasoning based on the self-interest of the agent, but a matter of (p.292) highlighting the need for individual agents to practice and develop their moral compass. The idea is that human agents are not born with such a compass, but need to gain experience in real-life situations, building what Aristotle called phronesis or practical wisdom. In the context of virtue ethics, the point is not to submit oneself to abstract rules but to elicit the right rule for the situation at hand. This is a matter of acuity and judgment rather than the application of existing rules or a calculation of utility.

As Varela wrote in his work on Ethical Wisdom:

As a first approximation, let me say that a wise (or virtuous) person is one who knows what is good and spontaneously does it. It is this immediacy of perception and action which we want to examine critically. This approach stands in stark contrast to the usual way of investigating ethical behavior, which begins by analyzing the intentional content of an act and ends by evaluating the rationality of particular moral judgments.

Whereas episteme, according to Aristotle, is a matter of reasoning and theoretical insight, phronesis is a matter of experience, action, and perception. Young men (Aristotle was not interested in women) are great in achieving epistemic knowledge, whereas phronesis can only be achieved in the course of a lifetime. Perhaps virtue ethics is the most interesting type of ethics in an onlife world, where non-human agents challenge our understanding of moral agency. It seems clear that machines may develop something akin to epistemic knowledge. They will, however, by definition be excluded from developing virtues or practical wisdom. This is related to the difference between knowledge and wisdom, and between rationality and moral character. Wisdom and moral character require a type of acuity that implies both ambiguity and good intentions, together with skilled intuition, a kind of tacit knowledge that incorporates virtues such as prudence, temperance, courage, and justice. It is hard to imagine that a deep learning algorithm develops any of these characteristics in its relationship with other agents, even if it beats grand masters in chess, Go, and whichever other closed game with well-defined rules.

(p.293) How would algorithmic fairness fare with virtue ethics? Could one define the virtue of justice such that it can be formalized and computed? Might Aristoteles’ distinction between distributive and corrective justice (section 2.2.2) lend itself to research designs that detect unfair bias, while repairing whatever bug led to the violation of justice?

11.1.4 Pragmatist ethics: taking into account

The founding father of pragmatism, Charles Saunders Peirce, developed the so-called ‘pragmatist maxim’:

Consider what effects, which might conceivably have practical bearings, we conceive the object of our conception to have. Then, our conception of those effects is the whole of our conception of the object.

This clearly has implications for ethics, as it highlights that the way we try to achieve our objectives shapes them, also in the realm of ethics. In the context of utilitarianism, technologies are often seen as neutral tools, ignoring the way they enable and constrain both intended and unintended effects. In the context of deontological ethics all that seems to matter is one’s moral duties to other agents, based on an abstract rational consensus that fails to take into (p.294) account the situatedness of human agency. This results in moral duties that abstract from the mundane means of executing them, thus missing out on their impact on human autonomy. Other than Kant, an ethical pragmatist would not assume or postulate an autonomous human subject, but seek to uncover the real-life conditions for autonomous agency.

Virtue ethics seems highly relevant in the realm of value-sensitive design, as the success of ‘ethical design’ will depend on the skills needed to make value-sensitive design work. But it is pragmatism that has the clearest understanding of the normative implications of designing a technology one way or another, precisely because it is already aware of how the means shape the goals. A pragmatist ethics shares awareness of the situatedness of the human agent with virtue ethics, and a sensitivity to the importance of experience, since pragmatism highlights the need to anticipate consequences (albeit not in the utilitarian sense). As with virtue ethics and utilitarianism, a pragmatist ethics is less impressed with the universal moral duties of deontological reasoning, and it endorses a more situated understanding of human autonomy.

We can point to the work of Helen Nissenbaum, notably to her ‘contextual integrity’ (CI) heuristic, that traces the implications of novel types of technologies, providing a step-by-step assessment of how the environment is changed and how this may affect the legitimate, context-based expectations of human agents. One of the consequences of introducing novel technologies may be a redistribution of risks and benefits within and across contexts, which may reinforce existing inequalities or even create new types of inequality. Her analysis fits with the core assumptions of a pragmatist ethics, it moves beyond privacy and provides a coherent framework to assess fairness as an ethical value that may be disrupted.

Note that contextual integrity does not equate fairness with equality. As we have seen above, when discussing Rawls’ maximin principle, treating different people equally may actually be unfair. Think of Anatole France’s famous finding that: ‘In its majestic equality, the law forbids rich and poor alike to sleep under bridges, beg in the streets and steal loaves of bread.’ The balance (p.295) that must be struck between corrective and distributive equality requires choices that assume a moral and a political evaluation of what counts as fair under what conditions. There may be clear indications of unfair treatment, but it is not easy to come to an agreement on what constitutes fair treatment.

