Google (now Alphabet) first introduced its Self-Driving Car Project (now Waymo) to the public ten years ago. Since then, the burgeoning autonomous cars (ACs) industry has been at the center of significant investment in research and development (Townsend, 2020). Car manufacturers, incentivized by the seemingly “winner take all” market of shared rides, have pushed to become the first to take their experiments in “engineering labs and university test beds as well as on the city streets” to scale (Forlano, 2019). Even as the companies behind the technology promise a safer, more reliable, and sustainable future for transport and mobility, an increasing number of visible challenges put the narrative of an inevitably improved future in doubt. Resolving and negotiating these value tensions require design assessment methods, revisions of the beliefs and desires behind autonomous cars, as well as an understanding of the complex socio-economic and political systems within which they sit.
Driving forces
In his book, The Clock of Long Now (Brand, 1999), Stewart Brand explains the idea behind what he calls Pace Layers, a systems theory about robust and adaptive systems and how we can best understand the timescales within which humans are situated. He imagines six concentric circles, each representing an independent layer on a different timescale from the others but interacting with their neighbors in a way that keeps the system adaptive, robust, and resilient (Brand, 1999),). From the slowest at the center to the fastest at the outside, Nature, Culture, Governance, Infrastructure, Commerce, and Fashion (NCGICF) move at “at their own pace, safely sustained by the slower levels below and kept invigorated by the livelier levels above.”( Brand, 1999))
Figure 1- Pace Layering, The Clock of the Long Now, Stewart Brand
Noting the role of Fashion in the origin of mass manufactured automobiles, Brand then writes “[fashion] is culture cut free to experiment as creatively and irresponsibly as the society can bear. From all that variety comes driving energy for commerce (the annual model change in automobiles)” (Brand, 1999)
This driving energy has since––aided “by global markets and the digital and network revolutions” (Brand, 1999)––accelerated automotive commerce to an extent where its influence has reverberated across the layers with exponential pace. From transforming the infrastructure in cities, to promises of freedom of personal movement, drawing the urban and the rural together, and creating thousands of new jobs; cars have ultimately resulted in the dependence on one form of transportation, leading to suburban sprawls, “dull and time wasting journeys”, and of course, pollution (MacNeill, 1963). Cars require roads, roads lead to trips, trips to accidents and regulations, freedom of mobility transforms into traffic, and ultimately the extractive nature of vehicle production and the resulting pollution destroys nature. Indeed, to follow the impact of automobiles is to cut across Brand’s pace layers.
As the autonomous car, fashioned by commerce, emerges out of previous iterations of industrial revolutions (Schwab, 2016), we must interrogate the values and desires behind this Cyber-Physical System (CPS.) We must also ask how this AI enabled CPS will echo within the system?
How can a system level approach then, granted by the adoption of the Pace Layers Model, allow us to observe the current state of the system?
Shifting gears
Value Sensitive Design (VSD) is a “theory, method and practice to account for human values in a principled and systematic manner throughout the technical design process” (Friedman & Hendry, 2019; Winkler & Spiekermann, 2018). It is a tripartite methodology that explores values at different phases of the design process: namely, conceptual (who are the relevant stakeholders); empirical (how stakeholders experience a technology); and technical (how technology might support the identified values.) VSD’s interactional stance on technology, that “people and social systems affect technological development, and new technologies shape (but do not rigidly determine) individual behavior and social systems) (Friedman, Kahn & Borning, 2002); and its broad conception of ‘stakeholders’ to include ‘people, groups, organizations, neighborhoods, institutions, past and future generations, non-human species (e.g. animals), non-human elements (buildings, technologies, mountains),’ allows an abstraction that aligns with Brand’s definition of technology as “gravity” in the system (Brand, 1999). This commonality makes it then possible to apply Stakeholder Mapping, one of the methods of VSD, to the Pace Layers Model.
The utility of this process is twofold. First, by mixing Stakeholder Mapping and Pace Layers to explore the interactions between Fashion, Commerce, Infrastructure, Governance, Culture and Nature, we identify each layer as an irremovable stakeholder group within the system (lest we reduce the complexity of the system.) Second, we identify direct (interacting directly with the technology) and indirect (indirectly affected by the technology) stakeholders within these layers.
The following application of this suggested method is not meant to be an extensive assessment, and only serves to demonstrate the approach.
Fashion (“Try this! No, no, try this!”)
