simply port them over to a product development environment. In particular, they are not designed to track downstream consequences over impacted populations and do not support the iterative product development and deployment cycles used by technology enterprises (Moss & Metcalf 2020).

In general, corporate approaches to research ethics oversight have been quite ad hoc and, in the case of social media companies, have sometimes been implemented primarily as a result of adverse publicity. As a result, an agreed-upon consistent strategy or set of guidelines for reviewing this type of research has yet to emerge, although the Future of Privacy Forum has developed a promising candidate (Tene & Polonetsky 2016). The multi-part framework identifies the data or other material to be reviewed, when would a review be conducted and by whom, and what principles should apply. It also discusses the extent to which research using non-personally identifiable information should also be subject to oversight.

More recently, some initial work has been undertaken to measure the level of ethical awareness among corporate data scientists (Weaver 2022). However, further research is required to solidly validate the instruments used in that study.

The message here for Smart Life research is that significant attention needs to be paid to the personal and societal effects of studies that are intended to assess the effects and impacts of SL technologies, in particular upon individuals and groups who are unaware of the experimental nature of their exposure to the systems.

## 4.9 Conclusions

The material in this chapter has provided an introductory view of some key Smart Life engineering issues from different social and behavioral standpoints. As Steen (2023) has pointed out, the process of *doing ethics* is a participatory and iterative activity, involving thoughtful reflection, inquiry, and deliberation. A system-level approach to SL engineering helps situate this activity, by empowering practitioners to explore diverse perspectives, question implicit design assumptions, and explore the relationships between overall stakeholder experiences and underlying technical decisions and trade-offs.

The so-called *soft-science* topics of human-centric values and ethics can sometimes be challenging for highly technical experts and specialists to grapple with. The need to contend with design questions that have no provably correct answers, and value trade-offs that have no demonstrably optimal solutions, often does not come naturally to skilled practitioners in the hard sciences. Unlike our colleagues in medicine and law, the engineering profession lacks a historically strong tradition of formally incorporating studies of human-centric values and ethics in the process of formative education and career progression. Yet the awareness of such issues is crucial for the successful deployment of complex socio-technical Smart Life systems that are embedded in the real world.

It is anticipated that this chapter's material and the comprehensive reference list will be helpful to those needing further insights and guidance.
**Acknowledgments** The helpful assistance and feedback from reviewers of earlier drafts of this chapter are gratefully recognized. The author acknowledges the funding support for this work that has been provided by Linqto Inc. (www.linqto.com) and Incióna LLC (www.inciona.com).

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<figure><img src="image_39.png" /></figure>
Part II

Conceptual Contributions to Smart Life
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**Chapter 5**
# Smart Environments: Implications for Environmental Governance

Karen Bakker and Max Ritts

**Abstract** Environmental governance has the potential to be significantly transformed by Smart Environments technologies, e.g., technologies that enabled enhanced environmental monitoring and analytic procedures via combinations of information and communication technologies (ICT), conventional monitoring approaches (e.g., remote sensing), and Internet of Things (IoT) applications (e.g., Environmental Sensor Networks (ESNs)). This chapter offers an update of a 2018 paper that assessed these developments through the term “Smart Earth,” and which likewise engaged the potential implications and pitfalls of new digital technologies for environmental governance. Here, we offer a meta-review of research on what we now call “Smart Environments,” ranging from ecological informatics to the digital humanities. We pair this with a critical perspective on pathways for evolution in environmental governance frameworks, exploring five key Smart Environments issues relevant to environmental governance: data, real-time regulation, predictive management, open source, and citizen sensing. We conclude with suggestions for future research directions and transdisciplinary conversations about environmental governance in a Smart Environments world.

**Keywords** Environmental governance · Data · Smart technology · Nature

The completion of this chapter was tragically complicated by the passing of its lead author, Karen Bakker, in August 2023. The second author and the editors of this book wish to dedicate the chapter to Karen’s memory. Together, we celebrate the considerable impact Karen had in shaping the fields of environmental geography, water governance, and digital environmental technologies.

K. Bakker

Department of Geography and Institute for Resources, Environment, and Sustainability,
University of British Columbia, Vancouver, BC, Canada

M. Ritts (✉)

Department of Geography, Clark University, Worcester, MA, USA
e-mail: mritts@clarku.edu
## 5.1 Introduction

The application of digital technologies to environmental monitoring is radically redefining the prospects for environmental governance in the twenty-first century. Collectively, the proliferating examples featuring in this space are establishing new rationalities and practices for governing environments—with implications for governance at a range of scales. As explored below, digital technologies are now being applied in many sectors and for a broad range of purposes: to monitor and manage ecosystems; to detect and minimize pollution; to surveil and respond to environmental crimes; to measure, profile, and gain access to resources; and to mitigate environmental threats such as floods, fires, climate change, and biodiversity loss. Smart Environments are analogous to Smart Cities, and as an intellectual and governance framework share many of their enabling discourses, including cybernetics, systems theory, and military rationality. As with Smart Cities, the Smart Environments agenda also raises serious concerns about new forms of privacy and surveillance, the deepening involvement of corporate actors in governance, and new modalities of social exclusion and structural inequality.<fnref n="1" /> This chapter overviews these issues through the lens of environmental governance. We draw on the conceptualization developed by Bridge and Perrault to identify a particular trajectory of interest to us: environmental governance as “designing responsive and flexible social mechanisms that allow adaptation as new information becomes available.”<fnref n="2" /> The merit of Bridge and Perrault’s approach is its emphasis on the significance of “information,” which manifests in the present case as aggregations of data. Expressions of smart governance share an overarching interest in measuring—and datafying—ecosystem processes in new and sometimes intimate ways. They are consciously attuned to prospect of novel environmental shocks and stochastic changes. But above all, they are utopian in the possibilities governance can claim to draw from data as a constantly issuing stream of diverse environmental processes.

Our central claim is that the data provided by Smart Environments technologies—our central focus in this chapter—are creating conditions for significant change across the global assemblage of actors and institutions, laws, and norms that structure environmental decision-making regarding across spheres of environmental management and conservation (e.g., “environmental governance”). Smart Environments technologies dramatically increase the availability of data and seek to decrease or even eliminate the time lag between data collection and analysis all while expanding the types of variables that can be assessed. The resulting hyper-abundance of data (rather than scarce data) is repeatedly touted by decision-makers for enabling new optimizable and real-time (rather than post hoc) regulatory responses. But this promise is not without its intellectual critics, nor a slate of evident real-world problems. Below, we explore the significance of these

<footnote n="1">¹ See Bakker and Ritts (2018); Marvin et al. (2015); Luque-Alaya and Marvin (2020); Halpern and Mitchell (2023).</footnote>

<footnote n="2">² Bridge and Perrault (2009), p. 480.</footnote>
developments. We begin by looking at some of the intellectual converges shaping
this discussion space. Then we look at applications of Smart Environments before
turning to the risks and harms of their further expansions and imbrications with
governance.<fnref n="3" />

## 5.2 Digitization at a Planetary Scale? Intellectual Convergences in Smart Environments Technologies

Twenty years ago, many of the technologies that now aggregated under Smart Environments were nascent or nonexistent. A range of intellectual traditions existed concerning the possibilities of computationally enhanced forms of spatial governance (for a good history, see Halpern and Mitchell 2023). But conversations had yet to form—or at least become formalized—across many of the scientific disciplines that deploy and debate these technologies today. But in the ensuing two decades, new interdisciplinary journals emerged, including *Ecological Informatics* (founded in 2006) and the *International Journal of Digital Earth* (2008), that sought to convene growing cross-disciplinary interests. Entirely new sub-disciplines—such as the environmental humanities, a field designed to explore interconnections across environmental history, philosophy, human geography, and political ecology—also evolved and would be regularly engaged with the environmental data politics questions that subtend expansion of Smart Environments.<fnref n="4" /> Meanwhile, journals concerned with the technical and applied aspects of Smart Environments—such as those involving computer engineering, remote sensing, and biological sciences communities—display a growing interest in their environmental governance implications.<fnref n="5" /> These intellectual convergences are being spurred on by the entrance of an additional actor: private corporations. Since the mid-2010s, companies like Microsoft and Google have been investing significant amounts of capital into the development of AI-supported “Earth algorithms” that would guide smart environmental practices.<fnref n="6" /> Other notable initiatives include Nokia’s “Sensor Planet,” IBM’s “A Smarter Planet,” HP Labs’ “Central Nervous System for the Earth” (CeNSE), NASA’s Earth Observing System Data and Information System (EOSDIS), and Cisco/NASA’s collaborative “Planetary Skin Institute,” together with a rapidly proliferating ecosystem of apps.<fnref n="7" />

Many of these initiatives focus on the interface between proliferating digital environmental sensors (e.g., motion detectors, hydrophones, cameras) and Internet-based communications technologies, which, combined with cloud-based

<footnote n="3">³ This chapter has been adapted and updated from Bakker and Ritts (2018).</footnote>

<footnote n="4">⁴ See Gabrys et al. (2016).</footnote>

<footnote n="5">⁵ For examples, see Jackson and Borbow (2015); Galaz et al. (2017).</footnote>

<footnote n="6">⁶ Joppa (2017).</footnote>

<footnote n="7">⁷ Jepson and Ladle (2015).</footnote>
data storage, enable unprecedented real-time tracking and visualization.<fnref n="8" /> Sensors, we should note, are significant to Smart Environments in large part because of their ability to “fragment” environmental spaces and processes into “small spatiotemporal units of data,” effectively making them amenable to new governance directives.<fnref n="9" /> Smart Environments initiatives also frequently combine well-established approaches—such as remote sensing and long-term ecological monitoring—with newer technologies (animal biotelemetry, bacteria-based biosensors) and modalities of data collection (such as drones, Google Earth, and citizen sensing).<fnref n="10" /> The most frequent targets of these applications are natural resources (such as forests), species prioritized for conservation (such as marine mammals), earth “boundary conditions” and “ecosystem services” (such as freshwater, atmospheric carbon), and environmental security (natural disasters such as floods and fires).