11.1.5 The difference that makes a difference: closure

Before drawing conclusions regarding the major differences between law, code, and ethics, I will present the reader with an excerpt of a blogpost on Medium by the Berkman Klein Centre at Harvard University, on the so-called ‘Detain/Release’ teaching module. This module simulates pre-trial court decisions on whether to detain or release a defendant based on the available assessment of recidivism risk:

We wanted students to put themselves in the role of a judge, and think about how they would make pretrial detention decisions. We began with a tutorial run that students completed on their own: ten defendants, no risk assessments.

After that, we divided students into groups and had them do three full runs of the simulation. We wanted students to talk about how they made their decisions, during and after the simulation runs. By the third run, we found that students are invested in the simulation and in the detention and release decisions they’ve made.

Throughout, we were deliberately opaque about how the simulation worked—about how accurate the risk assessments actually were, and about what probabilities ‘low’, ‘medium’, and ‘high’ corresponded to. For the most part, no one asked, either in our classroom or during our tests of the simulation.

Despite that, as they progressed through the lesson, students began to feel more confident and assured in their detention and release decisions. They built interpretive systems to quickly make decisions from the information they had been given. Some of their rules and systems were expected: high violence usually meant detention. Others, less so: after seeing two female defendants fail to appear, one team began detaining women by default.

After the third and final run, we showed students the consequences of their decisions, with one last dashboard view: How did pretrial detention decisions affect defendant outcomes?

Closure: On Ethics, Code, and Law


The final dashboard view: consequences.

This reveal takes the air out of the room. It drives home the framing power of the risk assessment tool we had presented them: students relied on it, deeply, despite receiving no promises about its accuracy, and ‘corrected’ for it in random ways. This had consequences.

The aim here is not to take sides on who are right or wrong with regard to the use of pretrial software to conduct a risk assessment, or on whether human judges do better than the software. The point here is to demonstrate that MIT’s thought experiment will only contribute to a sustained reflection on e.g. algorithmic fairness if the framing problem is faced and addressed. The Berkman Klein module on the ‘Detain/Release’ simulation nicely shows how software systems can lure decision-makers into accepting assumptions and implications that should be called out before being put into action.


One could conclude that, whereas ethics is not a competitor of law, algorithmic decision-making systems are just that.

11.2 The Conceptual Relationship between Law, Code, and Ethics

Ethics is both more and less than law: it is more because many ethical concerns are not addressed by the law and less because the outcome of ethical considerations are not necessarily transformed into legal norms and thus not enforceable by way of law. As indicated above, since we often do not agree on ethical rules, values, or choices, the law mainly integrates ethical principles and considerations at a meta-level—for example, to make sure that ethical choice is not systematically overruled by economic interest. The idea is that law and especially the rule of law creates space to develop one’s practical wisdom and to act in accordance with the kind of rules one believes everyone should follow (seen from behind a veil of ignorance).

(p.298) I will now first return to section 2.2.2 to clarify once again the relationship between law and ethics at the level of law’s foundational architecture. After that I will flesh out how this foundational architecture relates to the employment of computer code when making legally relevant decisions.

11.2.1 Justice, legal certainty, and instrumentality

The goals of ethics can be summed up as ‘acting in the right way’, which assumes having taken the right decisions, taking note that these decisions may be implicit in our actions since much of our ethical knowledge is tacit and hard to spell out. The study of ethics hopes to explain how our actions can be justified, by, for example, referring to values such as liberty, equality, and autonomy. Though part of moral philosophy assumes that a universal rational consensus about what constitutes a right action is possible, the problem with ethics is precisely that there is no such consensus (neither is there a consensus that we should try to reason towards such a universal rational consensus). In point of fact, constitutional democracies take the position that it would be unethical to impose the ethics of a majority on minorities, let alone that the ethics of a minority should reign over a majority. But, as some would remind us, this position itself is precisely the kind of universal rule we need in a meta-ethical framework.

Law cannot disentangle itself completely from ethics. On the contrary, law and the rule of law embrace a pragmatic meta-ethics that integrates a system of institutional checks and balances that safeguard the freedom to live according to one’s own ethics—though within the limits needed to guarantee equivalent safeguards for others. This means that law is concerned with a specific type of justice, closely aligned but not equivalent with legal certainty. As discussed in section 2.2.2, law has to serve three different, partly overlapping and often incompatible goals: those of justice, legal certainty, and instrumentality.

  1. 1. treats similar cases equally to the extent of their similarity; and

  2. 2. provides for just desert in proportion to whatever elicits the desert (e.g. committing a tort or a criminal offence or creating added value for society).


Above, in section 11.1.2, we discussed Rawls’ maximin principle as a way to combine both types of justice, under the heading of ‘justice as fairness’. Even in that case, we need to take a series of decisions about how this balance can or should be struck, leaving room for choice, interpretation, and contestation.

