Safety requirements vs. crashing ethically: what matters most for policies on autonomous vehicles

Abstract

The philosophical–ethical literature and the public debate on autonomous vehicles have been obsessed with ethical issues related to crashing. In this article, these discussions, including more empirical investigations, will be critically assessed. It is argued that a related and more pressing issue is questions concerning safety. For example, what should we require from autonomous vehicles when it comes to safety? What do we mean by ‘safety’? How do we measure it? In response to these questions, the article will present a foundation for a continued discussion on these issues and an argument for why discussions about safety should be prioritized over ethical concerns related to crashing.

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AI & SOCIETY (2021) 36:405–415
https://doi.org/10.1007/s00146-020-00964-6
ORIGINAL ARTICLE
Safety requirements vs. crashing ethically: what matters most
forpolicies onautonomous vehicles
BjörnLundgren1,2
Received: 27 December 2019 / Accepted: 3 March 2020 / Published online: 6 April 2020
© The Author(s) 2020
Abstract
The philosophical–ethical literature and the public debate on autonomous vehicles have been obsessed with ethical issues
related to crashing. In this article, these discussions, including more empirical investigations, will be critically assessed. It
is argued that a related and more pressing issue is questions concerning safety. For example, what should we require from
autonomous vehicles when it comes to safety? What do we mean by ‘safety’? How do we measure it? In response to these
questions, the article will present a foundation for a continued discussion on these issues and an argument for why discus-
sions about safety should be prioritized over ethical concerns related to crashing.
Keywords Autonomous vehicles· Self-driving vehicles· Ethical crashing· Trolley problem· Safety argument· Vision zero
1 Introduction
It is widely presumed that autonomous (or self-driving)
vehicles will be safer than human-driven vehicles. This,
in turn, is often recognized as an important argument for
the future implementation of autonomous vehicles. Indeed,
many authors have argued that autonomous vehicles’ poten-
tial to be safer than ordinary vehicles provides strong ethical
reasons to develop and then transition to using such vehi-
cles. I will refer to this, and similar ideas, as ‘the safety
argument’. According to Daniel J. Hicks, versions of this
“safety argument is perhaps the most widely cited argument
in favor of the rapid development and widespread adop-
tion of” autonomous vehicles (2018, p. 63). However, in
the philosophical–ethical literature and in the public debate
on autonomous vehicles, most papers discuss the issue of
crashing—with a focus either on how we should crash (i.e.,
ethical crashing) or who is responsible in the event of a crash
(see, e.g.,Doctorow 2015; Hern 2016; Jaipuria 2017; Leben
2017; Lin 2014, 2015; Simon 2017; Wolkenstein 2018; see
also Nyholm 2018a, b for overviews). In the debate on
ethical crashing, there seems to be an implicit belief that
since autonomous vehicles will be extremely safe, the issue
of safety requirements will be of less importance than the
issue of ethical crashing.
In this article, I will first critically assess the discussion
on ethical crashing to argue that there are serious flaws in
the discussion and that there is a further need to evaluate the
safety argument. Next, I will critically evaluate the safety
argument to illustrate that there are fundamental policy
issues that need to be sorted out in relation to this argument,
issues that are more pressing than ethical crashing. I am set-
ting aside the issue of responsibility for crashes because two
manufacturers recently declared that they will take respon-
sibility for the accidents (Atiyeh 2015; Maric 2017)—if this
trend continues the question of responsibility will (from a
policy perspective) be less pressing. Questions of forward-
looking responsibility may still be important from a policy
perspective, but—as my argument will indicate—they relate
strongly to the safety argument.1
Before turning to the arguments, I should mention the
limitations and scope of the arguments in this article. First,
I am concerned with a technologically near (or close) future.
By ‘technologically near future’, I am not referring to a spe-
cific time, but rather a future in which autonomous vehicles
start to be implemented broadly and a future in which there
* Björn Lundgren
bjorn.lundgren@iffs.se
1 Institute forFutures Studies, Stockholm, Sweden
2 Department ofPhilosophy, Stockholm University,
Stockholm, Sweden
1 This does not mean that it is not philosophically interesting to dis-
cuss who is responsible. It just means that the policy question is prac-
tically resolved.
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406 AI & SOCIETY (2021) 36:405–415
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is mixed traffic (i.e., traffic including both autonomous and
human-driven vehicles). Thus, the issues that I will discuss
in this article do not concern a technologically further future
in which autonomous vehicles will have taken over; what
I am concerned with is the issue of what policies should
guide us to (or away from) such a future. Second, as already
indicated, I am interested in applied normative questions.
That is, ethical concerns that are relevant for policies. Thus,
I am less concerned with normative evaluations of science-
fiction. I am also less interested in more theoretical debates
on right and wrong. That is, while some considerations are
theoretical in nature, they should be policy relevant. It is in
this light that I will criticize the current focus in the ethical
discussion on autonomous vehicles.
The rest of the article will be structured as follows. In
Ethical crashing, I will critically assess the discussion on
ethical crashing. In The safety argument, I will turn to the
safety argument. Finally, I will conclude and summate my
findings.
2 Ethical crashing
2.1 What iswrong withthediscussion onethical
crashing?
