Hard Drive Crash

• Author: Keri Grieman
• Position: Queen Mary University of London; The Canadian Internet Policy and Public Interest Clinic
• Pages: 294-309

2018

Keri Grieman

294

3

Hard Drive Crash

An Examination of Liability for Self-Driving Vehicles

by Keri Grieman*

© 2018 Keri Grieman

Everybody may disseminate this ar ticle by electronic m eans and make it available for downloa d under the terms and

conditions of the Digital P eer Publishing Licence (DPPL). A copy of the license text may be obtain ed at http://nbn-resolving.

de/urn:nbn:de:0009-dppl-v3-en8.

Recommended citation: Keri Gr ieman, Hard Drive Crash: An Examination o f Liability for Self-Driving Vehicles, 9 (2018) J IPITEC

294 para 1.

Keywords: Self-driving vehicles; self-driving cars; vehicular liability; autonomous vehicles; AVs; artificial

intelligence; public policy; AV liability; self-driving vehicle liability

Discussion then turns to how liability might be al-

tered prospectively in order to incentivize outcomes

beneficial to both consumers and creators from a

public policy perspective. This includes a proposal

of how such a proposal might be structured. Focal

points include public policy, social acceptance, and

potential incidental problems raised.

Abstract: This analysis considers the poten-

tial impacts of completely self-driving vehicles on ve-

hicular liability. This begins with examining how such

vehicles might be treated under an evolution of the

current liability system, and the potential results of

attributing liability to an operator, the vehicle itself,

different manufacturers, and a government entity.

A. Introduction

1

Preventative maintenance is a benecial concept

to many industries - the pre-emptive “repair” of

areas that will become problematic in the future. It

is, however, a concept that rarely impacts common

law jurisdictions, where stare decisis rules the day.

Law very seldom pro-actively regulates activities,

particularly those of emerging technologies - one

cannot regulate what does not exist. How could one

have imagined the adaptation of privacy laws before

everyone carried a recording device in their pocket?

Moreover, regulating pre-emptively can serve to

quash the very innovation they attempt to pave the

way for.

2 Yet there are exceptions to this inability to predict

change. Areas that subtly adjust the way that we

interact with our world rather than radically altering

them. These are changes that we can see coming and

can conceivably prepare for without discarding the

current system. The self-driving car is such an area:

the modern world is already equipped with roads,

stoplights, and fuel pumps. We are not attempting to

regulate in a new dimension, no ying cars have yet

emerged; but the imminent changes would benet

enormously from pre-emptive adaptation.

3

If frameworks of legal liability for self-driving

or autonomous vehicles (AVs) are held off, the

potential benets of the AVs will be stied. This is

not to say that they will not come, merely that they

may come agonizingly slowly, as shareholders limit

the monetary risks they are willing to take. Nor is it

suggested that the changes required are simple, but

that they are necessary. It is important to balance

proactivity with over-regulation, and the difculty of

post-ante regulation with administrative efciency.

4

Vehicular liability must be written to incorporate

AVs. A system that reects the underlying

differences between AVs and human drivers

encourages benecial change. In order to achieve

this change as efciently and cohesively as possible,

AV legislation should be written proactively, rather

than allowing the question of liability to bring change

incrementally and with crippling uncertainty. Such a

legislation system may be best complemented by the

creation of an independent public insurance entity.

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2018

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B. Assumptions

5 The central tenet of vehicular laws in many, if not

all, common law countries is fault. Who is liable,

when they are liable, and why. Rules of the road

are written to reect what one can and cannot do,

resulting in fault when one fails to follow them. For

this reason, analysis will focus on fully autonomous

vehicles - those that do not require a human driver

whatsoever, and taking those countries basing

liability on fault as a starting point. There are

recognized levels of autonomy within the industry:

from an entirely human driven vehicle at 0, to an

entirely human excluded one at 5.1 Where a human

driver is required or expected to maintain full or

partial control of the vehicle, regular conceptions

of liability are imperfect, but may be sufcient.

Partially autonomous vehicle components can be

turned off, as can components of full autonomy, such

as self-parking.2 Level four autonomous vehicles

are indeed fully automated, but are not capable of

covering every driving scenario,3 and have already

been rolled out in some areas - namely Las Vegas

4

and Singapore city centre,5 although they are limited

* Queen Mary University of London; The Canadian Internet

Policy and Public Interest Clinic.

1 Hope Reese, ‘Updated: Autonomous driving levels 0 to 5:

Understanding the Differences’ TechRepublic (20 Janaury

2016)

driving-levels-0-to-5-understanding-the-differences/>

accessed 12 January 2018.

2 ibid - breakdown of vehicular autonomy levels

0: The human driver is in complete control.

1: The human driver still holds the majority of control, but

a specic function, such as accelerating, may be done by the

vehicle.

2: “[A]t least one driver assistance system of both steering

and acceleration/deceleration using information about the

environment is automated, like cruise control and lane-

centering.” The driver may be incrementally separated

from the operation of the vehicle, but must remain ready to

re-take control in an instant.

3.’Safety-critical’ functions are taken by the vehicle. While

the driver must be able to intervene, they are not required

to re-acquire control instantaneously.

4. The vehicle is able to perform all necessary functions, but

not under all conditions.

5. The vehicle is able to perform all necessary functions

under all conditions considered safe enough for a human to

operate a vehicle.

3 Natasha Merat and others, ‘Driver behaviour when resuming

control from a highly automated vehicle’ (Institute for

Transport Studies, University of Leeds, 16 October 2014),

280.

4 Saqib Shah, ‘Las Vegas’ self-driving bus crashes in rst

hour of service’ Engadget UK (11 November 2017)

engadget.com/2017/11/09/las-vegas-self-driving-shuttle-

bus-crash/> accessed 12 January 2018.

5 Andrew J Hawkins, ‘Singapore’s self-driving cars can now

be hailed with a smartphone’ The Verge (22 September 2016)

to a dened environment. While these vehicles can

be used as independent taxis, it will be assumed that

they can currently be run under transit-like liability,

particularly given that their activity is currently

conned to a dened area. The scope of this paper

will primarily be concentrated on privately-owned

vehicles. It will also be generally assumed that

society is in favour of a system that allows for the

compensation of victims in vehicular accidents.

While no specic jurisdiction will be focused on,

Canada provides a helpful, broad set of examples

as it employs different insurance systems in each

province and territory, but uniformly bases liability

on fault.

6

Finally, exceptionally rigorous testing will be

assumed. In order to be allowed to enter the

market, relevant regulators should conduct

stringent testing under a variety of conditions for all

different manufacturers and models. Cars are heavy

machinery, and their destructive potential should

not be underestimated. While manufacturers will

undoubtedly conduct in-depth testing themselves,

an entity independent from the company needs to

test the vehicles in question to ensure a sufcient

level of safety and driving quality.

C. Technical Aspects

7 There are several typical elements that are used by

AV manufacturers order to allow the car to function.

These include a video camera mounted on or near

the front windshield allowing for the detection of

trafc lights and moving objects; a rotating sensor

on the roof which scans the area in a large radius,

creating a three-dimensional map; distance sensors

on the bumpers to measure space between various

obstacles; smart-navigation maps updating in real

time to track accidents, speed limits, and car-to-car

communication; and the articial intelligence that

commands the control centre.6 These methods are,

as yet, imperfect - sensors struggle with inclement

weather, and the roof sensor aka LIDAR (light

detection and ranging) faces problems with bright

sunlight. 7 The technology in the marketplace has not

driving-car-nutonomy-grab-ride-hail-test> accessed 12

January 2018.

6 Muhammad Amat, Dr Clemens Schumayer, ‘Self Driving

Cars: Future has already begun’ (Institute of Transport and

Logistics, Vienna University of Economics and Business, 7

May 2015)

dokumente/Downloads___Links/WS_IV_-_Azmat_

Schumayer_-_The_future_has_already_begun_.pdf>

accessed 1 January 2018, 8.

7 ibid 5; A fatal crash occurred where a Tesla detected bright

sunlight reecting off a truck as a cloud rather than an

obstacle. While the driver was supposed to be able to regain

control, and the Autopilot feature was not intended to

2018

Keri Grieman

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yet reached level 5, though when it does, accidents

are still to be expected. While it is possible and

indeed likely that adaptations and new technologies

will emerge, the aforementioned will serve as a

minimum level of AV “competency” – that full AVs

will have at least these levels of technology available

to them.

D. Public Policy

8

The changes brought about by AVs will impact

society in many ways, and not all of them will be

positive. Challenges may be obscure - a decrease in

car accidents has the potential to result in an even

greater shortage of organs available for donation.8

Impacts have been noted to range as far as the airline

and hotel industries, predicting that as long-distance

automobile transit becomes more convenient and

comfortable, air travel will become less competitive.9

More directly, automation will bring about the loss

of work for many, including professional drivers.