In the end, political decisions must be made, for example, about what constitutes a fair market, enacting the relevant legislation, followed by legal decisions that apply what the legislature enacted. From that moment onwards, the law will take over and make sure that law’s instrumentality in terms of policy goals set by the legislature is achieved in alignment with legal certainty (foreseeability) and justice (distributive and proportional equality). Here again, courts will have to take decisions on what counts as equal and what is deserved. Sometimes, a decision may be fair but unforeseeable, foreseeable but unfair, or it may resist instrumentality to safeguard foreseeability or violate fairness to assure instrumentality.

There is no way to resolve—at an abstract level—the tension between the three goals of the law: justice, legal certainty, and instrumentality. What matters is that any and all legal decision(s) must be justifiable as striving to serve all three goals, thus sustaining rather than resolving the tension between them. This ‘demand’ can be termed a meta-ethics that basically enables people to develop their own moral competences. For instance, if ethical values such as privacy and fairness are left to ‘the market’, companies that build their systems in accordance with these values may be pushed out of the market (because they have to make costs that other companies externalize). If, however, the law puts a threshold in the market by requiring and enforcing companies to integrate these values into their systems, companies can ‘afford’ to act ethically.

11.2.2 Law, code, and the rule of law


Though justice is an ethical value, its role in law is limited by the instrumentality of the law (an orientation towards goals defined by the legislature, or, in the case of contract, by contracting parties) and by the demands of legal certainty (the ‘positivity’ of the law, meant to ensure both the enforceability of the law and the integrity of the law as a whole). This confirms that law is both more and less than ethics.

This raises the question of how law and the rule of law relate to code, an issue already addressed in Chapter 10, notably section 10.3 where we distinguished ‘legal by design’ from ‘legal protection by design’. Here we look more broadly at algorithmic decision-making systems, whether in the private or the public sector, without focusing on systems that supposedly execute legal norms.

Technological enforcement reduces the space for ethical choice, because ethical choice assumes the freedom to act otherwise and room to develop alternative ethical positions. The space for ethical choice can be occupied either by legal obligations or by computer code. Insofar as legal norms impose particular ethical choices, the relevant conduct is turned into legal compliance. The same can be said about computer code that forces ethical choices upon people or companies, since—in that case—the choices are no longer made by those people or those companies.

(p.301) The difference between law and computer code, however, is that a legal norm can in principle be disobeyed, whereas code that manages to constrain the behavioural options of people or companies may not leave any room for disobedience. This is a significant difference between law and technology, meaning that law leaves room for ethical choices even where it imposes its norms (think of civil disobedience), whereas computer code may leave no such room. Think of an algorithm that automatically allows advertisers to target white men for higher paid jobs, thus excluding women and people of colour from being informed about these jobs. The ethical choice that is at stake here is the choice of, for example, a website owner to disallow this type of unfair targeting. Since the algorithm is trained to increase ad revenue it may be difficult if not impossible to root out this type of algorithmic output, to the extent that the algorithm ‘finds’ that such exclusionary targeting increases ad revenue.

The reduction of the space for ethical choice will necessarily result in a loss of space to practice one’s moral compass. As Roger Brownsword has argued, this also goes for the law. If we develop algorithms that are ‘legal by design’ or ‘ethical by design’, we diminish the space of law or ethics in favour of ‘technological management’. This may ultimately impact our understanding of ethics and law, notably where some may argue that the technological management of our choice architectures is a better way to achieve a ‘good’ society than either law or ethics.

11.3 The Interaction between Law, Code, and Ethics

By exploring the distinctions between law, code, and ethics, and their relationship, we have prepared the ground for a study of their interaction. At a (p.302) conceptual level, I will do this by discussing ‘by design’ approaches to law and ethics, and, at a more concrete level, I will do this by determining how law and ethics interact with code in the context of algorithmic fairness.

11.3.1 ‘By design’ approaches in law and ethics

In section 2.1 I wrote that ‘[l]egal certainty, one of the core values of the law, is not about fixating the meaning of legal norms once and for all. Instead, legal certainty targets the delicate balance between stable expectations and the ability to reconfigure or contest them’.

Recall the pragmatist definition of meaning (section 11.1.4):

Consider what effects, which might conceivably have practical bearings, we conceive the object of our conception to have. Then, our conception of those effects is the whole of our conception of the object.

This definition is particularly apt for understanding what language ‘does’, because it highlights the anticipatory nature of language usage and the meaning it generates. In section 2.1.2 I briefly discussed speech act theory when explaining the performative character of the law; if specific legal conditions are fulfilled, law attributes specified legal effects. For instance, the meaning of ‘murder’ is defined by a combination of legal conditions that generate the legal effect of some action ‘counting’ as murder. This means that whoever performed this action becomes punishable. (p.303)

Code does not produce meaning but ‘mere’ effects, at the level of its integrated circuits, its logical operations, and decisional throughput and output (including effects in the real world as, e.g. in an internet of things (iot), or when using fintech, search engines, or social networks). Many of these effects may not only be unforeseen but also unintended, especially where the output pours out into the real world. This is where ‘by design’ approaches in law and ethics become interesting, in part because these limitations may also apply to ‘by design’ approaches that rely on adapting code as a solution.