In this section, I will critically assess the discussion on ethi-
cal crashing, in particular the “methodological” focus of this
debate, which is inspired by the so-called ‘trolley problem’
(Foot 1967; Thompson 1985). Thus, the focus is on what
SvenNyholm and JillesSmids (2016) call ‘applied trolley
problems’ (i.e., binary choice situations of how to crash
in a situation when an accident is unavoidable). This has,
arguably, been the most common focus in the philosophical
and public debate on the ethics of crashing (see Nyholm
2018a for an overview). While it is fair to say that there is
no consensus in the literature, I will refine some older argu-
ments and introduce some new ones in support of the posi-
tion that holds that the ‘trolley methodology’ is mistaken in
some sense (e.g., because the applied trolley problems are
irrelevant or misleading for the issue of ethical crashing).
Despite broad criticism, application of the trolley method-
ology has been defended as recently as this year by Geoff
Keeling (2020) and became broadly well-known because of
the so-called Moral Machine experiment (Awad etal 2018).
According to Edmond Awad etal., consumers will only
switch from human-driven vehicles to autonomous vehicles
if they understand the origins of the ethical principles that
are programmed into these vehicles (p. 59). This, according
to Awad etal., implies that:
even if ethicists were to agree on how autonomous
vehicles should solve moral dilemmas [i.e., these
applied trolley problems], their work would be use-
less if citizens were to disagree with their solution,
and thus opt out of the future that autonomous vehi-
cles promise in lieu of the status quo. Any attempt
to devise artificial intelligence ethics must be at least
cognizant of public morality (p. 59; my addition within
brackets).2
Hence, Awad etal. attempted to investigate the public’s
preferences of decision-making in applied trolley problems
(i.e., of “unavoidable accident scenarios with two possible
outcomes”), proposing that “these preferences can contrib-
ute to developing global, socially acceptable principles for
machine ethics” (ibid).3
Like most ethicists discussing ethical crashing, Awad
etal. discusses binary choice situations of unavoidable acci-
dents; accidents involving, for example, the choice between
the unavoidable killing of a man and a woman. While dis-
cussions of such examples have been broadly popular, engi-
neers have argued that they have yet to encounter a trolley
problem and “if we did see a scenario like that, usually that
would mean you made a mistake a couple of seconds earlier”
(Hern 2016).4
So why would the focus on these binary choices make
sense? Contrary to the engineer’s supposition of the vehi-
cles’ faultlessness, autonomous vehicles will inadvertently
crash (see, e.g.,Goodall 2014a, 2014b; Lin 2015). Given
that all crashes, arguably, include trade-offs, the vehicle must
be prepared to crash in an ethical way. For example, Patrick
Lin uses various types of trolley-like situations where the
vehicles must choose between, for example, crashing into an
8-year old girl and an 80-year old grandmother (2015, p. 70).
This type of example is supposed to illustrate the trade-off
that is at the core of the trolley problem. Lin also thinks that
these examples relevantly illustrate the need to program the
vehicles to make ethical choices insituations where harm is
unavoidable (ibid). But do they? There are several reasons
to be critical—in particular—of the trolley methodology, but
also—in general—of the discussion of the ethics of crashing.
Most of the problems I want to discuss relates to differ-
ent problems of idealization. First, the application of trolley
problems with scenarios involving two possible outcomes
are highly idealized. But reality is not. And when you add
uncertainty to a situation you are arguably changing the
2 Casey (2017) argues that the problem should be resolved by law-
yers instead, by making use of liability regulations.
3 Recently Harris (2020) have criticized the Moral Machine experi-
ment for, for example, conflating preferences with morality, calling
the work of Awad etal. “useless” (pp. 74–75).
4 That we should focus on avoiding trolley problems, has been
defended more extensively by Alexander G. Mirnig and Alexander
Meschtscherjakov (2019).
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407AI & SOCIETY (2021) 36:405–415
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normative analysis of it. Previously, Nyholm and Smids
(2016; cf., e.g., Goodall 2016 Himmelreich 2018), building
on the work by Sven Ove Hansson (2013, cf. 2003) have crit-
icized the usage of trolley problems, for example, for ignor-
ing the fact that the machine decision-making involves risk
(i.e., decision under known probabilities) and uncertainty
(i.e., decision under unknown probabilities). Henceforth, I
will sometimes—for simplicity—use ‘risk’ or ‘uncertainty’
to indicate both risk and uncertainty (cf. Hansson 2003).
As argued by Hansson (2003), there has been a flawed
division of labor in philosophy, in which ethics deals with
idealized and well-determined situations under the suppo-
sition that when the ethics of these idealized situations are
resolved, decision-theory can deal with any uncertainties.
However, Hansson argues that risks and uncertainties must
be normatively evaluated (i.e., risk and uncertainty itself
presents ethical problems that are not reducible to idealized
examples), so risk and uncertainty cannot be dealt with by
decision-theory alone. Supposing Hansson is right, then it
is fair to say that there is “a categorical difference between
trolley-ethics and accident-algorithms for AVs” (Nyholm
and Smids 2016).