In 2014, it was reported that more than 4.4 million

persons in the United States alone worked as

drivers.10 While it may in turn bring new jobs, the

specics of such work remain to be seen. In terms

of hired driving services, some stakeholders are

already making their investments – in 2015 then

CEO of Uber, Travis Kalnick, stated the intention

to replace human drivers with AVs.11Undoubtedly,

there will be opposition to AVs for various reasons,

and astute commentators note that unions for

drivers will likely respond to the challenge to their

replace human senses, it does demonstrate the problem

for future vehicles and their sensors. Neal Boudette,

‘Tesla’s Self-Driving System Cleared in Deadly Crash’ New

York Times (New York, 19 January 2017)

com/2017/01/19/business/tesla-model-s-autopilot-

fatalcrash.html?_r=0> accessed 1 January 2018.

8 Ian Adams, Anne Hobson, ‘Self-Driving Cars Will Make

Organ Shortages Even Worse’ Future Tense (30 December

2016)

tense/2016/12/self_driving_cars_will_exacerbate_organ_

shortages.html> accessed 1 January 2018.

9 Kevin LaRoche, Robert Love, ‘Autonomous vehicles:

Revolutionizing Our World” Borden Ladner Gervais LLP

(2016) < blg.com/en/News-And-Publications/Documents/

Autonomous-Vehicles2016.pdf> accessed 18 June 2017,

citing ‘Autonomous cars will make domestic ights run

for the money: Audi’ Telematics Wire (27 November 2015)

<telematicswire.net/autonomouscars-will-make-domestic-

ights-run-for-themoney-audi/> accessed 18 June 2017.

10 Mark Fahey, ’Driverless cars will kill the most jobs in

select US states’ CNBC (2 September 2016)

com/2016/09/02/driverless-cars-will-kill-the-most-jobs-

in-select-us-states.html> accessed 4 January 2017.

11 Stephen Edelstein, ‘Uber CEO to Tesla: Sell me half a million

autonomous electric cars in 2020’ Green Car Reports (7 July

2015)

to-tesla-sell-me-halfa-million-autonomous-electric-cars-

in-2020> accessed 18 June 2018.

profession by raising doubt about AV safety, and

lobbying against them.12 AVs are likely to face more

opposition than most changes, given that humans

appear to have an inherent distrust of non-human

intelligence. While evidence-based algorithms are

shown to be more accurate than humans, people

lose condence in the algorithm more quickly than

humans, and continue to prefer the human even

where the algorithm consistently outperforms the

human.13

9 AVs are indisputably on their way. Both traditional

and disruptive automakers are steering into the skid

of AVs – even by 2012, Google’s AV had completed

over 300,000 miles of accident-free self-driving.14 AVs

are already in commercial use: AV trucks transport

mining materials in the Australian outback, and self-

driving tractors are already in the eld.15 Moreover,

autopilot systems have been used by commercial jets

for many years, aiding in maneuvering, navigation,

and landing, lending “a signicant amount of

automated assistance,” and allowing planes to land

in conditions that were “previously difcult for

human pilots.”16

10

Accident reduction is a crucial potential benet.

While AVs are imperfect, they lack the failings

endemic to human drivers: limited scope of vision;

ability to be distracted; inability to focus on multiple

areas at once; etc. Even more importantly, they lack

the ability to be affected by the same level of choice

as humans in terms of driving; namely, humans can

and do drive when tired, ill, or impaired. Advocates

of AV note that more than 90% of trafc collisions are

caused by human error17 - while AVs are imperfect,

12 Ratan Hudda and others, ‘Self Driving Cars’ (Fung Institute

for Engineering Leadership, UC Berkeley College of

Engineering, 29 May 2013)

wordpress.com/2013/06/self_driving_cars.pdf> accessed

1 January 2018, 9.

13 Berkeley J Dietvorst, Joseph P Simmons, Cade Massey,

‘Algorithm Aversion: People Erroneously Avoid Algorithms

after Seeing Them Err’ (July 2014) Journal of Experimental

Psychology: General, forthcoming

com/sol3/papers.cfm?abstract_id=2466040> accessed

2 January 2018.

14 ibid.

15 David Robson, ‘The Truth About Driverless Vehicles’

BBC (London, 13 October 2014)

story/20141013-convoys-of-huge-zombie-truck> accessed

30 December 2017.

16 Harry Surden, Mary-Anne Williams, ‘Technological

Opacity, Predictability, and Self-Driving Cars’ (2016)

Cardozo Law Review 38:121

edu/articles/24> accessed 30 December 2018, citing Simon

Wood, ‘Flight Crew Reliance on Automation’ Federal

Aviation Administration Advanced Avionics Handbook

(UK Civil Aviation Authority, 2009)

docs/33/2004_10.PDF.157>

17 Emily Chung, ‘Autonomous cars could save Canadians $65B

a year’ CBC News (Toronto, 21 January 2015)

news/technology/autonomous-cars-could-save-canadians-

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3

they will not eliminate these accidents immediately,

but they have the potential to greatly reduce such

accidents. Currently, the World Health Organization

estimates that injuries caused by road trafc will

become the worldwide fth leading cause of death

by 2030.

18

In the United States, automobile accidents

are “the lead cause of death for people between the

ages of 3 and 34,” with a death every 30 seconds.19

It is estimated that in the United States alone, AVs

could save 300,000 lives per decade - 29,447 lives per

year,20 and as much as $190 billion USD per year in

health costs.21 However desirable these miraculous

predictions, they depend on a minimum level of

widespread adoption of AVs.22

11

Infrastructure efciency and cost will be directly

impacted. Worldwide, the cost of trafc congestion

is estimated to reach $2,200 billion USD per year.

23

In

northern North America, self-driving cars have been

predicted to save $65 billion CAD by reducing trafc

congestion, fuel costs, and “time wasted behind the

wheel.”24 In reducing the need for car ownership,

$5 billion CAD can be saved on congestion costs

alone.

25

Google has already built the largest trafc

jam surveillance network in the world by providing

over 500 million smart phones with an operating

system - the mapping function allows Google to

track trends over time.

26

Independent researchers

have modelled an algorithm that allows signicant

alleviation of trafc jams by multi-vehicle routing,

and requires only 10% of vehicles on the road to

follow the algorithm.

27

In other words, benets of

AVs need not reach a majority before they produce

tangible infrastructure benets - only a minimum

point of saturation.

65b-a-year-1.2926795> accessed 1 January 2018; Berkeley

J Dietvorst, Joseph P Simmons, Cade Massey, ‘Algorithm

Aversion: People Erroneously Avoid Algorithms after Seeing

Them Err’ (n 13) 6.

18 Ratan Hudda and others, ‘Self Driving Cars’ (n 12) 5-6.

19 ibid 0.

20 Adrienne Lafrance, ’Self-Driving Cars Could Save 300,000

Lives Per Decade in America’ The Atlantic (29 September 2015)

<www.theatlantic.com/technology/archive/2015/09/

self-driving-cars-could-save-300000-lives-per-decade-in-

america/407956/> accessed 1 January 2018.

21 ibid.

22 ibid.

23 Hongliang Guo and others, ‘Routing Multiple Vehicles

Cooperatively: Minimizing Road Network Breakdown

Probability,’ (2017) 1/2 IEEE Transactions on Emerging Topics

in Computational Intelligence.

24 Vijay Gill and others, ’Automated Vehicles: The Coming

of the Next Disruptive Technology’ The Conference Board

of Canada (21 January 2015)

library/abstract.aspx?did=6744> accessed 1 January 2018.

25 Emily Chung, ‘Autonomous cars could save Canadians $65B

a year’ (n 18) 1.

26 Ratan Hudda and others, ‘Self Driving Cars’ (n 12).

27 Honglian Guo and others, ‘Routing Multiple Vehicles

Cooperatively’ (n 23) 121.

12 In terms of accessibility, AVs would open an entire

world to those unable to drive themselves. Many

individuals are, for reasons of age, physical ability,

or current state, unable to drive. These individuals

are dependent on either public transit, expensive

private means of transport, such as taxis, or family

and friends. It has been noted that allowing these

individuals increased access to transportation has

the potential to increase total vehicle transit by up

to 11%.28 While this obviously increases demand, it is

cause for celebration as these individuals evidently

do not have the freedom or capability to travel as

much as their able-bodied counterparts.