Privacy by design has long been an example of a ‘by design’ approach in ethics, because there was no legal obligation to integrate privacy at the level of design. Data protection by design (DPbD) is an example of a ‘by design’ approach in law, at least within the jurisdiction of the General Data Protection Regulation (GDPR), because since 2018 this is a legal obligation (section


However, this right is limited to discrimination based on a specific type of grounds (Article 21 CFREU speaks of any ground such as ‘sex, race, colour, ethnic or social origin, genetic features, language, religion or belief, political or any other opinion, membership of a national minority, property, birth, disability, age or sexual orientation’), and may be justifiable if specific conditions apply (e.g. reserving the payment of a pension to people beyond a certain age, reserving pregnancy leave to women, and reserving positive discrimination to a disadvantaged minority).

To the extent that algorithmic decision-making systems result in violations of fairness that is not unlawful in terms of DPbD, the obligation does not apply. In that case, a design approach could be based on ethical considerations. In the next subsection I will discuss fair computing as an example of ‘fairness by design’ that may in part be warranted under the legal obligation of DPbD and in part be based on a ‘by design’ approach to ethical issues around fairness in computing.

11.3.2 Fairness by design and ‘fair computing’ paradigms

Before heading into ‘fairness by design’ I need to address two preliminary issues.


Having drawn attention to these preliminary issues, I believe that it is nevertheless pivotal to invest in researching and exploring ‘fairness by design’. Section 10.1.2 has provided an analysis of discrimination in parole decisions that are based on proprietary software, demonstrating that different people and organizations frame the issue of fairness differently, ending up in a deadlock between those who claim statistical objectivity and those who argue that individual persons are in point of fact wrongly discriminated against, due to aggregate profiles that do not apply to them (the fact that 87 per cent of black people recidivize does not mean that every black person has a chance of 87 per cent to recidivize). Here we see the crucial difference between (1) ethical notions of unfairness that are by definition contestable; (2) legal notions of unfairness that are reasonably circumscribed but remain contestable on legal (p.306) grounds; and (3) computational notions of unfairness that are necessarily disambiguated to cater to the need to formalize.

Note that I have shifted from addressing fairness to addressing unfairness, because in a design context it may be a bit pretentious to claim that one can design ‘fairness’, whereas a sustained and systematic effort to design against unfairness will also keep us alert to new types of unfairness. Binary logic fails us here; the fact that something is not unfair (in some particular sense of the term) does not imply that it is fair (in all senses of the term). Fairness is what Gallie would term an essentially contested concept that requires vigilance and acuity rather than closure.

The point of this exercise is to develop mutual respect for the difference between ethical, legal, and computational notions of fairness and unfairness. To demonstrate what I mean with such mutual respect, I will sketch three approaches to the use of the COMPAS software: an ethical ‘by design’ approach, a legal ‘by design’ approach, and a computational ‘by design’ approach. Before doing so, I explain the background of the decisions supported by COMPAS. The case of COMPAS

When deciding about whether to detain or release a criminal defendant or a criminal offender, courts in the United States assess the likelihood of recidivism. This may concern pre-trial decisions (probation), trial decisions (sentencing), and post-trial decisions on early release (parole). These decisions are to some extent discretionary, meaning the court is not bound by strict legal conditions (this may differ per state, and for sentencing stricter rules may apply). A high likelihood of recidivism is one of the factors weighing in on a decision to detain or release the defendant (who is awaiting trial), or of the offender (who was convicted and awaits sentencing or has been detained but is eligible for early release).

(p.307) The idea is that detention prevents additional offences, so the goal of this particular assessment is to protect potential victims (this is often identified as protecting ‘the public’ or ‘the community’). In the case of a defendant the goal cannot be punishment, because being a defendant means there is no conviction yet. In the case of an offender, the goal of detention is punishment, early release can, for example, be justified as a reward for good behaviour, a way to reduce pressure on prisons, or a way to contribute to reintegration into society. These decisions, however, are not only based on the assessment of potential recidivism, they should also take into account what would be best for the defendant or offender.

On the website of the US Justice department,2 the status and the goals of parole are clarified as follows:

When someone is paroled, they serve part of their sentence under the supervision of their community. The law says that the U.S. Parole Commission may grant parole if (a) the inmate has substantially observed the rules of the institution; (b) release would not depreciate the seriousness of the offense or promote disrespect for the law; and (c) release would not jeopardize the public welfare.