Recently, Keeling (2020) attempted to counter this argu-
ment by showing that the difference between choices in
scenarios with absolute descriptions and standard decision-
making under risk are not sufficiently different to warrant the
claim of a categorical difference (pp. 299–300). However,
this seems to miss the point made by Hansson (2003) that
risky decision requires normative evaluation beyond what
standard risk analysis offers. Hansson argues that stand-
ard normative theories currently do not address risks in a
satisfactory way and his proposal ends up being very dif-
ferent from a standard risk analysis, which is merely about
expected utility maximation. WhatHansson proposes is that
we have a prima facie—or pro tanto—right not to be exposed
to risks, a right that may be overridden under specific condi-
tions (2003).5
Although such arguments are not universally accepted
(e.g., objective utilitarians would deny this), it is still a sub-
stantial question how we can translate ethical evaluations
of absolute outcomes with perfect information to situations
involving probabilities and uncertainty. As pointed out by
Adam Bjorndahl etal (2017), Decisions that are easy to
make under certainty can become much more difficult and
morally fraught under uncertainty.
For the empirical methods of Awad etal., the situation
is worse, since they are dealing with people’s preferences,
they cannot rely on a normative theory of how to aggregate
from preferences about choices in scenarios with absolute
descriptions to scenarios involving risk and uncertainty. The
problem is that it is not evident that people’s preferences in
idealized scenarios, with certain outcomes of well-defined
harm, can be perfectly converted to preferences in real situ-
ations involving risks of uncertain harms. For example, it is
well-known that risk preferences cannot be presumed to uni-
formly match with standard models of expected utility (see,
e.g,Kahneman and Tversky 1979), and it has been shown
that even factors such as time can influence risk preferences
(Andreoni and Sprenger 2012). Arguably, to enable a per-
fect conversion, we would have to presume the truth of the
independence axiom, which sometimes fail to hold with gen-
erality under experimental settings (see, e.g.,Chandler 2017
for a brief overview). The independence axiom allows us to
deduce choice-preferences for more granular and complex
situations from preferences of choices in simpler situations.6
Second, another form of idealization involves the problem
of human–machine incongruence. Simply put, it is not evi-
dent that human preferences can be translated into rules for a
machine. This is because choice-descriptions from a human
and a machine perspective differs and may be incongruent.
Indeed, the machines may both lack information humans
have and vice versa, or the machine descriptions may be
incompatible with human descriptions of reality, possibly
making a translation impossible. Thus, it is not obvious that
we can construct machines rules that satisfy the surveyed
preferences, which potentially would provide a problem for
policies based on such preferences. (cf. Lundgren 2020b).
A related argument is presented by Johannes Himmel-
reich (2018), who argues that reliance on trolley problems
assumes “a top-down” design approach, which implements
rules rather than allowing the system to learn (pp. 675–676).
More to the point, conclusions about what should be done
in singular trolley situations are hardly helpful, since the
examples are too few to enable training data for so-called
‘machine-training’. This is true even for the 26 million
possibilities considered in the Moral Machine Experiment
survey (Awad etal 2018, see the complimentary method
description). Part of the problem is, again, the focus on
binary choices, ignoring all forms of situational complexi-
ties and risk and uncertainty. Hence, the information is not
representative for the complexity of reality. Alternatively,
if we instead try a top-down approach, we could attempt
to program the vehicles based on the results of Awad etal.
However, as previously noted, it is unclear what the results
5 Hansson uses the term ‘prima facie’ following a long-standing tra-
dition in ethics. However, as pointed out by me Lundgren (2020a), it
would be more sensible to talk of a pro tanto right in this case, since
as argued by Shelly Kagan (1989), ‘prima facie’ is an epistemic con-
cept (i.e., something that appears to be have genuine weight), while
‘pro tanto’ indicates something that has genuine weight but may be
overridden.
6 I want to thank Erik Angner for a helpful conversation on the last
issue in the paragraph.
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408 AI & SOCIETY (2021) 36:405–415
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are. For example, even if a majority prefers to save women
over men, how can we translate that conclusions from binary
choice situations to a broader principle ready for program-
ming? We, certainly, cannot—in practice—encode each and
every possible situation and if we were to attempt that, these
26 million possibilities are way too few.7
Keeling (2020, pp. 301–303) attempts to counter these
types of arguments by noting that discussions of trolley
problems may nevertheless be useful in that sense that
analysis of them provide an answer to the question what we
ought to do. While this may motivate ethicist to focus on the
applied trolley problems, it offers no argument for applying
the more empirical methods of Awad etal. More impor-
tantly, when doing applied ethics, it is arguably important
to contribute with something that can actually be applied.
I am not denying that trolley problems can be an important
tool for normative ethics, but if we want our conclusions
about applied ethical issues to be useful in practice (i.e.,
for real policies), then something must be added. Keeling
does not seem to address this issue, which is arguably the
real problem (or at least the argument that I and others are
concerned with).
Third, the problem of human–machine incongruence is
also related to what we may call the ‘science–fiction pre-
sumption’ (or at least presumptions that fall outside of the
scope of a technologically near future). The problem is that
in the discussion of ethical crashing, idealization does not
only apply to the situations, but also to the type of informa-
tion that the machines will be able to access—or extract
from reality—instantly, while making a choice on how to
crash. To illustrate my point, consider some of the vivid
examples used in the applied trolley problems. For example,
Lin’s previously mentioned example—that is, of choosing
between crashing into an 8-year-old girl and an 80-year-old
grandmother—would require both instant face-recognition
capabilities and retrieval of personal information. However,
the problem also extends outside of the trolley methodology.