13

Ecologically, there are also several benets. Even

with the current state of technology that is expected

to improve, projections have placed the reduction

of oil consumption and related greenhouse gas

emissions at 2 to 4%.29 These predictions were

based on the use of technologies such as “adaptive

cruise control, eco-navigation, and wireless

communications.”30 The ease of incorporating AVs

with other technologies has even greater potential,

with “car to infrastructure communication”31 - one

“smart” parking system reduced time spent looking

for spaces by 21%.

32

Additionally, AVs do not need to

park in a space that is convenient or easily accessible

– they can park underground or remotely, and the

driver can summon the car when required. A trafc

signal synchronization program saved “31.2 million

hours of travel time, 38 million gallons of fuel and

337,000 metric tonnes of carbon dioxide per year.”33

Furthermore, most cars are unused for 95% of their

lifespan, but AVs have the potential to reduce the

amount of cars on the roads overall, as AVs can

be farmed out for others when not in use by the

owner.34 Car sharing programs have led to less car

ownership, and a reduction of emissions in cities,35

28 Todd Litman, ‘Autonomous Vehicle Implementation

Predictions: Implications for Transport Planning’ Victoria

Transport Policy Institute (22 December 2017)

www.vtpi.org/avip.pdf> accessed 1 January 2018 13; citing

Michael Sivak, Brandon Schoettle, ‘Road Safety With Self-

Driving Vehicles: General Limitations And Road Sharing

With Conventional Vehicles, Sustainable Worldwide

Transportation Program’ University of Michigan (2015)

.

29 Julia Pyper, ‘Self-Driving cars Could Cut Greenhouse

Gas Pollution’ (15 September 2014) Scientic American

cars-could-cut-greenhouse-gas-pollution/> accessed

1 January 2018.

30 ibid.

31 Muhammad Amat, Dr Clemens Schumayer, ‘Self Driving

Cars: Future has already begun’ (n 6) 13.

32 ibid.

33 ibid.

34 Ratan Hudda and others, ‘Self Driving Cars’ (n 12).

35 Darrell Etherington, ‘Car sharing leads to reduced car

ownership and emissions in cities, study nds’ Tech Crunch

(19 July 2016)

2018

Keri Grieman

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a benet which is likely to increase as it cuts into

the requirements for taxis and other chauffeuring

needs. Finally, there is the simplest benet of all:

not having to drive.

14

Whilst the advantages are numerous, the technology

remains vulnerable to smothering by the tyranny

of the immediate - the defence of the bottom line

in companies protecting themselves from liability,

and legislation in taking a “wait and see” approach.

E. Liability

15

For all the many benets of AVs, they are imperfect.

Accidents will still happen, particularly in the early

years. It is thus important to determine what party

is potentially liable; specically, who should pay for

any damages incurred as well as compensation to

the victim. While current liability systems will need

to be tweaked to allow for integrated AI driving; i.e.

for vehicles between levels 1 and 5, their setup still

allows for and generally requires a human driver

to take control. In aiming to fully achieve their full

societal benet, level 5 vehicles should have no

interaction from the driver. This raises the obvious

question as to who should be liable and how.

16

Informed commentators have suggested that

parties potentially liable for AV accidents could

include the user, the owner, the manufacturer, the

manufacturer of AV components, or a government

entity. Methods such as product liability have the

potential to cause difculties both in the expense

incurred through the legal process in determining

liability, and in determining how and why an AV

made the “decision” that it did; class action suits

are too cumbersome for something as ordinary as

auto accidents. What mechanism, therefore, should

be used to allocate liability? As will be examined,

negligence under our current conception of the

notion, has the potential to prove problematic in

allocation of liability.

I. Potential Liability Allocated

to the Operator

17

Given that one of the potential benets of AVs is

increased car-sharing, it is possible that the user and

owner may be different individuals. The user might

simply be someone who has independently hired the

car through, say, a taxi service app. The owner is the

person who has technical ownership of the AV. For

the purposes of legal application, one can treat the

sharing-leads-to-reduced-car-ownership-and-emissions-

in-cities-study-nds/> accessed 1 January 2018.

user, owner, or general occupant as one entity, as

they run into the same potential concerns. For the

sake of discussion, these entities can be rened into

one, the “operator” – the denition of which should

rely on the individual determining the destination.

18

In a liability context, the operator is the entity

who is most closely aligned with current fault

attribution. While each country differs slightly

in their application of the law, vehicular liability

generally relies on the individual who has control

of the vehicle. In Canada, for example, section 214

of the Canadian Criminal Code states that to operate

“means, in respect of a motor vehicle, to drive the

vehicle.”36 Crimes such as operating under the

inuence rely on this denition of operation, and

on the concept of “the care or control of a motor

vehicle… whether it is in motion or not.”

37

“Care and

control” has included situations such as a passenger

grabbing the steering wheel,38 sitting in the driver’s

seat “braking and steering an inoperable vehicle,”39

or using the steering wheel while being towed, as

noted by Osler PJ in R v Morton:40

when, though the means of propulsion is under the control

of the driver of a towing vehicle, there is a person in charge

of the towed vehicle who is manipulating the steering wheel

and brakes and exercising a signicant measure of control

over the direction and movement of that vehicle, I consider

that person can be said to be operating or driving the motor

vehicle.

19 In other words, determining liability of an operator

has centred around their intent and ability to

inuence the movement of a vehicle through

functions in the province of a driver. Many

jurisdictions are willing to nd drivers liable for

driving under the inuence of intoxicants even if

they were not in the driver’s seat, nor piloting the car,

but were in the car and had access to the keys. The

capacity to direct the car, whether or not in current

use, has been used to determine care and control,

and thus liability. This approach does not make sense

for AVs. The intent of a fully autonomous vehicle is

that the occupant will not have control, and thus will

not be able to direct the specic movements of the

vehicle. The occupant may have the ability to direct

the car generally - they are, after all, determining

the end destination of the AV. However, “care and

control” does not make this distinction.

36 ‘An Act respecting the criminal law’ (RSC 1985) c C-46,

‘Canadian Criminal Code’ section 214.

37 ibid section 2(a).

38 R v Belanger, [1970] 10 CRNS 373 (SCC).

39 R v Flemming [1980], 43 NSR (2d) 249 (NS Co Ct), cited in R v

Danji, [2005] ONCJ 70, 16 18 MVR (5th) 1.

40 R v Morton [1970], 12 CRNS 76 (BCPC).

Hard Drive Crash

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3

20 An operator of an AV is analogous to the passenger

of a bus. They have an ultimate destination in mind.

They are capable of inuencing the vehicle’s path

by asking the driver to stop, potentially by pushing

a button or pulling a cord. If the bus were to be in

an accident, however, even if the bus is inarguably

at fault, the bus passenger is in no way responsible

for that accident or the damages resulting from it.

Operators of an AV have no less a duty of care to the

occupants of other vehicle than an ordinary human

driver does, but attempting to extend liability does

not, from a logical standpoint, make sense. The duty

of care may include not interfering with drivers,

or distracting them, but should not overextend to

include the “but for” test – i.e. “but for” the occupant

choosing to use the AV, the accident would not have

occurred. This is simply too broad to be functional.

21

In the case of an accident between an AV and a

human driver, the legal result would depend on

which vehicle were at fault. If the AV were at fault,

the previous issue arises: the occupant is unlikely

to have acted negligently or unreasonably. If the

human driver were at fault, all current laws are

easily applicable. If fault is mixed, the court can

apportion damage based on contribution to the

harm, as is common in many areas of law, but the

AV portion should not fall on the operator.

22

There are cases where traditional conceptions of

liability should apply, namely where the operator

had previous knowledge of a potential issue with the

AV. AVs have the potential to learn, and better their

“driving”. This is a desirable feature of AVs - not

only can AVs learn from their own behaviour, but

potentially the behaviour of other vehicles capable

of communicating with them. Such a system is likely

to function on an update system, similar to updates

on a computer or smart phone. This could result in

a situation where an operator, or an owner, were

confronted with a notice warning them of a defect

with the car’s programming, or a potential update.

If the operator were to ignore this warning and

continue to use the vehicle, they can and should be

found liable for an accident resulting from the lapse

in update. This may be an extreme outlier scenario

but serves to sufciently include the operator’s

negligence.

23 Additionally, if an individual - whether owner, user,

operator, or unrelated party - were found to have

tampered with any programming impacting the AV’s

ability to function safely, this could produce a range

of liability. This range should run from negligence

to attempted murder, depending on what happened

and how, such as if it was intended to affect another

operator. This does limit the operator’s freedom to

adjust their vehicle’s programming as they would

like, but such a step is crucial to the uniformity and

thus predictability of AVs - a necessary requirement

for ensuring the safety potential of the vehicle.