Direct stakeholder: companies producing prototypes
Values: creativity, identity, originality
Indirect stakeholder: tech enthusiasts
Values: originality, vision
Commerce (“Commerce may instruct but must not control the levels below it”)
Direct stakeholder: autonomous car manufacturers, software providers
Values: ownership and property, safety, public image
Indirect stakeholder: public transit users
Values: accessibility, reliability, safety
Infrastructure (“high yield, but delayed payback”)
Direct stakeholder: urban planners
Values: sustainability, accessibility
Indirect stakeholder: long-time residents
Values: liveability, community, safety
Governance (“public, private, and social sectors […] serve the larger, slower good”
Direct stakeholder: safety regulator
Values: safety, accountability
Indirect stakeholder: pedestrians
Values: safety, freedom from bias, privacy
Culture (“slower than economic and political history, it moves at the pace of language and religion”)
Direct stakeholder: people using ACs
Values: comfort, privacy, trust, inclusivity
Indirect stakeholder: people using cars
Values: community, identity
Nature (“when we disturb nature at its own scale, […] we risk triggering apocalyptic forces.”
Direct stakeholder: environment from which natural resources are extracted
Values: sustainability, responsibility
Indirect stakeholder: planet’s ecology
Values: environmental sustainability, responsibility
Evaluating the values of different stakeholders with this systems approach forces the practitioner to peer into the interactions between-and-within the layers.
The Fashion layer, true to its nature, pushes the limits of creativity, with stakeholders juggling values of originality and identity to envision a multitude of autonomous car prototypes, or what Anthony Townsend calls vehicular “multispecies.” (Townsend, 2020) These imaginaries are at the heart of experimentations undertaken by commerce, as it surveys this landscape to latch on to stickier “technovisions” (Dourish & Bell, 2011), transforming them into profitable business models. These values of ownership and property bring their own set of challenges. Primarily bound by values of safety, and the technical limitations of executing on Level 5 fully autonomous cars (SAE International, 2018) in the short-term, the industry has been propelled by a winner-take-all approach to the shared ride market (Wayland & Kolodny, 2020). While the jury is still out on whether shared rides complement or replace public transit (Hall, Palson & Price, 2018; Graehler, Mucci & Erhardt, 2018), the transit user’s expectations of accessibility, reliability, and safety impose some obligations on the execution of such business plans (NACTO, 2019). If left unfettered, as Brand writes, commerce can have a detrimental impact on the layers below it. And so, as autonomous cars begin to integrate with existing infrastructure, the past consequences of the automobile on urban and rural landscapes should also remind us of the extractive nature of such technologies, and help us anticipate their future environmental costs. Such tensions between the values of commerce and infrastructure invite a balancing response from the governance layer. The death of Elaine Herzberg, the first pedestrian killed by an autonomous car (Gonzales, 2019), and stories of discriminatory dynamic pricing (Wiggers, 2020) become trigger events for local governments to intervene in order to assure safety and accountability within the system. The eagerness of governance to deliver on these values leads to further friction as it begins to interact with culture. When governance begins to leverage the sensor reliant capabilities embedded in autonomous cars (Anderson, 2019) to develop mechanisms aimed at social stability, stakeholders at the culture level respond. Likes tires on concrete, governance’s pace of surveillance is in friction with a culture of privacy that is moving at a much slower pace. Slow as it may be, culture itself experiences significant shifts when it is traversed by such a technology. The earliest narrative behind autonomous cars has been their promise of inclusivity and accessibility, with goals of activating the freedom of mobility for a new set of users in young, old, and handicapped passengers (Saripalli, 2017). Another shift, Julie Carpenter adds is the mutation of cars into “work or living space[s]” which could veer our relationship with cars beyond “product attachment” into “primary territory” (Carpenter, 2017) Eventually, if autonomous cars accelerate past these stabilizing layers unchecked, they begin to “disturb nature at its own scale.”( Brand, 1999) Such a CPS, perhaps more environmentally reliant and tasking than the automobile (EIA, 2018), “risks triggering apocalyptic forces.” (Brand, 1999)
If these early indications of the impact of the autonomous car point to unresolved conflicts of values, with potentially dire and destructive consequences, how then can we begin to imagine a more robust, adaptive, and ultimately resilient future state for the system?
One of the main critiques of Value Sensitive Design, and particularly Stakeholder Mapping, is its limitation in addressing long-term design goals (Friedman, Nathan, & Yoo, 2017). Because it is centered on present values, the design outcomes are observed to negotiate values within a very limited timeframe and do not account for shifting values and changes in the system. Akin to a handheld speed camera, the method only provides the practitioner of a NBE a tool with which to assess the current state of the system. How then can we begin to imagine a future state of the system?