For example, Derek Leben (2017), in arguing for a Rawl-
sian algorithm—based on a normative evaluation of utility
in terms of the likelihood of survival, which we can ques-
tion in its own right—“assume[s] that it is possible for an
autonomous vehicle to estimate the likelihood of survival
for each person in each outcome” (p. 110). Such abilities are
far from the current and near future autonomous vehicles.
Some of these examples would require an ability for autono-
mous vehicles to perform instant and complicated object
identification (sometimes not only for types, but for tokens)
and information retrieval in a time-limited accident situa-
tion. Similarly, evaluating the likelihood of survival with
any precision would require tremendous capabilities not yet
available.8
One may argue that science–fiction discussions, like
Leben’s examples, are still valuable. That is, one may argue
that if the normative argument holds, which we can question,
we can potentially use these idealized arguments as a guide
on what to do insituation in a technologically closer future
(similar to the argument from Keeling considered previously
above).9 However, the question, again, would be how we can
abstract from these idealized (science–fiction) situations to
fit with the way that current and near future technology does
or will function.
Fourth, the discussion on human–machine incongruence
also raised the issue that the accident situations are too ide-
alized (i.e., beyond the issue of probabilities and uncertain-
ties). As previously indicated, traffic situations like those
envisioned in binary choice situations are arguably rare (cf.,
e.g.,Hern 2016). Thus, even if discussions of applied trol-
ley problems could give us guidance about how machines
should act and be programmed or trained in binary choice
situations of unavoidable crashes, it is not evident how we
can extrapolate moral choices for any type of traffic accident
from preferences or moral choices in trolley-based traffic
accidents or a small sub-set of traffic accidents. Crashes in
normal traffic are often more complex and involve many
more choices (cf., e.g.,Borenstein etal 2019), so it is not
evident how we can abstract from moral choices in simple
situations to moral choices in more complex situations.
Furthermore, the ethical choice of an autonomous vehi-
cles, even in a crashing situation, cannot be designed in
isolation. We must take the whole infrastructure into con-
sideration (a similar point is made by Borenstein etal 2019;
cf. also Nyholm and Smids 2016). Indeed, take—as an
illustrative example—the Vision Zero policy, which aims
to remove fatal and serious injuries (see, e.g.,Belin etal
2012). It addresses the whole infrastructure. More impor-
tantly, it contradicts the engineer who previously argued
8 In connection with this argument it is worth to mention that despite
enthusiasm amongst some engineers and companies, there have been
an increased skepticism about when, or even if, we can achieve a
level 5 autonomous vehicle (see, e.g., Tibken 2018; Murray 2019;
Henry 2020). Level 5 standardly implies full automation under all
road conditions (see, e.g.,SAE 2018). It is easy to see why this would
be problematic if we take level 5 to include the ability to use as infor-
mation input the kind of bodily expressions that pedestrians use to
communicate with human drivers when, for example, passing a street.
9 The normative presumptions are problematic for several reasons,
for example, since this may exclude serious harm from which a per-
son is likely to survive. See Keeling (2018) for a detailed criticism of
the normative ideas underlying Leben’s argument (including whether
it is actually Rawlsian).
7 I am not saying that this cannot be given an answer (e.g., we can
assign a specific priority to women over men in accordance with the
mean preference), I am saying that it is not clear whether this cap-
tures the actual preferences.
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409AI & SOCIETY (2021) 36:405–415
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that accidents are an error, since avoiding fatal and seri-
ous injuries can sometimes require more non-serious traffic
accidents. For example, a round-about would normally have
more accidents than an intersection, but the accidents in a
round-about would mostly be non-serious, while accidents
in intersections are often serious (ibid).10 What we should
infer from this example is that we cannot evaluate ethics of
any machine or machine-decision in isolation, but only as
part of a larger system. The ethics of machine decisions need
to take this system approach into consideration.
Lastly, there is a problem that only concerns the Moral
Machine experiment. It is the problem of ethically bad pref-
erences. That is, although Awad etal. claims that consumers
will only shift technology if the ethical choices underpinning
these machines respects the public opinions,11 it is not evi-
dent that preferences should guide moral action (cf. Harris
2020). Indeed, suppose that a majority has preferences for
racist policies, what guidance should that give us? Arguably,
none. Thus, even if the survey reflected actual preferences of
more realistic situations, which could be translated into rules
for a machine, it is questionable if it should give us guidance
in deciding upon such rules.
2.2 Why should we turn tothesafety argument?
As I have argued there are various problems with the discus-
sion on ethical crashing. However, that does not necessarily
imply that we should direct our focus to the safety argument.
Alternatively, it may imply that we need to revise how the
issue of ethical crashing is discussed. Thus, before turning
the discussion of the safety argument, I will briefly defend
the idea that the safety argument is a more pressing policy
concern, and that discussions of the safety arguments are
relevant for further discussions of the ethics of crashing.
To answer this question, we should first look to the argu-
ments in favor of the importance of the ethics of crashing.