Parole has a three-fold purpose: (1) through the assistance of the United States Probation Officer, a parolee may obtain help with problems concerning employment, residence, finances, or other personal problems which often trouble a person trying to adjust to life upon release from prison; (2) parole protects society because it helps former prisoners get established in the community and thus prevents many situations in which they might commit a new offense; and (3) parole prevents needless imprisonment of those who are not likely to commit further crime and who meet the criteria for parole. While in the community, supervision will be oriented toward reintegrating the offender as a productive member of society.

(p.308) The assessment of the likelihood of recidivism is done by whoever is competent to decide on detention or release. Those competent (often courts, e.g. supported by parole boards, probation officers etc.) can use their common sense and their trained intuition as well as empirical reporting by experienced or expert advisers to reach a conclusion. In line with calls for ‘evidence based’ sentencing decisions, various types of data-driven software tools have been developed that are usually claimed to assess the relevant risk more accurately or more expediently. Some of this software has been developed by federal or state courts, but some courts rely on proprietary software from commercial vendors. One such vendor, with a substantial ‘market share’ was Northpointe (now Equivant), who developed the COMPAS system, which stands for correctional offender management profiling for alternative sanctions. The COMPAS risk score is based on six features, after its learner algorithm was trained on available data sets with a feature space of 137 features. The learner algorithm has found these six features highly indicative of recidivism. The risk score is based on an interview and/or a questionnaire that is filled in by the defendant or offender, and on their criminal file.

Because of the major impact of the use of proprietary software on detention decisions, Julia Angwin (an investigative journalist working with Propublica), decided to test the accuracy of the predictions and came to the following conclusions (based on her own scientific data-driven research):

In forecasting who would re-offend, the algorithm correctly predicted recidivism for black and white defendants at roughly the same rate (59 percent for white defendants, and 63 percent for black defendants) but made mistakes in very different ways. It misclassifies the white and black defendants differently when examined over a two-year follow-up period.

Our analysis found that:

Black defendants were often predicted to be at a higher risk of recidivism than they actually were. Our analysis found that black defendants who did not recidivate over a two-year period were nearly twice as likely to be misclassified as higher risk compared to their white counterparts (45 percent vs. 23 percent).

White defendants were often predicted to be less risky than they were. Our analysis found that white defendants who re-offended within the next two years were mistakenly labeled low risk almost twice as often as black re-offenders (48 percent vs. 28 percent). (p.309)

The analysis also showed that even when controlling for prior crimes, future recidivism, age, and gender, black defendants were 45 percent more likely to be assigned higher risk scores than white defendants.

Black defendants were also twice as likely as white defendants to be misclassified as being a higher risk of violent recidivism. And white violent recidivists were 63 percent more likely to have been misclassified as a low risk of violent recidivism, compared with black violent recidivists.

The violent recidivism analysis also showed that even when controlling for prior crimes, future recidivism, age, and gender, black defendants were 77 percent more likely to be assigned higher risk scores than white defendants.

This gave rise to a turbulent debate, where Northpointe accused Angwin of methodological incompetence, stating that their own predictions were the result of objective application of statistics. This in turn generated a series of scientific articles on both sides of the debate, resulting in a number of initiatives on the side of law, social science, and computer science to counter what has been termed ‘bias in machine learning’, finally prompting a new ACM conference dedicated to ‘fair accountable and transparent’ computing.

At some point, an offender was sentenced to six years of imprisonment, after the judge had taken note of the high risk score attributed by COMPAS.3 The offender, Eric Loomis, appealed the decision on the grounds that his sentence was based on proprietary software that should not have informed the decision because it was not possible to assess its accuracy, thereby violating his due process rights and/or because it may have wrongly taking gender into account. The appeals court rejected his appeal.

Note that the COMPAS recidivism risk score is part of the so-called ‘Presentence Investigation Report (PSR)’, that was used to determine the sentence. The PSR explicitly stated:

For purposes of Evidence Based Sentencing, actuarial assessment tools are especially relevant to: 1. Identify offenders who should be targeted for interventions. 2. Identify dynamic risk factors to target with conditions of supervision. 3. It is very important to remember that risk scores are not intended to determine the severity of the sentence or whether an offender is incarcerated (emphasis added by the court).

(p.310) The court of appeal, however stated:

In addition, the COMPAS report that was completed in this case does show the high risk and the high needs of the defendant. There’s a high risk of violence, high risk of recidivism, high pre-trial risk; and so all of these are factors in determining the appropriate sentence.

( … )

You’re identified, through the COMPAS assessment, as an individual who is at high risk to the community.

In terms of weighing the various factors, I’m ruling out probation because of the seriousness of the crime and because your history, your history on supervision, and the risk assessment tools that have been utilized, suggest that you’re extremely high risk to re-offend. A computational ‘fairness by design’ approach to detain/release court decisions

Julia Angwin’s main point is that, though the accuracy for black and white defendants is the same, the error in the case of black defendants concerns false positives (they are given a higher risk-score compared to their actual recidivism), whereas in the case of white defendants the error concerns false negatives (they are given a lower risk-score compared to their actual recidivism). Northpointe/Equivant has argued that this is inevitable because black people (as an aggregate) recidivize more often. Proper use of (p.311) statistics—according to Northpointe/Equivant—results in an undesirable but unavoidable disparate outcome.