While other VSD centered methods like Multi-lifespan envisioning are recent developments seeking to address this limitation of short-termism (Friedman, Nathan, & Yoo, 2017), we suggest another more established method as a more practical tool for the practitioner of an NBE in assessing possible future states of a system.
Back to the Future
Speculative Fiction/Futures (SF) is an overarching genre that captures works whose narratives explore the long-term implications of current realities (often technological) as a means of reflecting on today’s anxieties and yearning. Because the imagined worlds that come out of such works are often based on science and current trends, the methods behind “world building” (Johnson, 2011) provide meaningful and established ways of imagining and interrogating future uncertainties in complex systems As such, decisions makers have applied similar methods of forecasting and backcasting in quest of short-term and long-term goals alignments.
A simple but commonly used method within SF is Scenario Planning, which uses axes of uncertainty (Webb, 2020) to imagine possible and probable scenarios of the future. When applied traditionally, this method relies on the STEEP (Social, Technological, Economical, Environmental, and Political) categorization of the identified factors (uncertainties.) Because we are looking to extend our exploration of the current state of the system, we must first situate Scenario Planning within the same systems theoretical model. Integrating Scenario Planning within the Pace Layers Model removes the Technological categorizations of factors, turning it into an abstraction that instead permeates the system. This conversion allows us to engage both Stakeholder Mapping and Scenario Planning within the same model of an adaptive and complex system.
Using this approach, we begin to oscillate between the current and future states of the system, all the while carrying the values identified in the current state into their future state(s). The examination of these values and how they might transform the state of the system as they sit atop dynamic pace layers should unearth generally unintuitive states of the future, forcing the practitioner to think beyond their personal or professional biases. This can ultimately lead to preemptive engagement with perceived future values that could, using the same iterative approach, transform or adapt over time.
Though we are unable to demonstrate the detailed application of this approach, an example for two identified axes of uncertainty (Webb, 2020): 1) autonomous cars are nowhere <-> autonomous cars are everywhere, and 2) planet health declines <-> planet health improves and the possible future states are illustrated below.
Figure 2 Axes of Uncertainty atop Pace Layers
In a regular scenario planning exercise, each quadrant would be populated to include more descriptive scenarios. Instead, the focus here is the addition of pace layers to visualize potential stable and unstable states of the system. By no means prescriptive, the process of speculating on the possible future states of the system is intended to highlight system imbalance by drawing the effects of applying layer level values for an extended period. Here for example, the choice of the two axes aligns with values of commerce and nature, with what we consider to be a stable state in the top right quadrant, commerce pushing ahead in the bottom right quadrant, both commerce and nature slowing down in the bottom left, and finally nature controlling the pace in the top left corner.
Speed bumps
The combinative approach of Stakeholder mapping and Speculative Fiction within a systems theory dimension is an attempt to reveal a more comprehensive view of autonomous cars (and other CPSs). As Sustar et. Al suggest, “similar to belief, imagination is a representational state that has intentionality.” (Sustar, Mladenovi ́c, & Givoni, 2020) Equipping practitioners of a New Branch of Engineering with the means to design, construct, commission, manage and decommission a CPS requires methods to visualize current and future expected states of the system, as it is between these two states that intention sits.
Our hope is that by using Pace Layers as a bridge between the two methods, we are offering a more long-term and system theoretical approach to intent. In some ways this framework also heeds Laura Florano’s call for “balanc[ing] intellectual contributions to knowledge in traditional forms along with the making of things that are intended to help us think?” (Forlano, 2019) In fact, to think about the future is to sit within the realm of “myths emanated from the center of Silicon Valley” where “technovisions” suggest inevitably better worlds. It is therefore fitting then, that we find ourselves interrogating intent within this speculative fashion layer, by taking apart elusive futures that distance us from our “responsibility to our world today.” (Forlano, 2019)
Wherever it is introduced in the system, technology is nothing less than a gravitational force (Brand, 1999) that resonates across the system, negotiating beliefs, desires, values, and intentions across stabilizing layers. How then can we begin to imagine a healthier, adaptive, and resilient system? How do we decide which values to keep and which values to leave behind?
To this, Brand writes, “it is precisely in the apparent contradictions between the pace layers that civilization finds its surest health” (Brand, 1999)
Figure 3 From Stakeholders mapping to Speculative Fiction
References
Brand, S., 1999. Clock Of The Long Now. New York: Basic Books.