For example, Awad etal. argues that there is something spe-
cial about a situation in which machines will make decision
about who lives or dies (2018, p. 59). However, it is not clear
why that requires more attention from a policy perspective,
than choices in which human’s make determinations about
lives. Nor is it clear why it is more important who dies from
a machine than whether the machine imposes serious risks
to people’s life or the quality of their lives.12
Nyholm (2018a) provides another form of argument.
Based on three recent examples (from 2016 to 2018) of acci-
dents involving autonomous vehicles, in which the failure
was (at least in part) due to the machines rather than other
human drivers, he argued that:
These incidents in 2016 and 2018 illustrate that crashes
involving self‐driving cars are not merely material for
hypothetical thought experiments. This is a real‐world
issue. It requires a serious response from both society
and the developers of self‐driving cars. Human lives
are at risk. Accordingly, the new and developing topic
of the ethics of crashes with self‐driving cars is a very
important one. (pp. 1–2).
Yet, these—arguably anecdotal—examples are insuffi-
cient to establish how important this issue will be. Even if
crashes—in a technologically near future—remain relatively
common, the ethics of crashing is only relevant for a subset
of all accidents (i.e., those involving substantial choices).
That subset is likely much smaller than the amount of people
we can save by appropriate safety requirements. Thus, it is
arguably more pressing to consider what we should accept
when it comes to accidents and safety policies.13
Furthermore, as pointed out already by Bryant Walker
Smith (2015, cf. Thierer 2015), under the presumption that
10 This argument will apply to autonomous vehicles if they, like
humans, have a behavior that is partly imperfect, as it relates to the
given safety-goal. While it should be held true that AI applications
will be partly imperfect, it is likely that the autonomous vehicles will
result in different kinds of errors than those of human drivers.
11 The implicit claim that consumers would not purchase (or use)
an autonomous vehicle without influence over the ethics settings is
not supported by Awad etal. and there is prima facie evidence to the
contrary. You can look to products or services on the market with
unpopular policies, but more importantly at least one survey asked
the question of “Who should determine how the car responds to the
Tunnel Problem?” (the Tunnel Problem is an applied trolley prob-
lem in which the choice is between killing you—the passenger—or
a child). Respondents answer: Passenger (44%), Lawmakers (33%),
Manufacturer/designer (12%), and Other (11%) (Moon et al 2014).
Although there are methodological limits to this survey, we should
note that even if we take “Other” to be “the public”, it is only a weak
majority that answers in a way that may support Awad et al. Moreo-
ver, the survey do not ask what you would require to buy the vehicle,
but what you would prefer. We have no reason to think that everyone
who would prefer to set their own settings would also require such a
function to buy the vehicle.
12 An illustrative example is the case of the Ford Pinto. According to
Ibo van de Poel and Lambèr Royakkers, Ford knew that the car could
explode under special circumstances. They could also make adjust-
ments that would protect against it. Ford opted not to do so, based on
a cost–benefit analysis of the societal costs and benefits. In the end
the vehicle exploded with a couple of teenagers inside (2011: 65–70).
13 Now, of course, it may seem as if I have not only re-introduced the
trolley problem, but also argued that the answer is simple: prioritize
more people’s lives over fewer. However, the argument above lacks
an important element in order for it to match with the standard trol-
ley problem. In the trolley problem we have a choice between action
and inaction, to interject in an ongoing event. Here we have a choice
what to focus our research endeavors on. That some may already have
focused on applying the trolley problems to autonomous vehicles is
certainly no reason to keeping doing it. (We could potentially argue
that the same argument applies to how the trolley problem is used in
the discussion—i.e., that it is not really a trolley problem.)
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410 AI & SOCIETY (2021) 36:405–415
1 3
autonomous vehicles have a potential to reduce the death
toll from accidents substantially, we should ask: “what is
theproper balancebetween caution and urgency in bring-
ing these systems to the market?How safe is safe enough?”
These issues are not only more important than the ethics of
crashing, but the importance of ethical crashing also depends
on these issues. If autonomous vehicles cannot be justified,
then the ethics of crashing is just a theoretical problem of
little or no practical concern.14 Thus, these issues are related,
and the relevance of ethical crashing depend on settling the
questions related to the safety argument.
Presumably, however, autonomous vehicles can be jus-
tified. Nevertheless, the issues of justification and safety
requirements are more important, because it is about the
fundamental question whether we should use the technology
at all and if so, how? That is, under which conditions should
it be allowed, relative to safety requirements, to broadly use
autonomous vehicles and under which conditions should we
switch from human driven vehicles to autonomous vehicles?
While the debate on ethics of crashing and the responsibility
of crashes seems to presume that “Self-driving cars hold out
the promise of being much safer than regular cars” (Nyholm
2018a, p. 2; cf., e.g., Hevelke and Nida-Rümelin 2015, p.
620), I will argue in “The safety argument” that this claim
is more complicated than it prima facie seems and that the
safety argument requires further analysis. Hence, I will now
turn to address issues relating to these questions.15
3 The safety argument
3.1 Specifying thesafety argument
As implied by Hicks (2018), one of the main reasons to favor
an autonomous vehicle over a human-driven vehicle would
be that the former is supposed to be safer than the latter. In
this part I will critically asses this argument (i.e., the safety
argument). To do so, I will introduce a prima facie reason-
able specification of the argument in the form of a justifica-
tion-criterion: a necessary criterion of justification for the
broad usage of autonomous vehicles is that they should be
at least as safe as human-driven vehicles. Henceforth I will
call this the ‘safety-criterion’.