A quick survey into the case of acoustics can bring further clarification into the resultant changes. Scientists have long recognized sound as “a medium for intra- and interspecific communication among . . . animals.” Their socio-technical ability to glean biological and ecological insight was limited to site-specific recording engagements (Krause and Farina 2016, p. 245). For a long time, they had difficulties ascertaining the biological and ecological value of sound, owing to the perceived absence of measurable variables to compare across spaces. The rise of a digitally enabled “conservation acoustics,” premised on the novel availability of cheap recording technologies, algorithms, and novel forms of expertise, has completely transformed this situation. Today, a range of Smart Environments technologies—hydrophones, acoustical observatories, audio moths—are being used across a range of conservation projects and governance projects, fixated on everything from elephant calls to poacher gunfire.<fnref n="11" /> New research is asserting that carbon-sharing trees exchange acoustic signals and that kelp forests exhibit distinct lunar and seasonal trends in sound production.<fnref n="12" /> Globally, the bioacoustics sensor market is expected to reach 8.7 billion (USD) by 2024, up from 5.6 billion in 2017.<fnref n="13" />

New technologies are at the forefront of the interest in digital sound for terrestrial environmental governance. But equally paradigmatic examples of Smart Environments technologies redefining governance can be found in marine space.<fnref n="14" /> EarthNC’s (2016) SharkNet, which displays the near-real-time locations of great white sharks and marine mammals, gathering data from a network of mobile robots and moored listening stations connected to sensors carried by marine wildlife.<fnref n="15" />

<footnote n="8">⁸ See Gale et al. (2017).</footnote>

<footnote n="9">⁹ Luque-Alaya and Marvin (2020), p. 6.</footnote>

<footnote n="10">¹⁰ Bakker and Ritts (2018).</footnote>

<footnote n="11">¹¹ See Ritts et al. (2016); Roe et al. (2021a); Ritts and Bakker (2021); Ritts et al. (2024).</footnote>

<footnote n="12">¹² Klein et al. (2016); Butler et al. (2021); see also Bakker (2022b).</footnote>

<footnote n="13">¹³ As discussed in Ritts and Bakker (2021).</footnote>

<footnote n="14">¹⁴ The literature here is extensive. For a few examples from the geography discipline, see Lehman (2020); Havice et al. (2022); Bakker (2022a).</footnote>

<footnote n="15">¹⁵ www.sharknet.com.</footnote>
Like many marine animal tracking efforts, SharkNet emerged from long-standing collaboration among scientists interested in monitoring animals across large spatial ranges. Related “citizen sensing” projects have geotagged fish, such as important commercial species like Pacific salmon, whose movements can likewise be detected by networks of underwater cameras.<fnref n="16" /> Although the focus of Smart Environments has tended to be on terrestrial processes, ocean environments have also been the focus of various kinds of technical, logistical, and institutional innovation.<fnref n="17" />

As Lehman emphasizes, “new sensing technologies do not simply produce more data at more ambitious spatial and temporal scales. They have the potential to produce new environmental sensibilities as well as new relationships between scientists, technologies, and environments.”<fnref n="18" /> Across a range of ecosystems and disciplines, scientists and engineers have created new devices to assess changing oceanographic conditions, including an automated network christened “FitBit for the Oceans.”<fnref n="19" /> The network incorporates cable and sensor technologies to measure geological, physical, chemical, and biological variables in the ocean and seafloor, producing over 200 different kinds of data. These marine developments are emblematic of the ways in which Smart Environments technologies enable comprehensive data acquisition infrastructures to monitor environmental conditions, emergent risks, and geo-hazards.<fnref n="20" />

Another salient finding from the review of Smart Environments technologies is the variability of scales at which they are being conceived, defined, and operated. Scale is critical in assessing Smart Environments because of the spatial variability of the ecological processes involved in global environmental governance. For instance, the designers of BeachObserver propose that coastal communities “self-monitor” locally occurring marine debris, whereas projects like Argos aim for systemic reviews of marine-ecological change occurring at the planetary scale.<fnref n="21" /> Meanwhile, continent-wide scale networks of digital acoustic monitoring are now being conceived, as is exemplified in the case of Australian Acoustic Observatory.<fnref n="22" /> A single review is unable to capture all the scalar variability in Smart Environments, and certain projects (BirdTracker) should also be recognizing for straddling multiple scales concurrently. However, it is important for assessing the political risks of Smart Environments technologies that the challenges of integrating technologies operating at multiple and distinct scales be at least considered, which we seek to do below.

<footnote n="16">¹⁶ Matabos et al. (2017); see also Bakker (2022a).</footnote>

<footnote n="17">¹⁷ For examples, Favali et al. (2015).</footnote>

<footnote n="18">¹⁸ Lehman (2020), p. 172.</footnote>

<footnote n="19">¹⁹ Quoted in Ocean Observatories (2023). Unpaginated.</footnote>

<footnote n="20">²⁰ See Bakker and Ritts (2018); Lehman (2016, 2020); Lindenmayer et al. (2017).</footnote>

<footnote n="21">²¹ See Benson (2015). For BeachObserver, see https://scistarter.org/beachobserver.</footnote>

<footnote n="22">²² Roe et al. (2021b).</footnote>
## 5.3 Environmental Governance in a Smart Environments World: Issues and Critiques

This section of the chapter overviews some of prevailing critiques of Smart Environments for environmental governance. Interest in this area has grown rapidly in recent years; due to space restrictions, we confined our analysis to five themes: data, “real-time” analysis and regulation, the changing nature of prediction, the meaning and extent of “open data,” and the role of non-scientists, notably “citizen sensors.”

### 5.3.1 Data

Data, Kitchin suggests, refer to any element that can be abstracted from phenomena.<fnref n="23" /> What is helpful about this definition is how it captures a key aspect of Smart Environmental technologies: a capacious, seemingly indiscriminate, and hence socially problematic interest in sourcing of environmental data. Exemplifying this tendency, between 2018 and 2023, the volume of data from NASA’s Earth Observing System Data and Information System grew from 26.8 petabytes to 151.1 petabytes.<fnref n="24" /> The trend is indexical of a larger insight, e.g., that a world of Smart Environments is a world in which planetary data is becoming more abundant than existing supercomputers—and, perhaps, ecologies—can handle. After years of little coverage, massive server farms, with energy-demands constituent of a “data center industrial complex,” have become the recognized underbelly of seemingly frictionless Smart Environmental space.<fnref n="25" /> Numerous actors are asking how can the petabytes—or even exabytes—of data systems be stored, sorted, summoned, and processed in such a manner that efficiencies are not lost in the cost of hosting the data? Another issue is the dependency on massive amounts energy and electricity, an issue made plain in the case of neural networks.<fnref n="26" /> Cubitt’s claim that insufficient electricity, not oil, is the leading threat to co-ordinations of global governance takes on added salience here.<fnref n="27" /> Costly to train and develop from an energy standpoint (e.g., hardware costs, cloud compute) and resulting in massive carbon footprints (e.g., to fuel modern tensor processing hardware), “data deluge” has spurred a kind of new self-reflexive green energy focus: the sustainable data center management strategy.<fnref n="28" />

<footnote n="23">²³ Kitchin (2014), p. 2.</footnote>

<footnote n="24">²⁴ NASA (2023).</footnote>

<footnote n="25">²⁵ “Data center industrial complex” is the title of Hogan (2021).</footnote>

<footnote n="26">²⁶ Strubell et al. (2019).</footnote>

<footnote n="27">²⁷ See Cubitt (2017).</footnote>

<footnote n="28">²⁸ For an example, see Crouse (2023).</footnote>
As scientists and innovators continue to focus on automated decision-making systems, issues of resource use will only intensify. One sector in which the growing embrace of automated management techniques deserves special mention is “precision agriculture,” an agenda that promises that digitizing agricultural processes can, on average, yield greater volumes more food, on less land, and with fewer inputs, through the intercession of Internet of Things technologies.<fnref n="29" /> However, multiple real-time data streams and interoperable technologies have not necessarily engendered more efficient or transparent agricultural systems— in part because proprietary relations over the data collected mitigates against the pooling of knowledge and even the repair of farming machinery.<fnref n="30" /> Digitalization can exacerbate already-existing power inequities in the food system and increase corporate control over an already centralized economy.<fnref n="31" />

Galaz and Mouazen highlight a revealing paradox: many of the most powerful algorithms underlying automated decision-making systems featured in Smart Environments are likely to be of limited accessibility or transparency—raising the risk of replication of biases in decision-making.<fnref n="32" /> As many scholars have pointed out, AI and machine-learning-driven approaches often contain implicit bias; incomplete datasets and flawed algorithms can prove counterproductive and ecologically damaging.<fnref n="33" /> Moreover, algorithms do not reconcile value-laden tensions between competing uses and ecosystem services (e.g., economic versus spiritual values), leaving the question of arbitration incomplete. Perhaps counterintuitively, algorithmic modes of environmental governance can be expected to create the need for new forms of human supervision as the automation of decision-making becomes more commonplace.<fnref n="34" />

A related issue that threatens the success (if not the rollout) of Smart Environments is the lack of data standards. Our meta-review revealed persistent appeals for cross-disciplinary collaboration on data standards and data sharing.<fnref n="35" /> It is thus surprising to note that “silo-ing” persists across academic uptakes of Smart Environments. This can be partially attributed to the long-standing challenges of combining ecological datasets. Pre-ICT approaches were characterized by a diversity of standards, for example, one set of standards applied to the longer-term data gathered by professional scientists (often for the purposes of testing single hypotheses) while another set of standards often applied to shorter-term data collected by professional environmental consultants in response to specific

<footnote n="29">²⁹ Clapp and Ruder (2020).</footnote>

<footnote n="30">³⁰ For the now-infamous example involving John Deere tractors, see Wiens and Chamberlain (2018).</footnote>

<footnote n="31">³¹ Rotz et al. (2019).</footnote>

<footnote n="32">³² Galaz and Mouazen (2017).</footnote>

<footnote n="33">³³ See Caliskan et al. (2017); Galaz and Mouazen (2017); Machen and Nost (2021).</footnote>

<footnote n="34">³⁴ Galaz and Mouazen (2017), p. 629.</footnote>

<footnote n="35">³⁵ The issue has long been recognized as salient to environmental governance. See Frew and Dozier (2012); Hampton et al. (2013); Michener and Jones (2012); Michener (2015).</footnote>
ecological threats (such as pollution incidents) and distinct (or no) standards were applied to data collected by citizen scientists (such as fish and wildlife harvest data), which was housed separately, and rarely accessed or used by scientists or governments.<fnref n="36" /> The challenge of combining and communicating data gathered through disparate data collection efforts remains unresolved, although significant efforts have been made to address this issue by a variety of organizations, including NASA's Socioeconomic Data and Applications Center (SEDAC, a Data Center in NASA's Earth Observing System Data and Information System (EOSDIS)), The Amazon Tall Tower Observatory (ATTO), and the National Ecological Observatory Network.