24

Despite these minor exceptions, conceptions of

liability surrounding the vehicle’s operator must be

updated to reect the reality that the operator does

not, and should not, affect the “decision making” of

the vehicle. This is the societally desirable outcome

- removing the operator from the second-to-second

decision making process is what allows the AV to

drive in a way that avoids human failings. In the same

way that a taxi passenger has made a responsible

decision and thus should not be charged with driving

under the inuence, neither should an AV operator

be at the mercy of decisions which are not their own.

II. Potential Liability Allocated

to the AV Itself

25 The AV itself is not a logical successor to the human

driver in terms of liability, although it may at

rst glance appear to be so. The entity that best

ts current conceptions of liability in terms of

“driving” and “care and control” of the vehicle is the

articial intelligence entity that enables the AV. For

simplicity’s sake, the AI and the AV will be treated

as a singular entity given their inseparability for the

purpose in question.

26

Determining whether the AV has made a “wrong”

decision may require extensive evaluation of the

way in which it makes decisions. It may require a

comprehensive understanding of how decisions

were made, and what information was available.

Requiring the AV to take on responsibility for actions

taken implies a level of responsibility. However,

there are two problems with this: rst, from a

functional standpoint, the AV has no assets except,

potentially, itself. In an accident, the victim is to be

compensated for damage to the vehicle, injury, etc.

However, without delving into an analysis of robo-

slavery, it is clear that an AV does not own anything,

whether or not it owns itself. Accordingly, whether

or not the AV owns itself, depriving the owner of the

AV is detrimental to the owner, rather than the AV.41

Second, the AV’s decision making originally depends

on how it was programmed. While it may “learn”,

its key input is given before it ever hits the road

41 Interestingly, a robot has been already been ‘arrested’ for

its actions. A robot in Switzerland was created by a group

of artists and given a bitcoin budget per week to randomly

purchase from the dark web, with the intention of

displaying the items purchased. The robot was conscated

along with its purchases, which included a passport and

ecstasy tablets, but was returned three months later with

all purchases except the Ecstasy. Arjun Kharpal, ‘Robot with

$100 bitcoin buys drugs, gets arrested’ CBC Tech Transformers

(Ottawa, 22 April 2015)

with-100-bitcoin-buys-drugs-gets-arrested.html> accessed

1 January 2018.

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- it does not inherently “choose” to do something

wrong, it follows directions that it has been given.

This is not the sort of “guilty mind” or mens rea

envisioned by current legal regimes. Moreover,

this approach to liability would assume that the

AV both can and does “think” like a human, and

thus could be assessed to the same standards. Even

the ways that the AV “learns”, or what it “learns”

about, are initiated by its programming, and are not

inherently based on human thought patterns. The

AV’s “decisions” are not the same as human ones.

This evokes the question of whether the party that

originally programmed the AV should be liable for

what the AV is programmed to do.

III. Potential Liability Allocated

to the Manufacturer - parts

27

Manufacturing can be separated into two parts:

the main manufacturer or assembler, and parts

manufacturers. Consider rst the parts themselves.

Continuing to treat parts manufacturers under

traditional common law liability understandings

does not seem particularly problematic - main

manufacturers maintain the duty to check parts

they buy to a reasonable standard, and the parts

manufacturers maintains the duty to manufacture

them to the standard promised. Individual parts

currently account for relatively few accidents, and

there is no reason to believe the relatively low rate of

product liability suits or issues would increase. While

machinery for AVs may be more complicated, even

vehicles that are not fully autonomous are improving

at tasks like diagnosing parts or physical issues with

vehicles. While product liability suits are slow and

costly, the relatively small-scale requirements for

individual faulty parts means that this is likely still

a functional way to address the problem without a

systemic overhaul.

IV. Potential Liability Allocated to the

Manufacturer - programming

28

First, some denitional clarication. It has been

suggested that Google is likely to license a developed

version of its AV software to car manufacturers,

allowing for a prospective licensing industry

alongside the AV market.42 However, given that

Google has a successful AV of its own, and major

automotive manufacturers are creating their own

AVs, discussion will focus around manufacturers as

having produced and programmed their own AVs.

42 Ratan Hudda and others, ‘Self Driving Cars’ (n 12).

29

In the current state of the market, most

manufacturers selling vehicles that have AV features

state that the driver must be able to take control at

all times, and that any autonomous features are not

in fact self-driving; thus by using the vehicle, the

“driver” conrms that they will always be “driving”,

even if the car is able to function in any way on its

own. This appears to be an attempt to potentially

contract out of liability in favour of having the driver

agree to assume it. Whether or not this will hold up

to substantial legal challenges remains to be seen,

either in tort liability or contractual restrictions,

but it nonetheless appears to be the current method

attempted. The current state of the liability union

is divided - automotive companies and Google have

lobbied governments to absolve them of liability -

to negative effect in California, but positive effect

in Nevada.43 Volvo has already made public its

willingness to accept full liability, whereas Tesla

has stated that it will accept liability only for design

failure.44

30

Whether liability should fall on major manufacturers

through the decisions made by their agents in

programming an AV, and on the subsequent decisions

of the AVs acting on that programming, opens an

obvious chain of questioning. While removed from

the immediacy of the road, programing largely

ts the conception of “control” over the vehicle -

how it is driven, when and where it stops, how to

react to changes in the environment. Programmers

for manufacturers, acting in their professional

capacity, could be treated as creating liability for

the manufacturer in embedding their decisions, even

if it is an initial step in a machine learning process.

This is compounded by the “black box” problem - it is

often difcult for articial intelligence to “explain”

why it did what it did - the AV in question might

have weighed many factors, and learned from many

sources, which ultimately resulted in a particular

action. Elon Musk, co-founder, CEO and Product

Architect at Tesla, used the following analogy:

Point of views on autonomous cars are much like being stuck

in an elevator in a building. Does the Otis [Elevator Company]

take responsibility for all elevators around the world, no they

don’t.45

31

This presents an interesting point. Programming the

way something works has not previously resulted

in major liability. Nor has it prevented society from

doing away with elevator attendants, or in the case

of cars, drivers. However, not only do the number of

43 Ratan Hudda and others, ‘Self Driving Cars’ (n 12) 6.

44 Danielle Muoio, ‘Elon Musk: Tesla not liable for driverless

car crashes unless it’s design related’ Business Insider

(Sydney, 20 October 2016)

elon-musk-tesla-liable-driverless-car-crashes-2016-10>

accessed 1 January 2018.

45 ibid.

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elevator accidents pale in comparison to the number

of car accidents - even proportionately - the elevator

deals with a pre-set course with no obstacles or other

players, programmed or otherwise. Cars must deal

with a great deal more and put more lives at risk than

just those inside of it, and there is an inherent level

of “decision making” involved.

32 Priorities for the AV are set in advance. This often

brings to mind philosophical debates such as the

trolley problem, wherein one must choose whether

to divert a trolley hitting three people instead of

hitting one person. However, problems like this

do not address what AVs are, or are intended to

do: AVs are not intended to make a choice of the

amount of humans tied to the train track to kill.

They are intended to stop the trolley. Treating AV

“decisions” as identical to human ones ignores the

reality that AVs can work with far more input than

humans can: 360 degrees of vision, multiple heights

and layers of sensors, and a lack of distractions.

If AVs can communicate with one another, and

there are enough to do so, they could provide

information in real time; for example, “up-coming

pothole” and “group of joggers on road shoulder”

are not particularly difcult messages to transmit.

This translates into larger concepts as well, such as

“human-sized entity darting into trafc”. The world

is not tied to two tracks and no breaks, and reducing

the decisions to be made to such a scenario fetters

our understanding of what could be.

33

Statutorily pinning liability on manufacturers forces

them to prioritize liability. This does not mean that

manufacturers would place it as a rst priority -

human life is likely to forever hold the primary spot,

if only because cases of deaths may kill public favour

of AVs. But it does inevitably affect priorities as a

whole. Damage to property is certainly preferable

to damage to humans, yet focusing on liability may

shift this emphasis. It is entirely possible to be both

in the right legally, yet making the wrong decision.

While measures such as the strict liability approach

of capping the amount of damages to be paid may

be reasonable stopgaps, they present their own

domino issues - potentially neither covering the full

amount of damages, nor removing the incentive to

de-prioritize physical damage in favour of safety.

34

Consider a situation wherein an AV is suddenly

faced with an obstacle it can either hit lightly,

causing no injury, or stop immediately and cause

the human car speeding behind it to injure either

the AV and the speeding car’s occupants. In a case

where liability is not in question, and human safety

is the highest priority, the AV hits the obstacle -

damaging the AV, but neither set of passengers.