The aim here is to argue that there are complications
related to the safety-criterion that deserve further attention
from an ethical perspective as policy considerations are con-
cerned. Note that while I have stipulated the thesis I want to
consider (i.e., the safety-criterion), the discussion will not
depend on accepting the safety-criterion as such. Even if we
reject this thesis, or argument, the discussion will still be rel-
evant for other versions of the safety argument more broadly.
That is, the main point is to illustrate that the discussion on
these issues deserve more attention. In particular, I will aim
to specify what we need to discuss and introduce some pre-
liminary suggestions on how this discussion should proceed.
The discussion will require a degree of conceptual analy-
sis and it involves some empirical issues, which are partly
normative. More importantly, satisfying the safety-criterion
is further complicated by the fact that there are policy pro-
posals that we out to enact, which would improve the safety
of human-driven vehicles; thus further pushing the demands
on the level of safety that autonomous vehicles must achieve.
3.2 What dowe mean by‘safety’?
To determine whether autonomous vehicles are as safe as
human-driven vehicles, we must first qualify what we mean
by ‘safety’. Such a qualification is not, generally, as straight
forward as it may seem. Indeed, while ‘safety’ standardly, in
a technical context, is thought of as the inversion of risk, the
concept is arguably more complex (Möller etal 2006). What
we may call the traditional view of traffic safety, matches
close with the standard technical conception of safety. Tra-
ditionally traffic safety is defined as the absence of acci-
dents. In some more modern traffic safety policies, such as
the Vision Zero policy, safety is defined as the absence of
severe or lethal accidents (see, e.g.,Belin etal 2012).
As previously noted, these two ideas about what safety is
(or what the goal of safety is) yields different policy propos-
als. This is because there are trade-offs between different
forms of accidents. Again, an illustrative example of this are
roundabouts, which have a higher accident rate than four-
way crossings. However, with a round-about the accidents
are mainly non-severe examples of vehicles brushing into
each other. Comparatively, while four-way crossings have
fewer than round-abouts, when accidents occur—in four-way
crossings—they are usually of a more severe kind (such as
full-frontal or frontal-side collisions). Thus, if we want to
avoid as many accidents as possible, then a four-way cross-
ing is better than a round-about; if we want to avoid severe
and lethal accidents, then a round-about is better than a four-
way crossing.
Although the Vision Zero policy have been criticized (see
Abebe etal 2020 for an overview), we have reason to settle
14 Autonomous vehicles are, of course, already on our roads. But
it is not impossible that we may conclude that autonomous vehicles
should not be broadly used, and this decision can be made before the
implementation of any crashing algorithms. If so, then the ethics of
crashing is just a theoretical problem.
15 Of course, justification certainly depend on other issue than traffic
safety (such as climate effects—see Kopelias etal 2019 for a recent
review article on environmental impacts and climate effects of auton-
omous vehicles). For simplicity I will set those issues aside in part of
the upcoming discussion to show that issues related to safety require
further normative analysis.
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411AI & SOCIETY (2021) 36:405–415
1 3
for the Vision Zero policy, rather than the traditional view,
if we think of lethal or severe accidents as unacceptable in
road traffic. Such a view may, for example, be supported
by Hansson’s analysis of the ethics of risk. As previously
noted, according to Hansson we have a prima facie—or, pro
tanto16—right not be exposed to risks.17 Hansson argues
that this right can only be overridden under some specific
circumstances such that:
Exposure of a person to a risk is acceptable if and
only if the total benefits that the exposure gives rise to
outweigh the total risks, measured as the probability-
weighted disutility of outcomes. (2003, p. 306).
A potential problem, however, is that engineers seems to
develop autonomous vehicles in accordance with the tradi-
tional traffic safety view (cf., e.g., Hern 2016; Mirnig and
Meschtscherjakov 2019). Of course, it may turn out that the
best strategy to achieve autonomous vehicles that reduces
severe and lethal accidents is to reduce accidents in gen-
eral. Nevertheless, traffic planners, should still approach the
traffic system with an intent to minimize severe and lethal
accidents (even if the best practices for achieving that may
change if autonomous vehicles differ in substantial ways
from human-driven vehicles).
3.3 Should safety ofautonomous vehicles be
reduced merely toaccident‑related safety?
When we talk of safety in this context, we implicitly seem to
think of accident-related safety (as implied by the previous
section). However, in western societies, exhaustion and noise
from traffic have a more substantial effect on human lives
than accidents. For example, in the US, more Americans
die from pollutions from vehicles than from traffic accident
(Caiazzo etal 2013, p. 207). Furthermore, according to
TheWorld Health Organization, traffic noise is second only
to air pollution when it comes to health effects.18
This clearly implies that traffic policies need to take a
broader scope of issues into consideration. In line with
Vision Zero we should accept a zero policy for road traffic
deaths and severe harm more broadly, not just in relation to
accidents.