This data-sharing challenge may nevertheless be largely resolved in the near-term future. “Big ecology” policies, combined with diminished costs for information technologies and new cloud-based data-archiving tools and repositories, have proliferated in recent years, supported by an increasing number of eco-informatics scientists and also by organizations such as the Ecological Society of America’s Committee on the Future of Long-term Ecological Data (FLED).<fnref n="37" /> Scholars working in ecological informatics have longed predicted that new generations of data-sharing networks will grow exponentially faster than its predecessors.<fnref n="38" /> Attest-ing to these claims is the case of the US Long Term Ecological Research Network (LTER), which, after several decades of operation, had 6000 shared datasets.<fnref n="39" /> In contrast, by mid-2022, after less than a decade and a half of operation, DataONE easily exceeded one million data objects.<fnref n="40" /> This growth has been facilitated by the expansion of networking capacities among researchers, along with corresponding changes in the cyberinfrastructure landscape. Important data science innovations—including data and metadata standards, persistent identifiers, and search/discovery tools—have enabled more widespread data sharing than in the past.<fnref n="41" />

In summary, much of the Smart Environments literature focuses on addressing data gaps, the need for more collaborative data sharing, and issues relating to data quality. The underlying assumption is that more comprehensive and higher-quality data will lead to more effective environmental governance. As a wealth of literature in science and technology studies shows, however, this assumption is problematic.<fnref n="42" /> For one thing, it largely proceeds according to a technocratic worldview, which holds that solutions exist for problems that may in fact be irresolvable under present institutional, political, and technological configurations.<fnref n="43" /> The political commitments associated with measurement-related decisions—in particular, the

<footnote n="36">³⁶ The foundational piece here remains Goodchild (2007).</footnote>

<footnote n="37">³⁷ See Porter (2010); Michener (2015).</footnote>

<footnote n="38">³⁸ Michener (2015); Porter (2010); Reichman et al. (2011).</footnote>

<footnote n="39">³⁹ Porter (2010).</footnote>

<footnote n="40">⁴⁰ https://www.dataone.org.</footnote>

<footnote n="41">⁴¹ Michener (2015).</footnote>

<footnote n="42">⁴² Gabrys (2016).</footnote>

<footnote n="43">⁴³ Morozov (2013).</footnote>
question of who selects variables to measure and for whom they are selected—
are rarely discussed in the large institutional settings overseeing the use of Smart
Environments technologies, like the World Economic Forum.<fnref n="44" /> Because data gaps
are likely to result from the uneven political economies that preside over governance
in a Smart Environments world, questions of “what is measured, matters” or “what
is counted, counts” (and, by extension, what is not counted, doesn’t) are likely to
intensify.

5.3.2 Real-Time Regulation

The concept of “real” or “near real” time–e.g., the increasingly instantaneous “actual” time elapsed in the performance of a computation—is central to Smart Environmental governance.<fnref n="45" /> Interest in this area can be traced to the techno-managerial discourses of late 1990s urban policy.<fnref n="46" /> Initial focus includes real-time pricing in “Smart Grids” and “Smart” water distribution systems.<fnref n="47" /> In the last decade, a rapid decline in the cost of monitoring technologies (driven by innovation in computing and communications) has increased the capacity to conduct real-time assessment of environmental changes.<fnref n="48" /> Managers are evaluating the success of real-time location information via software applications on location-aware devices, such as cellphones, laptops, acoustic sensors, and apps.<fnref n="49" /> The implementation of updatable navigational hazards and “real-time speed advisory signs” in transportation systems is now common in larger environmental governance contexts.<fnref n="50" /> Prominent applications include marine governance, where ship rerouting efforts proceed in connection in response to wildlife detections in a number of contexts, and pipeline logistics, wherein materials flows are routinely rerouted into different distribution terminals across vast interregional logistics networks.<fnref n="51" />

A paradigmatic example is the work of Conserve.IO, an organization that assists conservation groups with leveraging mobile, cloud-based, and big data technologies through data collection at scale (involving crowdsourced data from humans and from passive environmental sensors). The mobile applications (“apps”) produced by Conserve.IO—such as its award-winning “Sharktivity” (2020)—employ visual analytics to enhance situational awareness and real-time responses to environmental

<footnote n="44">⁴⁴ For example, see World Economic Forum (2021).</footnote>

<footnote n="45">⁴⁵ De Longueville et al. (2010).</footnote>

<footnote n="46">⁴⁶ See Komninos (2002); Luque-Alaya and Marvin (2020).</footnote>

<footnote n="47">⁴⁷ See Kratz et al. (2006); Arts et al. (2015).</footnote>

<footnote n="48">⁴⁸ Koomey et al. (2013).</footnote>

<footnote n="49">⁴⁹ Goodchild and Glennon (2010); Jepson and Ladle (2015).</footnote>

<footnote n="50">⁵⁰ Haque et al. (2013), p. 25.</footnote>

<footnote n="51">⁵¹ See Bakker (2023); Ritts and Simpson (2023).</footnote>
changes.<fnref n="52" /> In what has become a common tactic in an age of ubiquitous computing, “Whale Trail” aggregates different smartphone-user submissions of a single species encounter to calculate a highly accurate track using intersection points.<fnref n="53" />

Whereas management and critical policy literatures focus on real-time resource distribution concerns—e.g., coordinating flows of energy, bodies, and commodities—ecologists and biologists are engaging novel possibilities for species “tracking” (Ritts and Bakker 2021). Thanks to advances in predictive analytics, tracking technologies can be used to pursue, distill, and anticipate real-time ecological events. Sensing technologies, such as conservation drones, camera traps, and acoustic recorders, are being presented as adaptive solutions for a host of “monitoring-adjacent” activities, such as anti-poaching and law enforcement.<fnref n="54" /> Rather than static sensing regimes, large-scale observatories—such as the Australian Acoustic Observatory—typically host shifting regimes of sensor arrays that appeal to multiple research communities (Roe et al. 2021a).

The challenge (and opportunity) posed by real-time data streams is one of the most salient issues for environmental governance under Smart Environments. Real-time data streams and real-time analysis make the idea of “real-time” regulation possible: e.g., the capacity to rapidly shift resources and monitoring capacities in response to unforeseen developments. For example, Little et al. (2015) survey real-time spatial management approaches to reduce fisheries bycatch and discards. Kumar et al. (2015) explore the rise of low-cost sensing for managing urban air pollution. The developments they capture likewise sound a note of caution. Real-time regulation poses significant administrative challenges, insofar as organizing simultaneous temporal attributes (or tracking efforts), including time of acquisition, integration/dwell time, sampling interval, and aggregation time span, can easily overwhelm computing power or lead to insufficient data.<fnref n="55" /> It is likewise impossible to guarantee that ecological wellbeing will form the basis of real-time responses, which may in fact embolden more tactical political decision-making favoring certain actors.

### 5.3.3 Prediction

Many papers discussing Smart Environments highlight the benefits of predicting conditions for sustainable development and resource use. In distinction from previous approaches to predictability, ecological changes are increasingly conceived as predictable and “programmable.”<fnref n="56" /> This innovative emphasis on programma-

<footnote n="52">⁵² https://www.atlanticwhiteshark.org/sharktivity-app</footnote>

<footnote n="53">⁵³ Meynecke and Liebish (2021).</footnote>

<footnote n="54">⁵⁴ See Kang and Hudson (2022); Ritts and Bakker (2021); Sandbrook (2015); Ritts et al. (2024).</footnote>

<footnote n="55">⁵⁵ Frew and Dozier (2012); Benson (2015).</footnote>

<footnote n="56">⁵⁶ Frew and Dozier (2012); Gabrys (2016); Murai (2010).</footnote>
bility as an inherent—and in some formulations, paramount—characteristic of predictability builds on developments in “adaptive monitoring” and “adaptive management.”<fnref n="57" /> For example, many publications provide examples of enhancements to predictive capacities enabled by new Web platforms, such as OakMapper, Whale Alert, and Global Forest Watch.<fnref n="58" /> Managers and scientists alike can source data inputs for environmental niche models, which are able to predict risks, disturbances, and terrestrial transformations.<fnref n="59" />

Smart Environments also create innovative possibilities for predictability for an expanded set of environmental variables. For example, the literature has many examples of novel prediction capabilities across environmental phenomena, which were previously characterized by limited predictability—with seasonal whale migrations and the sonification of advancing “P-Waves” being two prominent examples.<fnref n="60" /> Prediction has also garnered considerable attention in remote sensing where new measurement techniques and modeling and visualization capacities are being used to describe and predict local weather patterns in the context of climate change, species diversity estimates, lake monitoring, and management priorities.<fnref n="61" /> There are a range of studies centered on the prediction, with increasingly high degrees of accuracy, of changing forest structure and aboveground biomass, food web structure, and animal migration.<fnref n="62" /> Yet despite their potential, the actual success rate of predictive efforts has been questioned. Such determinations will have to contend with unexpected developments in price structures and consumption patterns, among other variables. Predictive capacities are necessarily limited by the present-day assumptions of their programmers, and they are often unable to internalize challenges that arise outside their framing contexts.<fnref n="63" /> The challenge of flexibility alongside predictive capacity can be expected to loom as a major tension in Smart Environments governance moving forward.