Townsend, A. M. (2020). Ghost Road: Beyond the Driverless Car (1st ed.). W. W. Norton & Company.
Forlano, L. (2019). “Stabilizing/Destabilizing the Driverless City: Speculative Futures and Autonomous Vehicles.” International Journal of Communication.
Schwab, K., 2016. The Fourth Industrial Revolution: What It Means And How To Respond. [online] World Economic Forum. Available at: <https://www.weforum.org/agenda/2016/01/the-fourth-industrial-revolution-what-it-means-and-how-to-respond/> [Accessed 30 October 2020].
On the City, 1963. [TV programme] CBC.
Friedman, B., & Hendry, D. G. (2019). Value Sensitive Design: Shaping Technology with Moral Imagination (The MIT Press) (Illustrated ed.). The MIT Press.
Dourish, P., & Bell, G. (2011). Divining a digital future: Mess and mythology in ubiquitous computing. Cambridge, MA: MIT Press
Bardina, J. (2013). Autonomy and Intelligent Technologies for Advanced Inspection Systems. SAE International Journal of Aerospace, 6(1), 94–100. https://doi.org/10.4271/2013-01-2092
Wayland, M. L. K. (2020, January 24). Debut of GM’s Cruise Origin shows the future of ride-sharing, autonomous vehicles is a box. CNBC. https://www.cnbc.com/2020/01/23/debut-of-gms-cruise-origin-shows-the-future-of-ride-hailing-autonomous-vehicles-is-a-box.html
Hall, J. D., Palsson, C., & Price, J. (2018). Is Uber a substitute or complement for public transit? Journal of Urban Economics, 108, 36–50. https://doi.org/10.1016/j.jue.2018.09.003
Graehler, M., R. A. Mucci and G. D. Erhardt (2019). Understanding the recent transit ridership decline in
major US cities: service cuts or emerging modes? 98th Annual Meeting of the Transportation Research
Board (TRB), Washington D.C.
Blueprint for Autonomous Urbanism: Second Edition. (2019, September 25). National Association of City Transportation Officials. https://nacto.org/publication/bau2/
Gonzales, R. (2019, November 7). Feds Say Self-Driving Uber SUV Did Not Recognize Jaywalking Pedestrian In Fatal Crash. NPR. https://www.npr.org/2019/11/07/777438412/feds-say-self-driving-uber-suv-did-not-recognize-jaywalking-pedestrian-in-fatal-
Wiggers, K. (2020, June 13). Researchers find racial discrimination in ‘dynamic pricing’ algorithms used by Uber, Lyft, and others. VentureBeat. https://venturebeat.com/2020/06/12/researchers-find-racial-discrimination-in-dynamic-pricing-algorithms-used-by-uber-lyft-and-others/
Anderson, M. (2019, June 24). The Self-Driving Car Is a Surveillance Tool. IEEE Spectrum. https://spectrum.ieee.org/cars-that-think/transportation/self-driving/surveillance-and-the-selfdriving-car
Saripalli, S. (2017, October 6). Are self-driving cars the future of mobility for disabled people? The Conversation. https://theconversation.com/are-self-driving-cars-the-future-of-mobility-for-disabled-people-84037
Carpenter, J. (2020). Kill switch: The evolution of road rage in an increasingly AI car culture. Living with Robots: Emerging Issues on the Psychological and Social Implications of Robotics, 38–40. https://doi.org/10.1016/C2017-0-03348-7
Autonomous Vehicles: Uncertainties and Energy Implications. (2018). Annual Energy Outlook, 1–18. https://www.eia.gov/outlooks/aeo/pdf/AV.pdf
Friedman, B., Nathan, L. P., & Yoo, D. (2015). Multi-Lifespan Information System Design. Aarhus Series on Human Centered Computing, 1(1), 4. https://doi.org/10.7146/aahcc.v1i1.21396
Johnson, B. D. (2011). Science Fiction Prototyping: Designing the Future with Science Fiction (1st ed.). Morgan & Claypool Publishers.
Webb, A. (2020, June 4). How Futurists Cope With Uncertainty - The Startup. Medium. https://medium.com/swlh/how-futurists-cope-with-uncertainty-a4fbdff4b8c6
Sustar, H., Mladenović, M. š. N., & Givoni, M. (2020). The Landscape of Envisioning and Speculative Design Methods for Sustainable Mobility Futures. Sustainability, 12(6), 2447. https://doi.org/10.3390/su12062447