These factors may, to some extent, depend on switching to
autonomous vehicles. For example, switching to autonomous
vehicles may affect number of vehicles or the total travelled
distance (see, e.g.,Soteropoulos etal 2018). Nevertheless,
for simplicity I will set these issues aside to—in the next
sections—further investigate the safety-criterion and safety-
argument relative to accident-related safety-requirements.
3.4 Problems ofmeasuring thesafety
ofautonomous vehicles
Supposing we have settled for the relevant measurement and/
or conception of safety (or that we can deal with different
conceptions and measurements at the same time), then the
issue of determining the safety level of autonomous vehicles
still remain. According to NidhiKalra and Susan M.Pad-
dock, autonomous vehicles would need to drive 275 million
miles “without failure to demonstrate with 95% confidence
that their failure rate is at most” 1.09 deaths per 100 million
miles (2016, p. 191). Of course, as previously noted, fail-
ures have already occurred. Thus, in order “to demonstrate
with 95% confidence and 80% power that their failure rate
is 20% better than the human driver failure rate of” 1.09
deaths per 100 million miles would require 11 billion miles
driven. According to Kalra’s and Paddock’s estimates, this
would take 500years. Comparatively, Hicks estimates that
it would go much faster, only 84years (2018, p. 64). Thus,
even with more favorable estimates, using the best available
observational data, it will take a lot of time to determine
if autonomous vehicles are indeed as safe as or safer than
human-driven vehicles.19
However, although companies developing autonomous
vehicles use many alternative methods, many of these have
limits as well. For example, results from simulations and
mathematical proofs are merely as certain as the assump-
tions that they are based on (Hicks 2018, pp. 64–65). How-
ever, most developers of autonomous vehicles probably use
a combination of technologies which may allow them to test
and revise such assumptions. For example, while Waymo
uses conventional road tests they have also “driven "tens
of billions" of miles through computer simulations” (Nieva
2020). Another type of combined effort is used by Tesla.
When the vehicle is controlled by a human, the automated
16 See footnote 5.
17 This, of course, does not imply that competing ideas (such as cost–
benefit analysis) are necessarily wrong when they claim that non-
severe accidents are more costly than severe or lethal accidents. It just
means that we have settled the conceptual issue of the main safety
concern (i.e., that it is about is severe physical harm to human lives).
18 https ://www.trans porte nviro nment .org/what-we-do/vehic le-noise .
19 It is worth to point out that, for example, for Waymo (Google’s
autonomous vehicle company) “It took 10years for the first 10 mil-
lion, then a little over a year for the next 10 million” (Nieva 2020).
At a speed of 10 million miles per year it would take Waymo along
1100years to do 11 billion miles. However, that is just one company
and what these number shows is an incredible increase in numbers
of miles driven per year. For example, if we have 20 companies
doing the same mileage that gives us 55years (excluding any yearly
increase in mileage). At the time of writing this Waymo will soon
start mapping routes in Texas and New Mexico, which will then be
followed by truck driving (Woodyard 2020). These and other trends
illustrate that the increase in resources continue in this area, which
will increase mileages per year.
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412 AI & SOCIETY (2021) 36:405–415
1 3
system will run in the background and compare its deci-
sions against the human decisions. However, although that
provides us with lots of relevant data, it does not tell us if
alternative strategies by the autonomous system would have
been successful (Hicks 2018, pp. 64–65).
An alternative option is the possibility of performing
experiments with the aim of testing autonomous vehicles
in extreme and difficult situations. Indeed, Neil McBride
(2016) suggests a driver test for autonomous vehicles (i.e.,
requiring a driver license for the vehicle). Testing has been
broadly suggested before (see, e.g., Koopman and Wagner
2017, p. 93, for several references). Of course, such test must
be designed with a high level of variation to both ensure that
the vehicle can manage all types of road conditions (for an
early attempt to develop a framework for safety validation,
see Koopman and Wagner 2018) and to ensure that vehicle
manufactures do no designing their vehicles simply for the
test, rather than for natural situations, similar to how Volk-
swagen cheated on emission tests.20 Relatedly, there is also
the question whether the source code should be evaluated by
an independent organization (Holstein etal 2018).
Furthermore, we can also aggregate from different meth-
ods. Indeed, if different ways of measuring the safety of
autonomous vehicles converges, then that strengthens the
evidence. Unless there is a systematic error for each or all
testing methods, multiple methods can be used to gain reli-
able results since it is possible to statistically exclude the
small chance that they converge because of coincidence or
random errors.
These empirical questions are important because we must
ask ourselves how much empirical evidence that we should
require to satisfy the safety-criterion or any specific safety
requirement. However, we should also recognize that we
currently experience similar challenges with new vehicle
models. There is uncertainty as to whether they will be as
safe as vehicles already available. Yet, once a new type
of vehicle is released, statistical data would more rapidly
accumulate.21 Nevertheless, we should recognize that new
models are reasonably closer in kind to what is already
available.22
3.5 Safe ascomparative towhat?
The issue of comparing autonomous vehicles and human-
driven vehicles also depend on applying a correct com-
parison. Arguably, comparing with current death rates of
accidents (or other current accident rates), as Karla and
Paddock (2016) does, is normatively misleading. That
is, although autonomous vehicles may offer a promise to
provide a safer option than human-driven vehicles—sim-
ply because human error is the major cause of accidents
(approximately 93% in the USA)—it needs to be noted that
a third of the human errors are due to intoxication, 30% is
speeding, and 20% is due to distracted drivers (Fagnant and
Kockelman 2015). Thus, by installing alcohol locks, speed
controls (suppose, e.g., that speed limits will be wirelessly
transferred to the vehicles from the traffic operatives), and
technology to evaluate driver focus (see, e.g.,Sandle 2017;
Szeszko 2017), we could avoid, or at least reduce, most of
human errors. Thus, when comparing the safety of autono-
mous vehicles against the alternative of human-driven vehi-
cles, we need to compare accident rates for future human-
driven vehicles, not accidents rates using old technology.