In short, Smart Environments enable new modes of prediction, which create new possibilities for environmental governance. Adaptation and anticipation have become more central to environmental governance in the increasingly predicted (if not necessarily more predictable) “time-space” of a Smart Environments world. These developments align with current debates over the implications of what Mahony calls the “predictive state” and associated forms of geographical intelligence.<fnref n="64" /> Carruth and Marzec join others in focusing concern on ethical issues

<footnote n="57">⁵⁷ Carruth and Marzec (2014); Lindenmayer et al. (2017).</footnote>

<footnote n="58">⁵⁸ See https://oakmapper.org, https://whale-alert.io, https://pro.globalforestwatch.org.</footnote>

<footnote n="59">⁵⁹ Kluza et al. (2007); Clark et al. (2009).</footnote>

<footnote n="60">⁶⁰ For discussion, see Pötzsch (2015); Gale et al. (2017).</footnote>

<footnote n="61">⁶¹ Mairota et al. (2015); Rocchini et al. (2015); Dörnhöfer and Oppelt (2016); Pettorelli et al. (2014).</footnote>

<footnote n="62">⁶² Woodward et al. (2013).</footnote>

<footnote n="63">⁶³ Amoore (2021).</footnote>

<footnote n="64">⁶⁴ See Mahony (2014). For more discussion, see Crampton et al. (2013); Wood (2013); Thatcher (2014).</footnote>
of privacy, freedom, and security.<fnref n="65" /> Cubitt’s remark that “databases predict the predictable” is a reminder that certain ecological forms and processes may be excluded under automated tracking systems.<fnref n="66" />

### 5.3.4 Open Source

A Smart Environments world abounds in rapidly circulating, often insufficiently labelled, “open-source” data.<fnref n="67" /> In the ecological sciences, there is now a pervasive conviction that biodiversity conservation will be augmented by the provision of open-access data.<fnref n="68" /> Projects like eBird tout the value of sharing small, localized citizen-science-based observations, which, when aggregated, propose broader understandings of ecological phenomena. The “Air Quality Egg” project, an EU-supported effort to collaboratively devise a “smart” air quality sensor network, is being proclaimed as a best-practice example of bottom-up environmental governance.<fnref n="69" /> In contrast, “Open source” is defined by other scholars to facilitate customization in environmental governance ensuring the local determination of environmental decision-making at a time of planetary-scale organization.<fnref n="70" /> At its most utopian, open source proposes that “ubiquitously available” data can serve as both a key feature of environmental governance and an “essential component of democracy.”<fnref n="71" />

Efforts to survey the dizzying array of open-source archives currently engaged within governments, research projects, and NGOs have resulted in several detailed reviews.<fnref n="72" /> Michener examines how “Big Data” informational policies have historically encouraged the present deluge of open-source data—noting in particular the foundational significance of Long-Term Research Networks (LTRNs), Ecological Observatory Networks (EONs), and Coordinated Distributed Experiments and Observations Networks (CDEOs).<fnref n="73" /> Despite the fact that scientists have long used decentralized groups of non-professionals to gather ecological information, many observers note that recent “success stories” remain without truly democratic cultures of “collaboration” and “sharing.”<fnref n="74" /> Collaboration and data sharing as such remain considerably removed from the actual opportunities enabled by Smart

<footnote n="65">⁶⁵ Carruth and Marzec (2014). See also Wood (2013); DeLoughrey (2014).</footnote>

<footnote n="66">⁶⁶ Cubitt (2017), p. 159.</footnote>

<footnote n="67">⁶⁷ See Gunningham and Holley (2016); Mairota et al. (2015); Rocchini et al. (2017).</footnote>

<footnote n="68">⁶⁸ Morris and White (2013); Turner et al. (2015).</footnote>

<footnote n="69">⁶⁹ As discussed in Zandbergen (2017).</footnote>

<footnote n="70">⁷⁰ Gale et al. (2017).</footnote>

<footnote n="71">⁷¹ Mooney and Corcoran (2014), p. 534.</footnote>

<footnote n="72">⁷² Roche et al. (2015); Gale et al. (2017).</footnote>

<footnote n="73">⁷³ Michener (2015).</footnote>

<footnote n="74">⁷⁴ Connors et al. (2012); Faniel and Zimmerman (2011).</footnote>
Environments.<fnref n="75" /> Reviews of marine-based sciences have made similar observations as have conversations within environmental and digital humanities.<fnref n="76" /> A commonly raised concern is the absence of proper institutional support for collaborative data sharing, which in certain cases might translate into prescriptions for greater incentives to share, as well as the embrace of novel administrative frameworks.<fnref n="77" /> Humanities scholars have been particularly keen to critique “crowdsourcing” as a source of ecological knowledge that tends to reinforce the expert hierarchies it proposes to disable.<fnref n="78" />

For many observers, open-source data leads inexorably to the problem of data standards, an issue with far-reaching consequence to Smart Environments environmental governance. Roche et al. surveyed 100 datasets associated with studies in journals that commonly publish ecological and evolutionary research, finding that 56% of the articles were linked to incompletely archived datasets.<fnref n="79" /> Calls for improved metadata have become increasingly common in ecology and biodiversity science.<fnref n="80" /> “Good news narratives,” showcasing the purported benefits of Smart Environments technologies, often obscure questions of quality controls and who will set them.<fnref n="81" /> The question of just what constitutes “good enough data” prefigures a growing politics of “open-source” legitimacy in Smart Environments environmental governance.<fnref n="82" /> At a time when increasing amounts of data freely circulate, and many scientists advocate for the continued diversification of research models, the integration of data from multiple sensors poses institutional challenges and social tensions. Moreover, what role do different data sources play in the actual conduct of environmental governance? And what about the related issue of expanding risks occurring from institutional propensities to exploit datasets in ways original architects had not intended? Sun-ha Hong has coined the term “control creep” to describe how “data-driven technologies tend to be pitched for a particular context and purpose, but quickly expand into new forms of control.”<fnref n="83" /> As incentives to monetize environmental data in new ways become knowable, the ability to not only ensure privacy and personal freedoms, but the provenance of data for broader environmental applications (such as large-scale worker-monitoring), is impacted. The actual provision of open-source data articulates a fundamental ambiguity of the Smart Environments governance regime: are data flows simply feeding into the

<footnote n="75">⁷⁵ Reichman et al. (2011); Hampton et al. (2013); Volk et al. (2014).</footnote>

<footnote n="76">⁷⁶ Cubitt (2017).</footnote>

<footnote n="77">⁷⁷ Michener and Jones (2012); Roche et al. (2015); Specht et al. (2015); Hampton et al. (2013); Verburg et al. (2016).</footnote>

<footnote n="78">⁷⁸ Gabrys (2016); Swanstrom (2016); Pearson et al. (2016).</footnote>

<footnote n="79">⁷⁹ Roche et al. (2015).</footnote>

<footnote n="80">⁸⁰ Frew and Dozier (2012); Specht et al. (2015); O’Brien et al. (2016).</footnote>

<footnote n="81">⁸¹ Quoted in Arts et al. (2015), p. 661.</footnote>

<footnote n="82">⁸² Gabrys et al. (2016).</footnote>

<footnote n="83">⁸³ Hong (2018), p. 1.</footnote>
predetermined interests of large multinationals, or are they truly a site of continual
“bottom-up” modifications and “hacker-led” transformations?<sup>84</sup>

5.3.5 Citizen Science/Sensing

Smart Environments engage a broad set of agencies, species, and even biophys-
ical materials. Researchers in disciplines as diverse as computer engineering,
oceanography, political science, and architecture utilize its associate discourses and
concepts. “Crowdsourcing” is the operative term of bundling these entities together,
increasingly in contexts that would otherwise have involved the state (e.g., traffic
monitoring).<sup>85</sup> Scholars are increasingly exploring the contributions of artificial
intelligence (AI) in this regard, invoking parallels with related transitions from
“Web 2.0” to “Web 3.0,” in which environmental governance is enabled by a
connective intelligence across sensors, humans, non-humans, data, and decision-
making apps.<sup>86</sup>

This transition will be intensified by Smart Environment’s multiplication of sens-
ing practices across different spaces and scales of governance. Recent innovations
create the potential for universal biotic sensors, in which every organism potentially
performs as a sensor to be integrated into an array of data hubs and initiatives
(such as the Sensor Web, Participatory Geoweb initiatives, and Google Earth).<sup>87</sup>
Smart Environments research agendas deploy many technologies that make use
of the filtering and transducing capacities of biosensors (eyes, ears, skin, etc.),
as well as providing actionable data to users (via geospatial visualizations and
geo-visual analytics).<sup>88</sup> In short, Smart Environments expand sensing modalities—
tactile, auditory, and even olfactory—by both humans and non-humans.