If liability is a priority, the AV avoids liability by

coming to a stop as the human driver would be in the

wrong through speeding, and being unable to make

a safe stop without hitting the AV. However, this is

not the societally desirable outcome: car parts are

replaceable, human health is not. It is quite possible

to be correct in law but not in morality, and the

concern for liability means the prioritization of cost

and correctness over better outcomes. Mandating

liability means incentivizing the wrong priorities.

As for the trolley problem, we want the AV to stop

the trolley, even if it means breaking said trolley.

35

While instances such as negligent or malevolent

programming should still be considered, from a

public policy perspective, governments should

encourage manufacturers to take safety of all parties

as the highest consideration. As AVs reach a point of

saturation, these priorities will have an increasing

impact and importance. Statutorily mandated

liability on manufacturers does not make vehicles

safer in and of themselves - it reinforces the priority

of doing the legally correct action, rather than the

socially benecial one. Allocation of liability for non-

human damage simply does not produce the best

incentivized outcome for social priorities.

36

Furthermore, if liability is focused on manufacturers,

risk is concentrated onto a concerningly small

number of entities, who will simply increase product

prices to cover the risk at an even greater rate

considering the unknown cost to the manufacturer

themselves. The current system of liability and

spreading liability cost, transfers the price to a

later point in the transaction, but allows for greater

predictability and a greater sharing of the smaller,

more predictable cost.

V. Potential Liability Allocated

to a Government Entity

37

Ultimately, insurance will still be necessary for AVs.

There will be accidents, and thus accident victims. An

insurance infrastructure will ensure compensation

for these victims and help to establish the viability

of AVs as an institution. As previously discussed,

naming one or a combination of the previous actors

and stakeholders presents many problems. Liability

needs to be apportioned without a concept of “blame”

- damage has occurred, and the damage needs to be

xed or compensated for. A strict liability regime is

a functional way to accomplish this and legislating it

pre-emptively for level ve AVs has the signicant

benet of predictability.

38

AV manufacturers are understandably concerned

with the extent to which they will be liable, and in

what ways. Companies have been easing slowly into

full automation by using automated features, being

careful to mandate that the driver must still be in

control - thus avoiding liability. A “wait and see”

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Keri Grieman

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approach to legislation means that manufacturers are

understandably hesitant to be the rst to throw their

hat into the ring with commercial, fully automated

vehicles. It also means that smaller companies are

struck from the automation race completely, as they

lack the war chest to fund costly litigation when

an accident occurs. Providing assurances allows

manufacturers to bring an actual product to market

- the societally desirable, completely hands-off, AVs.

39

How, then, should this system be structured? Ideally,

at least initially, as a government-run, AV-mandated

single-pool insurance entity through which all AVs

must be insured. First, such an entity has the initial

benet of actually providing insurance rather than

waiting for the private sector to enter the market.

Second, time and proteering can be avoided by

circumventing the private insurance sector. Third,

it allows for a specialized entity to deal with the

information created by accidents; specically,

assessing it, and passing it along to the necessary

parties, such as the manufacturer, when there is a

clear problem with the AV system. Fourth, it allows

for the reduction of administrative work - no time

and effort is spent resolving damages between AV

insurance providers; rather the costs are simply paid

and the accident can be analyzed from a systemic

perspective, i.e. could the AV have made a better

“decision”? While non-AV insurance providers

will still have dealings between themselves, they

too benet from a single-system for AVs, such as

a standardized system that specializes in how AVs

function, and can thus concentrate on, for example,

provision of crash footage in the case of a combined

AV/human accident. This is not to say that AVs

should suddenly become liable for all accidents they

are involved in, but rather those where a human

driver would similarly be found at fault.

1. Avoiding the private insurance sector

40

Single pool compensation has been employed in

other areas to good effect. One example is New

Zealand’s ACC, a crown-corporation accidental injury

insurance board. The fund is paid into by everyone in

New Zealand who “works and owns a business,” and

through levies on vehicle licensing payment and car

fuel lling.46 The levies provide a fund that pays out

in cases involving accidental injury. This coverage

applies to everyone in New Zealand, regardless of

age or employment status, and even includes visitors

to New Zealand.47 While there are various incentives

implemented, such as a slight discount on levies for

46 Accidental Compensation Corporation ‘What we do’ (2018)

do/> accessed 1 January 2018.

47 ibid.

companies with excellent workplace injury rates, the

overall structure is a no-fault, community approach

to accidents.

41

Outside of accident insurance, single-pool or

single-payer insurance has been most visible in

the healthcare sector. The United States is a noted

hold-out against such a system, and spends “more

than twice as much on health care as the average of

other developed nations, all of which boast universal

coverage … [while] more than 41 million Americans

have no health insurance [and] [m]any more are

underinsured.”48 In 2003 experts estimated49 that

converting the United States would “save at least

$200 billion [USD] annually (more than enough

to cover all of the uninsured) by eliminating the

high overhead and prots of the private, investor-

owned insurance industry and reducing spending

for marketing and satellite services.”50 From a purely

logical perspective, this makes sense - an industry

run for prot is intended to make a prot, and must

do so by either over-charging or under-providing. It

is not intended to be a zero-sum game that provides

the greatest amount of care at the lowest cost, it is

intended to create a gap between what is paid by the

insured, and what is paid to the provider. Without

this gap, there is no prot. In addition to this, money

is spent on advertising for the insurance company,

ghting claims both from providers and the insured,

and “avoiding unprotable patients.”

51

While it is

often argued that a private insurance market allows

individuals to suit coverage to suit their needs, this

inherently provides a problem for “unprotable

patients.”52

42

Returning to automotive insurance, Canada provides

an interesting comparison as some provinces have

mandated government insurance, whilst others have

not. British Columbia, Manitoba, and Saskatchewan

all have a “one-stop shop” approach to insurance,

but differ in their exact coverage, and Quebec

drivers all have personal injury insured through

the government, while private insurers cover the

rest.

53

Direct cost comparisons are difcult, as the

provinces have different challenges; for example,

more extreme weather in central Canada, and a

48 S Woolhandler and others, ‘Proposal of the Physicians’

Working Group for Single-Payer National Insurance’

(1 August 2003) Journal of the American Medical Association

290/6 798

49 ie, before both the roll-out and subsequent roll-in of

‘Obamacare’.

50 S Woolhandler and others, ‘Proposal of the Physicians’

Working Group for Single-Payer National Insurance’ (n 48).

51 ibid.

52 ibid.

53 Karen Aho, ‘When the government sells car insurance’

Nasdaq (25 March 2013)

when-the-government-sells-car-insurance-cm230568>

accessed 12 January 2018.

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greater amount of drivers in British Columbia,

Ontario, and Quebec. However, one study compared

the same city - one which straddled a government

insurance and a private insured province - and found

that those with government coverage paid less.54

Additionally, net income from public insurance, at

least in British Columbia, goes into reserves, rather

than exiting the system as a shareholder dividend.55

It has also been suggested that high costs are the

reason for the difference in percentage of uninsured.

For example, in 2002 Ontario (the province with the

highest average insurance rates) had an estimated

10-20% of drivers uninsured, whereas British

Columbia had 0.26% uninsured drivers.56

43 These arguments are not intended to frame the free

market as inherently negative or bad. What this

system aims to accomplish is to set aside, at least

temporarily, the prot of the insurance sector to

pave the way for the AV sector, for the simple reason

that AVs offer more direct societal benets.

2. No-fault

44

No-fault insurance is not a new concept to the

automotive world. Policy-holders and passengers

are reimbursed for accidents and damage through

their own insurer, rather than tort insurance, where

fault is assigned to a party. No-fault insurance

usually only covers up to a particular sum and

precludes individuals from pursuing the other party

in court. Unfortunately, it does not typically mean

the absence of attribution of fault, rather that the

insurance company or companies will determine

between them which party is at fault, and potentially

increase that party’s future insurance rates.57 Fault

can be attributed by percentage, wherein both

parties may see future increases in rates.58

45

As previously discussed, the attribution of actual

fault is difcult in scenarios solely involving AVs,

given the difculty of deconstructing the decision-

making process. What the future of AVs require is to

give up the concept of fault in actuality rather than

in name. This is not an easy thing to do - not only are

the rules of the road set up to determine fault, but

humans like blame, and we do not trust intelligences

we don’t understand. This is true even if the non-

54 ibid.

55 Lucy Lazarony, ‘Public vs. private auto insurance’ Bankrate

(22 July 2002)

private-auto-insurance/> accessed 12 January 2018.

56 ibid.

57 ‘No-Fault Insurance: What it Really Means to You’ Insurance

Hotline (11 November 2011)

com/no-fault-insurance-what-it-really-means-to-you/>

accessed 1 January 2018.