There may be a ‘cost’ insofar as this may result in more false negatives for black people who do recidivize, but a ‘cost’ will actually be inevitable, it is inherent in the employment of statistics. The question of which cost we accept is not a matter of accuracy or objectivity, but of either ethics or law (and, obviously, the political choices made when writing the law).

This relates to the issue of fairness. Having concluded that statistics in itself does not dictate the machine learning research design choices made by Northpointe/Equivant, we suddenly find ourselves in the realm of fairness. Some may find it fair that a black person who will not recidivize has a higher chance of being detained due to a false positive than a white person, whereas they would find it unfair—or maybe dangerous—to make design choices that would result in a higher chance of false negatives for black people who will recidivize.

From a computer science perspective, both can be formalized and made operational. Due to the fact that as a society, we may not agree on the choice to be made here, it is difficult to demand ‘closure’ from computer scientists.

(p.312) At this moment, computer scientists have come up with dozens of different ways to formalize fairness. This demonstrates that the employment of this type of software may seem expedient and effective, whereas in point of fact it may create more problems than it solves.

This conclusion may also be drawn from the Supreme Court of Wisconsin, where it finds that the appeal court would have made the same decision if COMPAS had not been used. Interestingly, computer science research by Farid and Dressel led them to the conclusion that the COMPAS algorithm does not outperform a randomly chosen set of human assessors who based their assessment on seven features. In other words, investing in this type of software may have no added value. Northpointe/Equivant, however, was seen to be rather proud that they did about as well as human assessors, arguing that their accuracy would improve (with more data). The Supreme Court of Wisconsin seems to assume the same, as it urged courts to adopt more evidence-based decision-support tools, though cautioning about the current state of the art. An ethical ‘fairness by design’ approach to detain/release court decisions

When reading the research presented by Julia Angwin, Northpointe/Equivant, a number of other authors, and the Loomis case, one cannot but conclude that merely ‘fixing’ the COMPAS algorithms will not suffice.

The case of COMPAS thus nicely demonstrates the complexity of the decisions that must be made by the court and of the interaction between different factors that play out on the side of the defendant or offender. In the (p.313) case of Loomis, the defendant had agreed to a plea bargain, which means that—even though he did not confess—he was willing to accept punishment. This is a common practice in the United States that offers the justice system some relief from procedural requirements, traded against a lowering of the sentence or fine for the defendant. The deal is struck between the public prosecutor and the defendant, meaning that the court is not bound by it, though most often taking it into account (some call this ‘trading with justice’). It may be that much of the unfairness starts here, and even much earlier, where black Americans have a much higher chance of being disadvantaged in numerous ways and of being treated in ways that do not reflect the idea that a government should treat each and every citizens with ‘equal concern and respect’.

Let’s remind ourselves that we are making decisions like this, based on various types of generalization, every day. There is no way we can escape from the dilemma these decisions pose.

Here, I believe, the contribution of ethics can be pivotal. This will only work if we steer free from uninformed utilitarian cost-benefit analyses that weigh, for example, public goods such as privacy as if they are merely private interests, against private interests of the state under the heading of public security, often remaining stuck in simplistic act-utilitarianism. Similarly, we should not fall into the trap of romanticizing the singularity of individual defendants, claiming they should never be compared to others. As I have tried to elucidate in section 11.1, ethics is deeply concerned with the need to articulate rules that are not informed by parochial interests, both in rule-utilitarianism and in deontological reasoning. A naive interpretation of the rule that maximizes utility (aggregate or average) would possibly align with the position taken by Northpointe/Equivant, insofar as the cost of false positives of black defendants that would not recidivize were to be less than the cost of false negatives of black defendants that do recidivize. This position is naive (p.314) because the distribution of the cost is not taken into account (whose costs are weighted against whose benefits?), and also because this approach reinforces existing bias and may incur enormous cost down the line where black communities are confronted with a downward spiral of disrespect. Instead, we could investigate whether Rawls’ maximin principle could be applied here, suggesting that fair algorithms should at least prevent loss of utility for the least advantaged, or develop a threshold in the learning algorithm that rules out picking on those already suffering systemic disadvantage.

But, maybe, the role of ethics is not only to achieve something like ‘counter optimization’. Perhaps, virtue ethics and pragmatist ethics can highlight the need for human judgment, showing that in the end this may be less complicated and less dependent on invisible computation, while it can be called out in a more transparent way. Though the Wisconsin Supreme Court judged that due process was not violated, the mere fact that the problem can be articulated in terms of due process may help to frame the issue.