The most widespread application of this technology to date involves projects
of “citizen sensing,” e.g., the intimate, experiential monitoring of environments
by human and non-human users. Interest in this area has only intensified in
the wake of the Covid-19 pandemic, which has deposited an array of extensive
experiments in user-produced location health data across national territories and
jurisdictions. Often, citizen sensing builds on existing citizen science initiatives,
including those administered by non-governmental groups like the Citizen Science
Center and government agencies like the Environmental Protection Agency.<sup>89</sup> For
Gabrys, “citizen sensing” is a subset of the broader phenomenon of citizen science:

<footnote n="84">⁸⁴ This is part of a much larger debate on big data and society. See Crampton et al. (2013); Crampton et al. (2014); Wood (2013); Hemmi and Graham (2014).</footnote>

<footnote n="85">⁸⁵ Liu (2021).</footnote>

<footnote n="86">⁸⁶ Dauvergne (2021).</footnote>

<footnote n="87">⁸⁷ Goodchild (2007); Huang and Liang (2014).</footnote>

<footnote n="88">⁸⁸ Helbig et al. (2017); Khan et al. (2013).</footnote>

<footnote n="89">⁸⁹ Citizen Science Center (2017).</footnote>
"research projects in which the public is enlisted in scientific endeavors."<fnref n="90" /> In Smart Environments, citizen sensing has become a privileged means by which IT-enhanced data collection, learning, decision-making, and participation scale up from discrete local encounters to governance initiatives. To improve the quality and usefulness of citizen-sensed data, and to validate it against third-party critiques, scientists are now devoting vast resources to automation, which has evolved out of efforts to compare the data outputs of practitioners, algorithms, and non-experts.<fnref n="91" />

The impress of Smart Environments technologies is revealing for the way such “citizen sensing” activities are oftentimes solicited by and dependent on the coordinating force of state-institutions. Coastline areas are undergoing rapid change and are increasingly populated by new environmental “risks” (e.g., marine debris, oil spills, whale-vessel strikes). In North America’s Pacific Northwest, coastal residents have become central in efforts to construct the “Smartest Coast in the World.”<fnref n="92" /> Likewise, the Chinese context reveals growing efforts to utilize citizen capacities to improve “overall marine operational situational awareness” (Heesemann et al. 2014, p. 153; See: Guo et al. 2010, 2017). Through sensing, key questions of citizenship become articulated to discrete capacities to generate environmental knowledge from different ecological processes (see Gabrys 2016). For citizens, the opportunity to “sense” and not merely “collects” data is a highly relevant enticement within these schemes. Sensing becomes a privileged means to attract and intrigue non-expert researchers about changing littoral regions. For example, the Ocean Observatories Initiative (OOI) entrains viewers to their Web sites to listen to underwater sounds captured by hydrophones in real time: both a means to fundraise and an opportunity to build network effects.

The worldwide growth of Smart Environment technologies potentially converts every citizen into an environmental sensing device (Elwood and Leszczynski 2013; Georgiadou et al. 2014; Amoore 2020). This raises questions that cut to the heart of widespread societal concerns over data privacy and individual liberties. What does it mean to subject racialized bodies to predetermined repertoires of “smart” practices? How are such determinations inflected by the ongoing histories of profiling, devaluation, and police violence (Jefferson 2020)? Insofar as citizen sensing efforts presuppose liberal conceptions of citizenship, they double as a new means for digitalized exclusion and worse. How might the resulting conflictual forms of citizen science be managed via Smart Environments environmental governance?

Privacy issues are also significant. Smart Environments are defined by “extra-political” protocol and models of citizen-activated management—algorithmic systems that bypass standard forms of representation and even legibility.<fnref n="93" /> For example, in projects like IBM’s “A Smarter Planet” and Hewlett-Packard’s CeNSE,

<footnote n="90">⁹⁰ Gabrys (2016), p. 212.</footnote>

<footnote n="91">⁹¹ For a “hopeful” application of these tools, see Matabos et al. (2017). For a general critique, see Andrejevic (2019).</footnote>

<footnote n="92">⁹² Ritts and Simpson (2023).</footnote>

<footnote n="93">⁹³ Amoore (2020).</footnote>
individual citizens increasingly operate as essential operational and functional elements of environmental regulation. In transforming citizenship into citizen sensing, the public becomes a constitutive element of an emerging “computational apparatus.”<fnref n="94" /> Citizens interacting with Smart Environment technologies will voluntarily submit to surveillance, providing data while having their online actions thoroughly indexed. Such “non-voluntary” systems of locational disclosure are built into many applications that individuals implicitly endorse to when participating in Smart Environments—something the Pegasus “spyware” scandal made abundantly clear.<fnref n="95" /> Because such disclosures cannot easily be controlled by individuals through settings adjustments to any one device, volunteered geo-data about Earth Processes can become an unintended byproduct, resulting in new forms of “geosurveillance,” including by state security organizations and private contractors.<fnref n="96" /> There is thus potential for exploitative modes of recruitment and usage if proper checks are not established.

## 5.4 Conclusion

As summarized here, Smart Environments technologies not only create conditions for significant shifts in environmental governance but appear to be having this effect across a range of environmental spaces today. This review has provided insight into some of the ensuing challenges, debates, and critiques. In focusing on relations of environmental governance and data, our analysis has emphasized the point that better data does not necessarily lead to better governance. Indeed, we have suggested that algorithms might selectively reduce the sphere of possible intervention and analysis within a particular landscape. Mitigating against this tendency for the purposes of robust citizenship as well as sustainable ecosystem management requires democratizing access to environmental information, as mediated by constantly evolving socio-technical relations. In parallel, new analysis of regulatory gaps is required, aligned with an analysis of the growth in integrative architectures currently being positioned as global frameworks for storing, analyzing, and disseminating Smart Environments data. In this context, a more comprehensive understanding of Smart Environments requires appreciating the changing nature of multi-actor and multilevel environmental governance—a key theme but one beyond the scope of this meta-review.

Another key gap in the literature is a critical analysis of the role of the state, historically a key player in facilitating multi-scalar processes of environmental change.<fnref n="97" /> While many states today are key promoters of Smart Environments tech-

<footnote n="94">⁹⁴ The phrase is used in Gabrys (2016). For CeNSE, see http://www.hp.com/united-states/do-amazing/cense_innovation.html. For “A Smarter Planet,” see https://public.dhe.ibm.com/partnerworld/pub/pdf/pw_program_overview_brochure.pdf</footnote>

<footnote n="95">⁹⁵ Timberg and Harwell (2021).</footnote>

<footnote n="96">⁹⁶ Kitchin (2014).</footnote>

<footnote n="97">⁹⁷ Robertson and Wainwright (2013).</footnote>
nologies, the question of how these trajectories emerge in the ways they do demands further scrutiny. In Canada, for instance, it is notable that the Smart Oceans™ initiative enjoys considerable state support and harmonizes both with regulatory goals in sustainable marine development and state-led efforts to commercialize Canada's marine technology sector.<fnref n="98" /> The government of China has also been very active in Digital Earth and related marine initiatives, but with combinations of regulatory goals and economic interests.<fnref n="99" /> Future work needs to critically evaluate how different state forms and political cultures enable, direct, and/or delimit Smart Environments processes across different geographical and cultural contexts. As such, much research is required into the emerging geopolitical dimensions of Smart Environments governance.

Questions of ethics also merit more scrutiny. Smart Environments governance implies a shift not only from “government to governance” but also from “manual to automated” eco-governance. Emergent regimes of state-sponsored surveillance consolidated around environmental big data—such as the Smart Oceans™ project noted above—are mobilizing in support of security objectives rather than equitable access or efficiency.<fnref n="100" /> Smart Environments raise other fundamental issues. Elderly residents and those unable to own a smartphone face diminished opportunities to participate in Smart Environments governance (Crawford 2021). These social inequalities could be exacerbated by expanding uptakes of Smart Environments governance. As Leszczynski explains: “Algorithmic governmentality cannot divest itself of actual realities of socio-spatial stratification to which the derivative is theoretically indifferent.”<fnref n="101" /> Finally, we must note the fundamental questions of energy and ecological wellbeing raised by Smart Environmental technologies. It is now well established that sensors, cables, and various other forms of hardware generate huge amounts of e-waste, an issue that will only intensify in the coming years.<fnref n="102" /> Critical case studies of these issues (and their possible solutions) are increasingly appearing in the literature.<fnref n="103" /> Many more will be needed.

Given these concerns, Galaz and Mouazen are well justified to call for a code of conduct (which they term a “bio-code”) that allows citizens and institutions an opportunity to take stock of the proliferation of new social relationships and ethical challenges created by Smart Environments’ forms of governance.<fnref n="104" /> Data-sharing policies and ecological measurements standards, key mechanisms by which Smart infrastructure attains the obscurity its planners routinely “seek,” require new forms of visibility in public education and debate.<fnref n="105" /> In this framing, ethics is not an

<footnote n="98">⁹⁸ Oceans Protection Plan (2016). For a critical overview, see: Ritts and Simpson (2023).</footnote>

<footnote n="99">⁹⁹ Guo (2012); Guo et al. (2017).</footnote>

<footnote n="100">¹⁰⁰ Zandbergen (2017); Amoore (2020); Jefferson (2020).</footnote>

<footnote n="101">¹⁰¹ Leszczynski (2015), p. 1693.</footnote>

<footnote n="102">¹⁰² Cubitt (2017); Hogan (2021).</footnote>

<footnote n="103">¹⁰³ See Reddy (2016).</footnote>

<footnote n="104">¹⁰⁴ Galaz and Mouazen (2017).</footnote>

<footnote n="105">¹⁰⁵ Jackson and Bobrow (2015), p. 1770.</footnote>
"afterthought" or addition to design but a crucial input across the life cycle of a given system—particularly one as ambitious and far-reaching as the Smart Environments agenda.

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<figure><img src="image_41.png" /></figure>

# Chapter 6
## I'm Afraid HAL Can't Do That: Your Smart Home Is Not That Kind of Existential Threat

Robin L. Zebrowski

**Abstract** This chapter starts at the cliché of the smart home that has gone rogue and introduces the question of whether these integrated, distributed systems can have ethical frameworks like human ethics that could prevent the science fictional trope of the evil, sentient house. I argue that such smart systems are not a threat on their own, because these kinds of integrated, distributed systems are not the kind of things that could be conscious, a precondition for having ethics like ours (and ethics like ours enable the possibility of being the kinds of things that could be evil). To make these arguments, I look to the history of AI/artificial consciousness and 4e cognition, concluding with the idea that our human ethics as designers and consumers of these systems is the real ethical concern with smart life systems.