58 ibid.

human intelligence is demonstrably better at the

task at hand.59 In essence, giving up fault is a leap of

faith: it requires letting go of the idea of an “intuitive

and automatic” desire to conceive of blame.60 It

is, however, a necessary step to improvement -

the move to acknowledgement of an undesirable

consequence rather than the attribution of the

individual entity responsible. There will be a period

of time where fault will still be apportioned, for

example, where accidents have occurred between

AVs and human drivers. This is necessary in order

to allow for incremental integration of AVs, rather

than wholesale substitution. However, AVs will

inherently make fault determination between AV

and non-AVs easier to determine, as AVs can provide

their own surveillance footage.

46 Moreover, as Reed et al. point out, common law is

not unfamiliar with strict liability for inherently

dangerous activities, such as the keeping of

dangerous animals, or ownership and use of

aircraft.61 These difculties have not quashed

either activity but serve to account for the dangers

inherent to them. Strict liability tends to invoke the

opposite conception of no-fault, as it incurs fault no

matter how careful or reasonable the activities of

the individual in question - “the person responsible

is required to indemnify the remainder of society.”

62

However, the result and acknowledgement are the

same: accidents do occur, and must be accounted for,

no matter the reasonability of the actors in question.

3. Structure

47

Even given a singular pool insurance provider, there

are many potential iterations of how insurance may

be structured. It is not unreasonable to leave the

consumer with a regular insurance cost that covers

damage - there is no indication that the cost involved

will be higher than a human driver, particularly

59 Berkeley J Dietvorst, Joseph P Simmons, Cade Massey,

‘Algorithm Aversion: People Erroneously Avoid Algorithms

after Seeing Them Err’ (n 13). Rather than debating what

truly constitutes intelligence, non-human intelligence

should be understood here as the computation behind the

AV’s decision making.

60 Janice Nadler, Mary-Hunter McDonnell ‘Moral Character,

Motive, and the Psychology of Blame’ (2012) 97/255 Cornell

Law Review

viewcontent.cgi?referer=https://www.google.ca/&httpsre

dir=1&article=3290&context=clr> accessed 11 January 2018

257

61 Chris Reed, Elizabeth Kennedy, Sara Noguiera Silva

‘Responsibility, Autonomy and Accountability: legal

liability for machine learning’ (Third Annual Symposium

for the Microsoft Cloud Computing Research Centre, 8-9

September 2016)

accessed 11 January 2018, 5.

62 ibid.

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Keri Grieman

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3

given that Google cars drove 1.3 million miles in

seven years before causing an accident.63 Reed et

al. suggest that the identiable party to insure is,

pragmatically, the keeper of the vehicle, and that this

allocation follows the precedent of aircraft owners,

where it has invoked no serious problems.64 This has

the additional benet of not disrupting the current

vehicular liability requirements, as vehicles must

already be insured by their owners.65 The amount to

be paid for insurance can initially reect the average

rates for their human driver counterparts, but

should not involve typical factoring characteristics

such as the driving record, where the owner lives,

driving experience, age, gender, or vehicle type. The

aim of the AV is to make these irrelevant, and to

exclude bias when pricing the coverage.66 It should

also provide an initial overhead for damage coverage

as the potential damage-reduction possibilities of

the AV bear fruit.

48

However, there should be another sector of

contribution to the singular fund - a per-car entry

cost from the manufacturer. While the initial amount

will be arbitrary, what the amount should eventually

reect is injury and related costs compensation for

AV accidents. This amount will require buffering

before a minimum level of saturation for AVs, as if

there is only one AV on the road which causes an

accident worth an accident pay-out of three million

dollars, this is unreasonably punitive. However,

as more and more AVs are put onto the road, the

injury pay-out amounts should be split between

their manufacturers on, for example, a year-to-

year determination basis. This means that when

manufacturer A causes an accident that produces

injury, that cost is split communally amongst all

manufacturers.

63 Alex Davies, ‘Google’s Self-Driving Car Caused Its First

Crash’ Get Wired Magazine (29 February 2016)

com/2016/02/googles-self-driving-car-may-caused-rst-

crash/> 2 January 2018.

64 Chris Reed, Elizabeth Kennedy, Sara Noguiera Silva

‘Responsibility, Autonomy and Accountability: legal liability

for machine learning’ (n 61) 29.

65 ibid, and further noting that “This approach is supported by

the Draft Report with recommendations to the Commission

on Civil Law Rules on Robotics” (2015/2103(INL), European

Parliament Committee on Legal Affairs 31 May 2016) paras

29-31.

66 These are commonly factored in features of auto insurance

- ‘Compare car insurance quotes to get the lowest rates

in Saskatchewan’ (LowestRates.ca)

insurance/auto/saskatchewan> citing David Marshall,

‘Fair Benets Fairly Delivered: A Review of the Auto

Insurance System in Ontario’ (Ontario Ministry of Finance,

2017) < www.n.gov.on.ca/en/autoinsurance/fair-benets.

html> accessed 1 January 2018; ‘Compare Auto Insurance

Quotes in Ontario’ (LowestRates.ca)

insurance/auto/ontario> accessed 1 January 2018.

49

This should not be seen as a shift or allocation of

blame, nor changing the insuring party. It is analogous

to collecting levies from, for example, blank CD sales.

Rather than requiring the time or public resources

to go after individual problematic activity, it is the

acknowledgment that an undesirable result occurs,

and is made possible by the manufacturer. The levy

is neither a punishment, nor an allocation of liability,

but a recognition that the end result is enabled by

the party in question. For the music industry, this

is the assumption that blank CDs are used to enable

industry-undesirable sharing. For AVs, this is the

assumption that no matter how well-designed AVs

are, accidents will, at least initially, occur and cause

injury. In both cases, costs are ultimately borne by

consumers, whether or not the purchase in question

actually enabled an undesirable result. The industry

simply passes costs along to its purchasers. While

damage may be sufciently and reasonably covered

through traditional insurance by AV owners, the levy

serves both a social purpose, of acknowledging the

enabling of these types of accidents, and a monetary

one, through compensation for injury caused. Even if

manufacturers are not held liable, it is benecial that

the consequences of design be acknowledged. While

collecting societies may have acquired a negative

reputation, the levy in itself is not necessarily a

negative way to address this problem - particularly

where manufacturers have both the motivation and

the capability of reducing this amount by decreasing

injury.

50 This may seem an arbitrary approach that punishes

manufacturers who produce vehicles that do not

cause injury. However, it incentivizes manufacturers

in societally benecial ways. First, it places injury

reduction as the ultimate cost-saving priority to

manufacturers; specically, they can reduce costs by

placing it at the top of the decision-making process

for the car, rather than avoiding liability. Second,

it promotes co-operation and standardization

between companies. Every manufacturer gains

when they collectively reduce injury costs.

Standardized reactions from AVs not only allow

for predictability for human drivers who share the

road with AVs but foster better interaction between

different manufacturers. It also encourages car to

car communication - rather than building an intra-

company network of communication, manufacturers

are incentivized to communicate cross-brand. The

success of one company is the success of all companies.

Third, it reduces the potential for manufacturers to

hold monopolies over AVs. Requiring an entry cost

to enter the market would mean that a company

must be of a certain worth to even attempt to

compete. When scal giants like technology

companies and traditional auto manufacturers are

involved, this is likely to be an unassailable moat.

Placing the cost per-car means that the success of

then-current market players reduces the potential

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3

cost per entry for new manufacturers, lowering the

entry to effective competition. Fourth, it means

that companies can fold in the one-time cost per

car into the purchase price, rather than being liable

in perpetuity for an unpredictable cost. Fifth, it does

not remove the benets of branding from individual

companies as car buyers “frequently cite safety as

the most important factor in selecting a car.”

67

There

is no reason to believe that this would change and

is in fact likely to be reinforced as drivers hand

control over to an AV. Overall, there should still be

the potential for pursuing a particular company in

extreme cases, such as egregious negligence. For

example, if it can be demonstrated that a company

had knowledge of a dangerous vulnerability and

ignored it - such as a design aw that made any

crash likely to ignite the vehicle - they should bear

the full cost for that oversight. While this may seem

like an unclear standard, the law has dealt with such

standards before, given that tort law is built on the

concept of a “reasonable person”.

51 It is possible that many of these incentives could be

achieved by allowing the insurance fund, or other

parties, to pursue manufacturers for negligence.

Even co-operation could be encouraged by allowing

manufacturers to be pursued as a single entity.