The court seems to give the COMPAS software the benefit of the doubt, hoping it will soon be better and admonishing courts in general to rely more rather than less on what it calls evidence-based sentencing. As one of the judges writes in her concurring opinion, however, the court allows the usage of these kinds of tools notwithstanding the observation that no agreement exists as to the reliability of COMPAS, neither in the scientific literature nor in the popular press. At some point, the tables may be turned, if current case law is overruled. Providing arguments based on ethical inquiry that takes into account the tension between individual retribution and equal treatment should help both legislatures and courts to refine their enactments and judgments, paying keen attention to the redistribution of disadvantages that may unintentionally occur due to disparate treatment. A legal ‘fairness by design’ approach to detain/release court decisions

As indicated above (section 11.3.1), I believe that the legal obligation to incorporate DPbD in the light of risks to the rights and freedoms of natural persons is not restricted to privacy by design (and not even restricted to data subjects). On the contrary, the articulation in the GDPR emphasizes the need to foresee implications for other fundamental rights, as required by the DPIA.

(p.315) A court decision to detain or release a defendant or offender is most often discretionary; it is based on a broader margin of appreciation than other decisions, notably the conviction itself (due to the presumption of innocence, a court may not convict a person if there is reasonable doubt whether the defendant committed the offence). Under the rule of law, however, discretion is not equivalent with arbitrary decisionism. A court will have to consider a number of factors before coming down with a decision, and this consideration cannot be outsourced to a machine. The reason is that such outsourcing might on the one hand enable the scaling and the streamlining of decisions, but on the other hand it may deskill the judge to the extent that they are no longer required to actually consider these factors themselves, face to face with the defendant or offender. This may diminish the practical wisdom of the court, which increases the chance that courts will uncritically rely on the calculations of software they cannot assess.

11.4 Closure: The Force of Technology and the Force of Law

In this chapter, I have argued that if ethics aligns with the force of technology, the rule of law confronts a dangerous competitor in our normative space. The fact that ethics lacks the checks and balances of the rule of law signifies that we should not become overdetermined by ‘ethical technologies’ (whatever that could mean).

However, we can also imagine the use of technological affordances to limit the unfairness of algorithmic decision-making, thus underpinning the equal concern and respect that a government owes each and every one of its citizens. This will only work if algorithmic decision-support systems challenge the acuity of human judgment instead of replacing it.

(p.316) References

On utilitarianism, deontological moral philosophy, virtue ethics, and pragmatism

Alexander, Larry and Michael Moore. ‘Deontological Ethics’. The Stanford Encyclopedia of Philosophy (Winter 2016 Edition), Edward N. Zalta (ed.). https://plato.stanford.edu/archives/win2016/entries/ethics-deontological/.

Hooker, Brad. ‘Rule Consequentialism’. The Stanford Encyclopedia of Philosophy (Winter 2016 Edition), Edward N. Zalta (ed.). https://plato.stanford.edu/archives/win2016/entries/consequentialism-rule/.

Hursthouse, Rosalind and Glen Pettigrove. ‘Virtue Ethics’. The Stanford Encyclopedia of Philosophy (Winter 2018 Edition), Edward N. Zalta (ed.). https://plato.stanford.edu/archives/win2018/entries/ethics-virtue/.

Johnson, Robert and Adam Cureton. ‘Kant’s Moral Philosophy’. The Stanford Encyclopedia of Philosophy (Spring 2019 Edition), Edward N. Zalta (ed.). https://plato.stanford.edu/archives/spr2019/entries/kant-moral/.

Legg, Catherine and Christopher Hookway. ‘Pragmatism’. The Stanford Encyclopedia of Philosophy (Spring 2019 Edition), Edward N. Zalta (ed.). https://plato.stanford.edu/archives/spr2019/entries/pragmatism/.

Sinnott-Armstrong, Walter. ‘Consequentialism’. The Stanford Encyclopedia of Philosophy (Winter 2015 Edition), Edward N. Zalta (ed.). https://plato.stanford.edu/archives/win2015/entries/consequentialism/.

Varela, Francisco J. 1992. Ethical Know-How. Stanford: Stanford University Press.

On ethics in design

Awad, Edmond, Sohan Dsouza, Richard Kim, Jonathan Schulz, Joseph Henrich, Azim Shariff, Jean-François Bonnefon, and Iyad Rahwan. 2018. ‘The Moral Machine Experiment’. Nature 563 (7729): 59. https://doi.org/10.1038/s41586-018-0637-6.

Dignum, Virginia. 2018. ‘Ethics in Artificial Intelligence: Introduction to the Special Issue’. Ethics and Information Technology 20 (1): 1–3. https://doi.org/10.1007/s10676-018-9450-z.

Hoven, Jeroen van den, Pieter E. Vermaas, and Ibo van de Poel, eds. 2015. Handbook of Ethics, Values, and Technological Design: Sources, Theory, Values and Application Domains. 2015 ed. Dordrecht: Springer.