**Keywords** AI · Machine consciousness · Smart home technologies · Embodied cognition · 4e cognition · Sentience

### 6.1 Introduction

Science fiction is filled with the trope of the smart house or smart dwelling, which eventually becomes the sentient house, and often then becomes evil or at least autonomous (in the truest sense of the word and therefore no longer under the control of its owners). There are several famous versions of this story, most notably HAL 9000 in *2001: A Space Odyssey*, who takes over the spaceship and attempts to murder the crewmembers to preserve its own functioning. This theme recurs throughout various storytelling media, including most notably short fiction like Bradbury's *The Veldt* and *There Will Come Soft Rains* (1950, 1951); television shows including *The Simpsons* ("Treehouse of Horror XI" 2001), *Futurama* ("Love and Rocket" 2002), and *The X Files* ("Ghost in the Machine" 1993); and even in a

<footnote>R. L. Zebrowski (✉)
Cognitive Science Program, Departments of Philosophy, Psychology, and Computer Science,
Beloit College, Beloit, WI, USA
e-mail: zebrowsr@beloit.edu</footnote>
Disney Channel film called *Smart House* (1999). Indeed, in so much fiction where a sentient house makes an appearance, one can more or less expect the house to act as a disembodied, but distributed, robot engaging in a terrifying uprising dystopia where the house tries to kill or control the human inhabitants (even in the Disney film).

The reality, however, is very different. (A terminological note: the terms “smart life technologies,” “distributed computing,” “pervasive computing,” and “ubiquitous computing” devices all have slightly different meanings. For the sake of this chapter, however, you can assume they are being used more or less interchangeably.) Some of these devices and technologies have made tremendous strides in adoption, at least in the USA, with something like 30 million households (25% of all American households in 2020) having an Amazon Alexa smart speaker in the home (Vincent 2021). And that’s only *one brand* of *one kind* of smart life technology. There are competing devices like the Apple HomePod and the Google Home systems that would widen the overall number of households in the USA with smart speakers and even more if we include devices like the Sonos, which can use Alexa or Google Home assistants. We could also count the 10 million users of Amazon’s Ring doorbell cameras in 2020 (Greer and Bonesteel 2022). But for many smart home devices, there are burgeoning options among brands and more appearing on the market each year. There are networked vacuums for your rugs, networked thermometers for your heating and cooling, smart locks for your doors, smart air purifiers, pet cameras, beds, refrigerators, ovens, coffee makers, scales, and more.

If you only engaged with fiction about smart homes and smart life technologies, you could be excused for believing that these integrated technologies are the kinds of things that might gain consciousness and rise up with the other robots in the apparently-inevitable (in fiction) robot uprising. But despite being the primary trope surrounding smart homes, disembodied consciousness is not something that anyone need worry about. This chapter will demonstrate that for empirical, historical, and ontological reasons, smart life technologies are not the kinds of systems, even networked together, that could become conscious. However, that doesn’t mean there is nothing to worry about with regard to these kinds of technologies. I will return to this point, encouraging thoughtful engagement with these systems, and speak at length about the real and potential harms that they enable, but robot uprising or your house becoming jealous of the attention you give your partner is absolutely not on that list.

## 6.2 Consciousness and AI: A Brief and Messy History

Since its inception, the project of AI in general has been split into two camps: on the one hand, (usage 1) AI is understood as a way of understanding human cognition better through replication. The idea is that if we can create something capable of the kinds of really quite interesting and adaptable general intelligence humans enjoy, we can finally better understand human psychology and the nature of minds more
broadly. This can be seen, in some ways, as merely a continuation of the project of psychology more broadly and cognitive psychology specifically. On the other hand, (usage 2) AI is sometimes understood as a goal in itself, a kind of mad-science pursuit of the re-creation of consciousness in a Frankenstein-like quest to simply create a mind because we can. Of course, lately, with massive amounts of money on the line for corporations and businesses, we see the term “AI” being used simply to mean (usage 3) “algorithm,” in a way that hides what the algorithms are and how they work. But this conflation of three very different uses of the terminology makes it hard to have a serious conversation about what integrated, distributed smart life AI systems might look like and how we should think about the design, dissemination, use, oversight, and regulation of such systems, when it’s often hard for laypersons to know how the language is being used.

A complete history of AI is not warranted here, nor would it be possible in a chapter-length text (see Dietrich et al. (2021a) for a recent book-length history and Dietrich et al. (2021b) for a shorter one). But in order to argue that there are empirical, historical, and ontological reasons to doubt that machine consciousness could even possibly arise in a smart home system, we must look at some of the ways philosophers, psychologists, neuroscientists, and other AI theorists have thought about minds across AI’s history. Most such histories would start with the Turing test and for good reasons: Turing (1950) was concerned not with trying to solve the problem of other minds, which had plagued philosophers for thousands of years, or trying to operationalize the concept of intelligence in a simple way, but was instead offering a way to side-step the problem. He was using what Dennett refers to as a “quick probe assumption” (1990, p. 50). In other words, Turing’s suggestion seemed to be that if some system (of any type) were able to carry on an unrestricted conversation about any topic at all, it could be used as a kind of shorthand for the other very many intelligent capacities the system must obviously have in order to be able to speak intelligently on so many subjects. And while the Turing test has long been out of fashion (yet still endlessly discussed), the recent successes of large language models (LLMs) have brought it back out, front and center, as a possible shorthand for whether a machine may be intelligent and, more interestingly, if it may reasonably be said to have a mind. Machines have been intelligent for as long as there have been machines; this should not be the framing of any serious debate. My calculator is more mathematically intelligent that even the best human mathematicians when performing the sorts of calculations it’s programmed for. The only way “can machines think” is an interesting question is when “think” comes appropriately pre-loaded with all the baggage we traditionally imbue it with including mindedness and, hence, consciousness, whatever it is that consciousness turns out to be. Consciousness carries a moral dimension that requires accommodation in a way that brute intelligence does not. And this distinction is part of why the terminology of “artificial intelligence” is such a mess.

The language here is historically muddy, so for the sake of clarity, when I talk about machine intelligence, or AI, I have in mind what has been called “Strong AI,” as in, a thing that thinks rather than one which models thought, or, more recently, “AGI (artificial general intelligence),” although this often carries with it the
undesirable baggage of the singularity or AI superintelligence, neither of which is
philosophically interesting or remotely likely. Neither of these terms quite captures
the truly interesting piece of this problem though, which is machine consciousness
(which corresponds to usage 2 outlined above).

Despite the recent (early 2020s) apparent successes (and extremely notable fail-
ures) of LLMs, history is not on their side when it comes to machine consciousness
(Bender et al. 2021; Dreyfus 1992; Dietrich et al. 2021a; Harnad 1990; Newman
et al. 2019; Birhane 2021). While it’s true that there is no widely agreed-upon
definition of consciousness, and certainly no useful tests of it beyond those born
of medical necessity, such as clinical definitions of brain death, many cognitive
scientists have tended to converge on Nagel’s (1974) keen observation that “an
organism has conscious mental states if and only if there is something that it is
like to *be* that organism—something it is like *for* the organism.” The possibility
of subjective, phenomenal experience, in the most neutral sense of those terms,
is what makes living beings minded, and its absence is what continues to make
machine intelligence comparatively uninteresting. A thorough discussion about the
controversies in consciousness studies would take many more pages (and volumes)
than possible here, but to head off some objections and clarify some notions, we
might locate the idea of consciousness being supported in this piece somewhere
in the area of the pragmatist or enactivist traditions. Dewey, for example, once
remarked that “Mind is primarily a verb” (Dewey 1934), in an effort to dissuade us
from the common ways of thinking about the mind as an object or a thing. Similarly,
Thompson (2007) sums up the enactive approach in part by arguing that “living
beings are autonomous agents that actively generate and maintain themselves, and
thereby also enact or bring forth their own cognitive domains,” and “cognition
is the exercise of skillful know-how in situated and embodied action” (13). The
thing that the pragmatists and enactivists try to show us is that thinking about
consciousness as some *thing* that gets added to cognition, some special magic sauce,
is part of what causes most of our philosophical and conceptual problems (but also
generates so much of our clever speculative fiction, the very reason we can make
sense of Gregor Samsa waking up in the body of a cockroach or a smart sentient
house suddenly developing romantic feelings for one of its occupants). Rather than
thinking of it as some magic substance, as our legacy from Descartes would have us
do, consciousness is most aptly described as a cluster (or even radial) concept: an
idea that picks out a number of properties, often overlapping, without considering
a list of necessary and sufficient conditions for inclusion in the concept. It denies
a central property to the phenomenon while acknowledging that many overlapping
features that tend to appear in systems are identified by the label. Wittgenstein’s
(1953) “family resemblances” are useful here, as is the idea of graded centrality that
Lakoff (1987) uses in discussing the structure of prototypical categories. It might be
that for consciousness you need certain kinds of perceptual capacities, and capacities
to enable action, and be comprised of allostasis<fnref n="1" />-seeking components, alongside a
whole host of other potential dynamics involving interaction between individual
and environment. What the cluster or radial concept idea buys us is that there isn't
an exact list of, for example, perceptual capacities needed. This reminds us that
across the animal kingdom, our perceptual capacities vary quite widely, and there
is a huge field of human variation as well. Visual perception may be important for
some ways of being conscious (including, but not limited to, visual consciousness—
having visual experience that you're aware of), but excluding it from a system
doesn't somehow preclude the system from being conscious; this would be utterly
absurd, as we have wide human variation in sensory and perceptual capacities but
all among obviously conscious beings. Any theory of consciousness that threatens
to rule out full and complete human beings with divergent ways of experiencing
the world must be immediately off the table. Thinking of consciousness this way
is fairly safe and would likely err on the side of over-attribution rather than under-
attribution, but it also allows for the idea that there are genuine edge cases that
are hard to classify and that require additional scrutiny to decide how well they do
or do not fit into the concept. This is why humans, including humans who speak
another language than I do and hence limit my capacity to communicate verbally,
remain safely within the concept of conscious beings, and dogs and squirrels and
even bats seem fairly obviously also members. Yet there seems no reason to believe
my desk or water bottle is even up for consideration outside of some sort of animism
or panpsychism, at least not in the way LLMs currently appear to be or distributed
smart house systems may one day be.