However, this places a greater burden on either

the consumer or insurance entity to undergo the

necessary litigation, or at least legwork, to show

the manufacturer’s negligence. One of the problems

unique to machine learning is that the decision-

making process of articial intelligence can be

particularly opaque - consumers may nd it difcult

if not impossible to understand “black box” decision-

making.68 It may be that the consumer attempts

to recover before having proper knowledge of

whether the AV was in fact negligent. Additionally,

litigation puts further strain on the court system.

Allowing for the levy to provide these incentives -

except in extreme cases - means that there is a strict

liability approach to a no-fault problem, namely, the

acknowledgment of blameless enablement, but the

ultimate injury caused.

4. Implementation:

52

When allowing manufacturers to side-step strict

liability, it is naturally important to hold high

standards to entry. This is not to say that the

entry requirements should have monetary value,

as previously mentioned, but should include such

areas as rigorous testing. Strict requirements can

67 Ratan Hudda and others, ‘Self Driving Cars’ (n 12) 7.

68 Chris Reed, Elizabeth Kennedy, Sara Noguiera Silva

‘Responsibility, Autonomy and Accountability: legal liability

for machine learning’ (n 61) 13-14.

reasonably be placed on manufactures as the AV

is still a multi-tonne machine that will be piloted

amongst unarmored pedestrians. The possibility

for co-ordination is also a positive one between

manufacturer and government, since co-ordination

such as car to infrastructure, or car to transit,

have the potential to benet both parties. Car to

infrastructure communication, such as trafc lights,

or road closures, have the ability to make the AV more

efcient, and to alleviate strains on infrastructure

such as trafc jams. Even more crucially, requiring

predictable procedures for emergency vehicles could

result in reduced emergency response times, as AVs

part like the Red Sea as required.

53

Car to transit communication can not only help avoid

collision, but also allows for better co-ordination in

timing, particularly when AVs are used to ll a gap

in transportation rather than replace an individually

owned vehicle. Implementation should also allow

communication between the government insurer

and manufacturers - where damage is tracked to a

particular problem, the government entity has the

ability to convey this to the manufacturer, and the

power to demand a solution. It is unlikely to reach

this level, as manufacturers are incentivized to

better their vehicles regardless, but it nonetheless

allows for a two-factor system of tracking issues with

the AVs.

54

A further requirement could also be standardized

signaling to third parties. One particularly prescient

analysis notes that while AVs are technically more

predictable than their human counterparts, this

does not mean that they are more predictable to

third parties – i.e., those who have not programmed

them.

69

Pedestrians have indications as to whether a

human driver has noticed them. This can include eye

contact, a hand-wave, or, in extreme cases, a rude

gesture. This sort of communication has not yet been

indicated by AV manufacturers, but could grow to be

included in the “price to entry” in order to qualify to

enter the market. This could be as simple as unique

blinking indicators in the pedestrian’s direction, or

as complex as screens on various parts of the car,

but overall serves to show that there needs to be

a consistent dialogue between the regulator and

manufacturers.

F. Public Policy Part Two

55

What a public policy approach to AVs aims to

achieve is incentivizing better questions. Rather

than demanding manufacturers wait on the answer

to “how liable will the company be?”, a proactive

69 Harry Surden, Mary-Anne Williams, ‘Technological Opacity,

Predictability, and Self-Driving Cars’ (n 16).

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Keri Grieman

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public policy approach, such as the one described,

forces companies to instead ask “what is the best

possible way to reduce injury?”.

56

Many billions have been put into researching

and developing fully autonomous vehicles, not to

mention the many stages of partial-autonomy along

the way. The industry growth rate is currently 16%

and is expected to be over $1 trillion by 2025.70 One

policy benet of the proposal thus far discussed

is that rather than stockpiling capital against the

eventuality of a lawsuit, companies can focus on

putting funds towards other areas such as increasing

fuel efciency, reducing vehicle cost, and improving

accessibility. This has the potential for positive

economic impact since research and development

is encouraged, rather than stied or put on hold

to wait for potential legal impacts. While there

is still an indeterminate amount of time to wait

before manufacturers are ready to put consumer-

model AVs on the road, the reluctance to assume

responsibility is palpable as all consumer available

automated features require that there be a licensed

human in the driver seat in order to take control

the instant it becomes necessary - and preferably

even before.

57 This paper’s proposal encourages the introduction

of AVs, while interfering minimally with the current

regime of road rules and liability. It does not require

the scrapping of an entirely workable system,

and simultaneously allows for the incremental

introduction of AVs on the road with a majority of

human drivers. While current automated features on

cars do still require a human driver, it is unnecessary

to allow for a change in liability where the human

driver must still be able to step in.

58

A Public Prosecution Service of Canada working

group has produced a report on the Future of

Automated Vehicles in Canada.

71

While the report

is naturally focused on implementation of semi and

full AVs in Canada specically, it provides a helpful

list on “The Role of Governments”:

• Regulate vehicle safety;

• Harmonize standards [between countries];

• Encourage innovation;

• Protect privacy of individual vehicle users;

• Educate the public;

70 Muhammad Amat, Dr Clemens Schumayer, ‘Self Driving

Cars: Future has already begun’ (n 6) 18.

71 Public Prosecution Services of Canada ‘The Future

of Automated Vehicles in Canada’ (29 January 2018)

accessed

23 June 2018.

• Build data expertise and capacity;

• Develop and enforce trafc laws;

• Oversee insurance and liability;

• Ensure a safe and smooth transition;

•

Build and upgrade transportation

infrastructure.72

59

While many of these areas have been discussed in this

paper, it is a helpful reminder that a government’s

role is not simply to mandate legal change from

a removed perspective, but to aid transition in a

variety of areas and elements. Insurance and liability

are naturally important, but if laws are not enforced,

or the public remain unconvinced, then the potential

benets will not be realized in full.

60 Public policy is an important tool to achieve social

acceptance. Transparency and clarity of legislation

will be key to sufcient initial condence in

consumers to start building positive interaction

– personal experience being the ultimate key to

social acceptance, both by the individual themselves

and word of mouth. If the policy is to achieve the

aforementioned benets of AVs, it must have

the public on board. Changes inherently bring

opposition, but this has not stopped legislating

in favour of change in the past; for example, high

occupancy vehicle lanes encourage car-sharing,

tax incentives for electric and hybrid vehicles

incentivize greener purchases, and seat-belt and

airbags have forced societal change directly.73

61

Testing and safety are priority concerns. Social

acceptance will never be achieved unless there is

a belief in the safety of AVs. Consumers have good

reasons to be skeptical of the automotive industry,

and safety records in particular, especially given

the Ford Pinto’s transmission problems, Firestone

tire blowouts, the Takata airbag recall, and the

Volkswagen emission scandal, which all suggest

that prot may have been prioritized over safety.

AVs cannot afford this type of prot post-mortem.

Testing must be particularly stringent, and indeed

better than the average driver to overcome the

concerns over non-human drivers. The adoption

of the aforementioned levy approach is benecial

as consumers could not only avoid liability, but it

would ensure that companies are serious enough

about the vehicle’s safety capacity passengers to

“put their money where their mouth is” in terms

of human safety.

72 ibid 14.

73 Ratan Hudda and others, ‘Self Driving Cars’ (n 12).

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62

Two potential ways to foster social acceptance

are publicizing existing uses and creating pilot

programs. The public already interacts with AI

transportation, such as Masdar and Heathrow

airport shuttles, the Milan driverless metro, and

driverless trucks in Australian and Chilean mines.74

A simple step is to make the public more aware that

these transportation methods are already in use,

safely, efciently, and successfully. Pilot programs

to provide AVs to impoverished communities or

those underserved by current transit initiatives

can be a way to allow for optional adoption and

demonstrable benet, though particular care should

be taken to show that this is not a testing ground.

Initiatives for the visually impaired, for example,

would demonstrate that unlike AI levels below ve,

fully autonomous AVs make car travel accessible

to all. Both publicization and pilot programs have

signicant potential in terms of building positive

personal experiences, promoting both personal

acceptance and word of mouth recognition.