Indeed, perhaps the most promising option may turn out to
be AI-assisted human-driven vehicles.23
However, although alcohol interlocks have been com-
mercially available for decades, no country has a policy of
requiring all new vehicles to be sold with alcohol interlocks.
Although a EU vehicle safety standards proposals from 2018
included a requirement that “All new vehicles sold in the EU
will feature a standardised interface to enable the fitment
of aftermarket alcohol interlock devices” (ETSC 2018),
we should recognize that efficient policies are not always
enacted. Therefore, autonomous vehicles may, all-things-
considered, turnout to be safer than AI-assisted human-
driven vehicles, even in a scenario where an AI-assisted
human-driven vehicle would be safer under the optimal
policy requirements. Conversely, Nyholm (2018b) argues
that “the introduction of self-driving cars might put some
pressure on people to either try to make their conventional
cars safer or switch over to self-driving cars instead” (p. 6).
So, autonomous vehicles may fast-track an improvement of
human-driven vehicles as well.
Lastly, we need to keep in mind that just as human-driven
vehicles can be improved by technology, autonomous vehi-
cles will also improve over time. For example, in the begin-
ning it may turn out that the safety of autonomous vehicles
are improved relative to human-driven vehicles (with or
without speed controls, alcohol locks, etc.) only in some
ways, which technological development may or may not
overcome in a slightly more distant near future. These issues
are to a large extent technical questions, but a technically
informed ethical analysis of how we should balance these
trade-offs is needed.
20 See, e.g., https ://en.wikip edia.org/wiki/Volks wagen _emiss ions_
scand al for an overview.
21 As pointed out to me in conversation by Sven Ove Hansson.
22 It may be illustrative to compare with the problems related to the
Boeing 737 MAX.
23 Requiring speed limiters and alcohol locks have previously been
defended (Smids 2018; Grill and Nihlén Fahlquist 2012).
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413AI & SOCIETY (2021) 36:405–415
1 3
When setting a safety-requirement for autonomous vehi-
cles, we need to consider what we should compare with.
But we also need to consider if we have a broader respon-
sibility to enact related policies for human-driven vehicles,
which may postpone the use of autonomous vehicles. This
may create complicated policy considerations, since such a
postponement may imply a slower implementation of the
technology. It is possible that a slower implementation of
autonomous vehicles over time would result in a large loss
of life, even if an earlier implementation of the technology
would result in a larger loss of life now. Arguably, this illus-
trates quite well why the safety argument is more impor-
tant than the ethics of crashing, because whether we should
broadly implement autonomous vehicles depends firstly on
consideration relating to the safety-criterion and safety con-
siderations of both autonomous and human-driven vehicles,
not the ethics of crashing.
4 Conclusion andsummation
In “Ethical crashing”, I argued that the focus on the issue of
ethical crashing is problematic for two reasons. First, there
are serious methodological challenges with the way that the
discussion is currently being performed, both in the philo-
sophical-ethical literature, in the empirical literature, and in
the public debate. Second, the debate relates and is second-
ary to the more important issues of safety requirements and
the safety argument.
In “The safety argument”, I turned to the safety argu-
ment to argue that there are lots of considerations that need
more attention from a policy perspective. We need to settle
the conceptual debate on what we mean by safety and how
broadly we should apply the concept. We also need to settle
the issue of what we should require from safety validation
and testing. Most importantly, we need to have a serious dis-
cussion about the justification of autonomous vehicles and
address the normative question on requirements of safety-
levels, and the—as I have argued—related issue of safety
policies for human-driven vehicles. All of which shows that
there is a lot to be done.
Acknowledgement Open access funding provided by Stockholm Uni-
versity. I want to thank two anonymous reviewers for AI & Society for
their helpful comments. I also gratefully acknowledge thatthis article
was written with funding from the Swedish Transport Administration
(Trafikverket), and that open access funding was provided by Stockholm
University.
Open Access This article is licensed under a Creative Commons Attri-
bution 4.0 International License, which permits use, sharing, adapta-
tion, distribution and reproduction in any medium or format, as long
as you give appropriate credit to the original author(s) and the source,
provide a link to the Creative Commons licence, and indicate if changes
were made. The images or other third party material in this article are
included in the article’s Creative Commons licence, unless indicated
otherwise in a credit line to the material. If material is not included in
the article’s Creative Commons licence and your intended use is not
permitted by statutory regulation or exceeds the permitted use, you will
need to obtain permission directly from the copyright holder. To view a
copy of this licence, visit http://creat iveco mmons .org/licen ses/by/4.0/.
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