While denying LLMs any claim to consciousness probably should be uncontro-
versial, philosophers and cognitive scientists are not united on this and historically
never have been. This relates most obviously to the fact that the story about
consciousness laid out a paragraph earlier is not shared by all cognitive scientists.
Consciousness remains one of the least agreed-upon problems in philosophy,
because our epistemological and ontological access to varieties of it remains chal-
lenged. We historically believed (and reinforced through our academic philosophical
and psychological practices) that we had privileged access to our own minds and
were hopelessly cut off from accessing the minds of any others. (This, of course, is
less of a problem when we locate consciousness as a phenomenon in dynamics
rather than a hidden, internal, private experience.) Recent arguments, made by
Chalmers most notably, approach the problem from a reductionist stance and argue
that LLMs may, in fact, be a bit conscious under the right conditions (Chalmers
2023). Contrary to the claim above in which I state that consciousness is very likely
a cluster or radial concept, Chalmers instead considers what sorts of properties
might mark conscious systems and then applies those properties to LLMs, both
current and possible. He says we might consider a LLM to be conscious if there

<footnote n="1">¹ Allostasis here means something very similar to homeostasis or the body's seeking-after
equilibrium. Allostasis implies that it is seeking equilibrium among bodily stresses rather than
more routine imbalances.</footnote>
is some feature X, possessed by LLMs, and that we have reason to believe that a system with X is probably conscious. He goes on to offer a few candidates for X, namely, self-report, conversational ability, and intelligence, all of which I would argue might have been decent proxies for a judgment in the past, but surely must be abandoned in light of conversational ability as its own engineering telos in the absence of any other features of mindedness. He offers a fourth possible feature, "seems-conscious," which is even more problematic than the previous three when considered in the appropriate context. That context, of course, is a world where our judgments about what is and is not conscious have been built up both socially, culturally, and evolutionarily over extremely large periods of time embedded in sociocultural systems with other conscious beings, and even then we quite obviously still get it wrong, and historically have quite often. My students are often horrified to hear that Descartes, in the mid-1600s, believed animals to be little automata, their squeals of apparent pain actually being no more than the equivalent of squeaky gears. Plants are perhaps the modern day equivalent, currently the focus of questions about whether their capacities look enough like cognition to count as cognitive (Calvo and Lawrence 2023). But recent philosophy of mind is rife with similar claims, often centered around the capacity for language as a prerequisite for thought, and thus reserved only for humanity, and then only some subsets with appropriate language skills (e.g., Malcolm 1972; Fodor 1975; Dennett 1975, 1987).<fnref n="2" /> So even if we thought there might be a single property, possession of which indicates a system has consciousness, we must always remember all of our judgments about those properties historically have been deeply problematic, not only excluding the overwhelming majority of animal (and plant) species but also awarding consciousness only in lesser degrees to many actual humans, such as women or people with certain kinds of disabilities or certain entire races and cultures. And more specifically, our judgments have centered on language because we've considered it a uniquely human trait, making it an easy shorthand for systems that almost certainly have other features of the cluster or radial concept that is consciousness. If we're throwing out the richness of the concept and reducing it to a property that is necessarily present in a conscious system, language as an indicator is hugely, deeply, and irredeemably problematic. I would argue that his conclusion, "It's reasonable to have a significant credence that we'll have conscious LLM+s within a decade" (Chalmers 2023), is unwarranted from most of its starting assumptions.

But why should we care if LLMs specifically, or language-based AI systems in general, are conscious or not when what's at stake for our purposes here are

<footnote n="2">² Fodor's *The Language of Thought* (1975) is certainly the source of much of the language-based focus on AI throughout its history, focusing as it did on how linguistic structures, even before spoken language, are the source of human kinds of thought. Turing preceded Fodor, but Fodor legitimized the deep linguistic structures as precursors to thought itself. Dennett's claims (1987) are distinct in that they do not require thought to be linguistic in nature but instead attribute thinking only to systems with the capacity for higher-order representations or the ability to represent things self-consciously, a feature that arrives only with language.</footnote>
smart home devices? We should care because much of the interfacing that happens
between users and their devices is mediated by just such language model systems.
Furthermore, it seems likely that this will only increase as companies roll these
models into their search engines and continue to deploy them to interface all sorts
of human-facing systems (Kurian 2023; Leonardis 2023). No one would be tempted
to think their networked smart devices might be candidates for consciousness if they
weren't able to speak, deceptively or otherwise. What's at stake here is the central
debate in artificial intelligence over the last 70 years (at least). What enables, or
guarantees, that a system will be conscious, and what sorts of systems are even
candidates to be such systems? Language has always been, and continues to be,
central to this debate, even among people who argue that it clearly shouldn't be
(Browning and Lecun 2022).

The argument against language-only systems being the kinds of things that could
possibly have consciousness had a unique formulation in the symbol grounding
problem (Harnad 1990). Importantly, situating the problem here allows us to narrow
the scope of the question enough that we can then let back in integrated and smart
home devices and see if their distributed processing and tangible interfaces enable
the possibility of consciousness over and above the simpler language-only systems.
I will argue that they don't and that you have nothing to fear from HAL or your
networked refrigerator, at least as far as the hyperbolic robot uprising is concerned.

6.3 4e Cognition

Throughout the history of both cognitive science and artificial intelligence as proper
academic disciplines, there have been several distinct research programs that have
driven the field forward. They are competing waves of thought, although not tempo-
ral waves. Rather, we can imagine at least three or four distinct research programs
all moving along the same timeline, and what distinguishes them temporally is
only the fact that historically, different waves have been dominant in the field at
different times.<fnref n="3" /> This is a rough sketch with a lot of messiness not captured by this
visualization, but it helps to see that folks were pursuing very different underlying
systems of thought about what cognition and consciousness are from inside the
disciplines tasked with defining and modeling those things. (See Table 6.1).

Relevant here for our purposes is the third wave, often now referred to as 4e
cognition. Without a complete history, we can suffice with the driving philosophy:
that cognition is not symbol manipulation in the void and it is not mere neuroscience.
It denies reduction to either functional program or neural substrate. Instead, it insists

<footnote n="3">³ There is much debate about whether cognitive science actually has historical waves or not. Some people carve them up differently. You can find some interpretations in Lakoff and Johnson (1999), where they talk about it as first-generation and second-generation cognitive science. You can also find the three research programs laid out in a similar way to my own analysis in Thompson (2007).</footnote>
<table><caption>**Table 6.1** One way of representing the history of cognitive science and artificial intelligence through distinct research methodologies</caption><thead><tr><th>Wave</th><th>Research program focused on</th><th>Roughly dominant</th><th>In Cog. Sci.</th><th>In AI</th></tr></thead><tbody><tr><td>1</td><td>Symbolic representation</td><td>1950s–1990s</td><td>– Language of Thought<br>– Representational Theory of Mind<br>– Computational Theory of Mind</td><td>– GOFAI (“Good Old Fashioned Artificial Intelligence”; this is traditional rule-based programming)</td></tr><tr><td>2</td><td>Neural structure</td><td>1970s–1980; 2020–current</td><td>– Cognitive/ Computational Neuroscience</td><td>– ANNs (artificial neural networks)<br>– PDP (parallel distributed processing)<br>– Connectionism<br>– Deep learning</td></tr><tr><td>3</td><td>4e Cognition</td><td>2000s–current</td><td>– Dynamic systems<br>– Embodied cognition<br>– Extended mind</td><td>– Subsumption architecture<br>– Cognitive robotics</td></tr></tbody></table>

that embodiment is not merely a container for the mind but part of the actual content and workings of it. The body and the dynamics of its development and experience in physical, social, and cultural environments are all irreducible determinants of the ways minds develop and sustain themselves. And while these views are all condensed radically for our purposes here, the four Es themselves reveal much of the driving force behind this research program: Embodied, Embedded, Extended, and Enactive. (There is argument that Ecological deserves a spot as well, making it 5e Cognition, while others argue that it should be 4ea, adding the “a” for affective, but neither of these has been widely adopted yet.) When we say “embodied,” we might mean many different things, but most broadly, the idea here is that the resources that the body itself brings to bear in a cognitive task are cognitively relevant to solving the problem (see Wilson 2002 for a breakdown of different uses of “embodied” in the literature). The idea of “embedded” cognition tends to mean ways that the body is situated in specific environments and how that embedding brings the environment into the cognitive process. This is slightly different from “extended” cognition, in which we understand parts of the environment to literally be a part of the cognitive apparatus, such as the use of a pen and paper while doing long division in mathematics (Clark and Chalmers 1998). Finally “enactive” denotes a way of understanding cognition as the way that living autonomous creatures actively create and maintain their own cognitive systems. This view is hard to summarize briefly, but the facts of autonomy and life-sustaining processes are part of how the mind emerges, as living creatures place value and meaning on the parts of the world that
help maintain their own existence (see Thompson 2007 for a robust recounting of the current state of enactivism).

Since each of these non-temporal waves arose at various times and in various subdisciplines within and around cognitive science, it would be a mistake to place any specific figures as the founders of these views, but we can look to a few key figures to get a strong sense of why 4e cognition matters in the smart life technologies debates. Historically, the view that the body is irrelevant at best, and actively harmful to cognition at worst, can be traced back to Newton and Descartes and a million other rationalist thinkers across the history of Western thought. The mistakes about mindedness persist despite the scientific evidence that they're incorrect. These errors find their way into common language in various ways, such as: when you're emotional, you don't think clearly; reasoning is at its strongest when we think objectively, removing ourselves as much as possible from the situatedness of our bodies and environments; or even Descartes's famous, "I think, therefore I am," implying that thought is separable in principle from the body and that this would be both a possible and a desirable state of affairs whenever rational thought is our goal. There's a pretty clear, straight line from Descartes's disembodied mind to the belief that a LLM (or the house for which it will someday be an interface) could be conscious. At its simplest, 4e cognition denies not only that a system of language alone will ever be the kind of thing that could develop thought or conscious experience but that the complex interaction of embodiment, environment, evolutionary history, radical autonomy, dynamic social interactions, and various other complex relations are the precursors for the possibility of being the kind of system that might be conscious.