63

Social acceptance of AVs through public policy

methods faces unique challenges. Seatbelt adoption,

for example, used a variety of methods in the United

States: policies and mandates such as laws regarding

use; incentives and rewards based on use; signs

politely reminding seatbelt use; and feedback on

community performance.75 These methods are not

easily transportable to AV adoption. While laws

regarding use are naturally important in terms

of regulation, AVs present unique challenges; for

example, although wearing a seatbelt or not is a

distinct choice, it is still possible to drive without

one. If one is in an AV, the choice is not whether

or not to drive, since by the time an individual has

made the choice to use an AV, they have accepted

the overarching function of the AV, rather than

deciding whether or not to wear a seatbelt while

still using the car in a way they are familiar. The

role of public policy in the case of AVs is to remove

uncertainties which might disincentivize use, rather

than attempt to force a particular choice. Public

policy should not be focused on forcing the adoption

of AVs, but on removing the barriers to those in the

position to adopt their use, such as uncertainties

like liability. No car owner wants to be unsure of

whether or not they will be liable for an accident

over which they had no control, even if they were

74 Alain L Kornhauser, ‘Smart Driving Cars: Where Are We

Going? Why Are We Going? Where Are We Now? What Is In It

for Whom? How Might We Get There? Where Might We End

Up?’ (2013) (Princeton University TransAction Conference,

18 April 2013)

Presentations/ITE_SmartDrivingCars/TransAction2013_

SmartDrivingCars_041113.pdf> accessed 22 June 2018 24.

75 E Scott Geller, Timothy D Ludwig, ‘A conceptual

framework for developing and evaluating behavior

change interventions for injury control’ (Health Education

Research, 1990) DOI: 10.1093/her/5.2.125.

aware that the probability of an accident occurring

was much lower.

G. Challenges – Legal and Technical

64

There are many challenges to be faced in introducing

AVs. There are uncountable minor changes that

must be introduced - everything from regulations

requiring hands on the wheel, to how vehicles are

fueled. There are much more impactful challenges

to be faced, however. Manufacturers must be

discouraged from attempting to allow their car to

game the system and offering consumers a vehicle

that disadvantages either other AVs or human

drivers.

65 Where AVs can communicate between themselves

and infrastructure, the ability of third parties to

hack the system for their own potential malicious

ends is a concern, particularly in a nexus with

personal privacy. Personal privacy has already

become a crucial battle in the 21st century, and AVs

will accelerate the race between laws protecting

privacy of data, and companies using that data for

their own means. AV data can not only identify a

person and their current whereabouts, but likely a

great deal of telling information about their habits,

friends, and lifestyle. Beyond hacking, connections

between vehicles and with infrastructure and the

manufacturer could still be used to collect and

transmit personal data. Unless forced to do so,

manufacturers are unlikely to allow consumers

to opt out of data transmission since a great deal

of the data will likely be used for positive means,

such as optimizing function and driving patterns.

However, there is still the danger that information

released could identify an individual. Collection

has signicant benets, and the problem must be

addressed by controlling use and disclosure. This

is done through data protection law. The question

remains whether existing data protection law is

sufcient. While some jurisdictions have unied their

approach to data protection, such as the European

Union’s General Data Protection Regulation, there

is no global unity on issues such as what constitutes

personal data; who can use what, and how; what

protection should be in place; or how to properly

anonymize that data. Common data protection issues

and proffered solutions can be seen in other areas

such as medical data; data is crucial for research,

but there is a signicant threat to privacy if data is

insufciently anonymized or used in ways that were

not foreseen at collection. Addressing such issues for

AVs might follow practices similar to medical data

collection or may be found to require a customized

regime that can be updated faster than traditional

data protection law.

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Keri Grieman

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66

While the system suggested should, on the whole,

be able to integrate with current systems, there

may be unforeseen challenges. For example, it has

been suggested that both the Geneva and Vienna

Conventions may not allow for a vehicle that does

not permit a human driver to resume control.76

Individual jurisdictions, not to mention countries,

may have legislation or precedents that negatively

impact, or currently do not allow for, the integration

of AVs.

67

Functionally, AVs still have hurdles to overcome.

They are expensive, perhaps prohibitively so as, the

extra equipment that allows the AV to drive itself

are not cheap, and their cost is in addition to the

vehicle itself. Extensive testing is also expensive and

is a cost that is likely to remain. AVs still struggle

with weather, and while testing is being carried

out to overcome this,77 accidents have occurred

on the basis of weather conditions.

78

Additionally,

the lack of opacity is a barrier to trust. While AVs

have much to offer, it is a legitimate complaint

that the “decisions” made by AVs can be difcult

to understand, particularly from a lay-person’s

perspective. This lack of clarity can carry through

to lawsuits and will challenge the technical expertise

of those who may be ill-equipped to evaluate such

decisions.

68

Even with the suggested changes, there are will

be systemic issues to be addressed. While co-

operation between companies in terms of life-saving

measures, predictability, and integration is positive,

it inherently raises concerns about competition

and collusion. Companies may be motivated to,

for example, nd a system that works well enough

between them and keep to it, rather than striving

for better, safer, or more efcient advancements.

H. Conclusion

69 There is a world of potential to be unlocked by AVs.

On a purely ethical basis, it would be very difcult to

ignore their lifesaving potential. Beyond this, there

are countless other, if lesser, benets. A car is an

expensive investment that sits unused an estimated

95% of its life.79 Currently, 40% of fuel is used nding

76 Jonathan Margolis, ‘Self-driving cars still face multiple

roadblocks’ Financial Times (New York, 11 January 2017)

<www.ft.com/content/f9847198-d40b-11e6-b06b-

680c49b4b4c0> accessed 2 January 2018.

77 snow - Ford Media, ‘Ford’s Industry rst autonomous

vehicle tests in snow’ (YouTube, 11 January 2016)

youtube.com/watch?v=vShi-xx6ze8> accessed 2 January

2018.

78 Neal Boudette, ‘Tesla’s Self-Driving System Cleared in

Deadly Crash’ (n 7).

79 Muhammad Amat, Dr Clemens Schumayer, ‘Self Driving

a parking space in urban areas.80 Time, energy,

and stress are expended on commutes that could

be spent in better, or at least more relaxing, ways.

Even better use of land is a possibility, as concepts

such as a “park and ride” for airports need no longer

take up space.81

70

AVs have the potential to remove every human

failing from the province of transportation. This

has an impact beyond human choices, such as

driving while intoxicated or tired. Vehicles can see

further than human eyes and communicate on many

more levels. A car that needs no human driver can

avoid a traditional vehicle’s security liabilities -

with no need for human eyes, there is no need for

a vulnerable glass portal at the front of the car. AVs

have the potential to become metaphorical tanks,

as they need not account for a driver’s ability to see

from various angles.

71

Current liability conceptions are deeply problematic

for AVs. Not only are they uncertain in terms of

introducing AVs, but the current jurisprudence

provides no promising answers as to where liability

may fall. Pinning liability on parties who have no

control, or on parties who will make it a primary

priority over more important concerns, is likely have

the effect of chilling the market before it can really

begin. Ignoring liability questions and assuming

that the market will develop and ourish when left

alone is optimistic at best, and at worst enables a

monopolistic and limited-benets system.

72

It is important that public policies regarding AVs

are scalable. It needs to be capable of addressing

a slow trickle of AVs as they enter the market,

and an increasing majority as they become more

affordable and marketable. The regime needs to

ensure that victims are not left out in the cold, and

manufacturers not incentivized to prioritize scal

vulnerability ahead of human safety.

73

It is crucial that we incentivize better questions -

how to achieve a perfect no-injury record, rather

than where liability should fall on a scale of

priorities. How to improve access for individuals

with mobility issues, rather than how to inch forward

without invoking liability. Regulation should aim to

encourage one particular future: where accidents

are unusual, and vehicular deaths non-existent. But

this needs to start somewhere and needs law reform

action to put the wheels in motion.

74

Ultimately, liability conceptions need to evolve in

order to fully realize the potential benets of AVs

on a societal level. This is best achieved by letting

Cars: Future has already begun’ (n 6) 11.

80 ibid.

81 ibid 18.

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go of traditional liability conceptions and blame.

There needs to be strict liability as damage needs

to be reimbursed, and no-one should face nancial

hardship for decisions beyond their control. This

strict liability needs to be placed without fault.

Attempting to place fault and blame results in

inevitable time, money, and litigation spent, when

such energies are better focused on remedying the

problem, compensating the victim, and improving

the AVs. It also sidesteps the problem of incentivizing

avoidance of liability rather than the prevention of

harm. Compulsory insurance is already required in

most if not all countries currently developing AVs,

and this insurance setup can and should be expanded

to cover AV accidents. Doing so from a single pool

allows for streamlined claims, a direct dialogue

between claim evaluators and manufacturers, and co-

operation regarding AV issues. Such a system could

be realized through an independent government

entity and augmented by a manufacturer levy.

75 “May you live in exciting times” is often cited to be

a curse. Yet these are indeed exciting times – we

are at a crossroads of design, manufacturing, and

vision. We have the unique opportunity to foresee

innovation, and to level the eld in preparation of its

arrival. We have a distinct moment to celebrate one

of humanity’s greatest qualities, the prerequisite of

all innovation: drive. Let’s put the pedal to the metal.