Nissenbaum, Helen Fay. 2010. Privacy in Context: Technology, Policy, and the Integrity of Social Life. Stanford: Stanford Law Books.

Porcaro, Keith. 2019. ‘Detain/Release: Simulating Algorithmic Risk Assessments at Pretrial’. Medium (blog). 8 January 2019. https://medium.com/berkman-klein-center/detain-release-simulating-algorithmic-risk-assessments-at-pretrial-375270657819.

(p.317) Powles, Julia. 2018. ‘The Seductive Diversion of “Solving” Bias in Artificial Intelligence’. Medium. 7 December 2018. https://medium.com/s/story/the-seductive-diversion-of-solving-bias-in-artificial-intelligence-890df5e5ef53.

Wagner, Ben. 2018. ‘Ethics as an Escape from Regulation. From “Ethics-Washing” to “Ethics-Shopping?” ’ In Being Profiled: Cogitas Ergo Sum: 10 Years of Profiling the European Citizen, edited by Emre Bayamlioglu, Irina Baraliuc, Lisa Janssens, and Mireille Hildebrandt, 84–87. Amsterdam: Amsterdam University Press.

On fair computing and framing problems

Barocas, Solon, and Andrew D. Selbst. 2016. ‘Big Data’s Disparate Impact’. California Law Review 104: 671–732.

Barocas, Solon, Moritz Hardt, and Arvind Narayanan, draft version of Fairness and Machine Learning. Limitations and Opportunities. https://fairmlbook.org/pdf/fairmlbook.pdf.

Callon, M., and J. Law. 2005. ‘On Qualculation, Agency, and Otherness’. Environment and Planning D: Society and Space 23 (5): 717–33.

Chouldechova, Alexandra. 2017. ‘Fair Prediction with Disparate Impact: A Study of Bias in Recidivism Prediction Instruments’. Big Data 5 (2): 153–63. https://doi.org/10.1089/big.2016.0047.

Chouldechova, Alexandra, and Aaron Roth. 2018. ‘The Frontiers of Fairness in Machine Learning’. ArXiv:1810.08810 [Cs, Stat], October. http://arxiv.org/abs/1810.08810.

Dressel, Julia, and Hany Farid. 2018. ‘The Accuracy, Fairness, and Limits of Predicting Recidivism’. Science Advances 4 (1): eaao5580. https://doi.org/10.1126/sciadv.aao5580.

Equivant. 2018. ‘Response to ProPublica: Demonstrating Accuracy Equity and Predictive Parity’. Equivant (blog). 1 December 2018. https://www.equivant.com/response-to-propublica-demonstrating-accuracy-equity-and-predictive-parity/.

Equivant. 2018. ‘Official Response to Science Advances’. Equivant (blog). 18 January 2018. https://www.equivant.com/official-response-to-science-advances/.

Fricker, Miranda. 2007. ‘Hermeneutical Injustice’. In Epistemic Injustice: Power and the Ethics of Knowing, 147–75. Oxford: Oxford University Press. https://www.oxfordscholarship.com/view/10.1093/acprof:oso/9780198237907.001.0001/acprof-9780198237907-chapter-8.

Gallie, W.B. 1956. ‘Essentially Contested Concepts’. Proc. Aristotelian Soc’ty 56: 167–98.

Kroll, Joshua, Joanna Huey, Solon Barocas, Edward Felten, Joel Reidenberg, David Robinson, and Harlan Yu. 2017. ‘Accountable Algorithms’. University of Pennsylvania Law Review 165 (3): 633.

(p.318) Northpointe. 2012. Practitioners Guide to COMPAS. http://www.northpointeinc.com/files/technical_documents/FieldGuide2_081412.pdf.

On the inner morality of the Rule of Law (and Rule of Law in cyberspace)

Brownsword, Roger. 2016. ‘Technological Management and the Rule of Law’. Law, Innovation and Technology 8 (1): 100–40. https://doi.org/10.1080/17579961.2016.1161891.

Dworkin, Ronald. 1991. Law’s Empire. Glasgow: Fontana.

Hildebrandt, Mireille. 2015. ‘Radbruch’s Rechtsstaat and Schmitt’s Legal Order: Legalism, Legality, and the Institution of Law’. Critical Analysis of Law 2 (1). http://cal.library.utoronto.ca/index.php/cal/article/view/22514.

Rawls, John. 2005. A Theory of Justice. Cambridge, MA: Belknap Press.

Reed, Chris, and Andrew Murray. 2018. Rethinking the Jurisprudence of Cyberspace. Cheltenham: Edward Elgar.

Waldron, Jeremy. 2011. ‘The Rule of Law and the Importance’. Nomos 50: 3–31.


(3) See the judgment of the Supreme Court of Wisconsin, 881 N.W.2d 749 (Wis. 2016), available at: https://www.scotusblog.com/wp-content/uploads/2017/02/16-6387-op-bel-wis.pdf.