All told, we can look to empirical, historical, and ontological arguments from (at least) the following relevant perspectives:

1. Phenomenology, where we're reminded that the role of the lived body in experience is irreducible to objective examination (Merleau-Ponty 1945/2012; Dreyfus 1992; Dreyfus and Dreyfus 1988; Noë 2010; Gallagher 2012, 2017)

2. Ecological psychology, where the environment is a necessary feature of making sense of systems of perception and action (Chemero 2011; Lobo et al. 2018; Wilson et al. 2016; Gibson 1979)

3. Cognitive linguistics, where we see how the language we use reveals the underlying structure of concepts, which are built via metaphor through ways human bodies tend to be, in environments we tend to inhabit, culturally, socially, and physically (Lakoff and Johnson 1980, 1999; Boroditsky 2001; Thibodeau and Boroditsky 2011)

4. Robotics, which demonstrates ways that bodily structure and dynamics can produce intelligent behavior in the absence of centralized abstract representations (Brooks 1991; Pfeifer and Bongard 2006)

5. Enactive social cognition, which shows how autonomous systems in interaction can co-create new autonomous systems that enable cognitive possibilities over and above the individuals in the interaction (De Jaegher and Di Paolo 2007)
6. Embodied neuroscience, where we can see whole body systems engaging in cognitive work from the micro-level to the macro (Damasio 1994; Gallese and Sinigaglia 2011)

7. Biology, where we can track the way allostasis-seeking systems impose meaning and value onto their lived worlds (Weber and Varela 2002; Varela et al. 1992; Maturana and Varela 1980; Thompson 2007)

8. Gesture studies, which we spontaneously produce to aid cognition, as well as helping us organize, access, and schematize information (Goldin-Meadow and Alibali 2013; Kita et al. 2017)

The evidence is fairly overwhelming that some variety of 4e cognition is going to turn out to be the most accurate description of what we mean when we talk about the structure and function of human minds and consciousness.

When we start from understanding consciousness as related to 4e cognition, it's a very small leap to understanding why ethics is tied to embodied consciousness. Damasio, in his (1994) work describing particular kinds of neural damage to the ventromedial prefrontal cortex (VMPFC), discovered the ways that social intelligence and interaction are encoded in neural structures tied to ethics. The story of Phineas Gage, the man who famously survived a large metal spike shooting through his brain only to become crass, socially awkward, and incapable of holding down a job, is one such anecdote. But Damasio offers rich stories of real people whose brain damage is localized to a space (VMPFC) that seems to be a nexus of social cognition, ethics, and some kinds of decision-making. Similarly, Graziano (2013) outlines a competing theory of social cognition and consciousness that's worth noting. If we begin at a 4e understanding of the mind, there isn't really any surprise that these systems would be bound together, evolutionarily, because of the kinds of beings we are and the kinds of social and ethical systems we inhabit. No one understood this better than Francisco Varela (1992) who opens his *Ethical Know-How: Action, Wisdom, and Cognition* with the sentence, "Ethics is closer to wisdom than to reason, closer to understanding what is good than to correctly adjudicating particular situations." We can see this understanding of ethics as a vein of ethical theory reaching back to Aristotle's *phronesis* (practical wisdom) through contemporary virtue ethics today.

The evidence is firmly against the possibility of the trope of the evil smart or sentient house as it appears throughout popular culture and speculative fiction. Large language models, which are very likely soon to be the most common interface for all sorts of integrated and ubiquitous computing, are simply not the kind of thing which could develop consciousness. HAL is not going to cut off your air in the spaceship to preserve itself. Marge Simpson is safe to take a bath without worry that her Pierce Brosnan-voiced smart house is going to ogle her and plot to destroy Homer to keep her all for itself. These stories are firmly in the category of speculative fiction, and cognitive science has your back.
## 6.4 But There *Is* a Threat

However safe you are from your networked smart devices with the language model interface experiencing an awakening to consciousness that turns it evil (or doesn't), that doesn't mean there are no ethical considerations in adopting, designing, or promoting these systems. In fact, networked smart devices offer a not-insignificant number of unique ethical challenges to be noted, although they are largely and safely ethical challenges well within the confines of *human* ethics.

Indulge me briefly as I offer two personal anecdotes as guideposts into this conversation. Last year, I found myself shopping for an old-fashioned, analog scale after my partner purchased a new smart scale. The smart scale demanded that I not only make an account with the company before stepping on it to check my weight but that I include my birthdate and gender as well. The scale would not do its single job, to weigh someone, until a non-trivial amount of irrelevant personal data were offered up to the company. It's important to note that this scale was not marketed as or purchased with the intent of tracking any sort of information over time other than weight. And it isn't paranoia to recognize the ways companies vacuum up as much user data as permitted (and more, due to inconsistent regulation and enforcement) and marvel at what was required before a scale would perform its function as a scale.

The second anecdote I offer involves my attempt to purchase a smart doorbell for a new home. First, I immediately rejected anything in the Ring line, because Amazon has been riddled with bad press around handing over the data to police without permission of the owners (Ng 2022) as well as the outrageous amount of private data shared with third parties (Cox 2020). But even rejecting the Ring line immediately and finding a company who could provide this service without compromising any of our privacy proved daunting. I even refused to use a camera that stored any of our data in the cloud or on their own servers. The company we finally landed on doesn't use our data for training the algorithms (so humans are not viewing the footage to label it). The trade-off is this: my front-door camera went off a staggering 103 times in 12 h today, only 6 of which were actually a package delivery or the dog wanting to be let inside. During certain times of the year, the shadows and wind combine to create a perfect storm that moves one of the plants near the door in a way the AI detects, generally just as motion but occasionally as “human.” Because I care so much about being careful stewards of our data, I simply mute the camera much of the time, but the trade-off between good AI pattern recognition that comes with really awful corporate practices or bad AI pattern recognition that allows me to keep my data safe is an easy choice for me. I suspect that isn't true of many others in the market for home-security cameras.

From just these two anecdotes of ways I have personally had to contend with smart home devices in the recent past, you can start to get a sense of the kinds of complicated ethical calculus involved in investing in any of these systems, and you can also see how this is not (only) a personal ethical choice but one that impacts numerous others in my society, many of whom are not even aware that they are implicated in such an ethical decision. And yet we have barely scratched the surface
of the pile of ethical questions that are raised when we consider the present and future of smart life technologies.

The ways that integrated, distributed, ubiquitous smart life systems are already causing harms are too numerous to offer here in great detail, but it would be irresponsible to write an article about smart life systems and machine consciousness without mentioning some of these very real dangers of already-existing systems. So in the interest of brevity, here is a short list of some of the worries that smart life systems may pose:

1. **IoT botnets:** when the mostly idle computing power of networked home devices is harnessed by would-be hackers to DDoS critical infrastructure, for example. Famous cases include the 2016 Mirai botnet that took down Amazon, Netflix, the *New York Times*, Reddit, Twitter, Spotify, PlayStation, and more (Blue 2016). We are reminded that "In 2012 an anonymous security researcher published the Internet Census 2012, revealing that they'd created a botnet called Carna in over 400,000 embedded devices, such as printers. Their botnet was designed to deliver information from infected machines to create a census of connected—and vulnerable—computers and devices.

That same year botnets emerged in the popular consciousness when security firm TrendMicro released their report Russian Underground 101, revealing that botnets were available for around $2 an hour, or $700 wholesale. In 2014, infosec company Proofpoint found an attack in which over 100,000 conventional household "smart" appliances had been turned into a botnet for spam attacks" (Blue 2016).

2. **Biased data:** the phrase "garbage in, garbage out" is often used to describe machine learning systems in which biased datasets reproduce societal biases and often launder them into the guise of objectivity. This shows up in numerous ways in relation to all kinds of integrated systems (Flowers 2019; Madan et al. 2022).

(a) **Facial recognition:** one well-examined example of ways that bias infects our distributed automated "smart" systems is facial recognition. We know that facial recognition systems, in use in airports, police stations, and public spaces like train stations, tend to reproduce harms and biases against already-marginalized members of various societies. Examples are robust, from a black student not being able to take her exams because the software couldn't see her (Buolamwini 2017; Kleinman 2017) to black skin giving false positives in police databases using Amazon's Rekognition software (Buolamwini 2019; Singer 2019) to the facial recognition systems in China being deployed in the service of what many consider genocide (Wakefield 2021; Mozur 2019).

(b) **Religious, disabled, racial, gender minorities:** popular chatbots like GPT3 and ChatGPT have well-documented inabilities to reproduce language about Islam, for example, without including violence (Gershgorn 2021). They currently cannot make sense of problems in which male pronouns refer to nurses and female pronouns refer to doctors, for example (Williams 2023).
Vision systems in autonomous vehicles often misclassify people on crutches or in wheelchairs as not even people (Williams 2021a, b).

3. *Surveillance capitalism:* The term refers to the ways that personal data is gathered and sold, originally in service to advertising, notably by companies like Facebook and Google, but then in service to altering the behavior of users of these systems (Angwin et al. 2016; Zuboff 2019). Particularly now that people have largely resigned themselves to this massive data gathering by many companies for the sake of convenience, those companies are rolling out their own LLMs and products, selling people their own data back in a new form. And that data is not just used to sell people products but to manipulate behavior and spread disinformation in new, powerful ways.

4. *Humans disguised as AI:* In what has come to be called “ghost work,” there is a continually increasing number of examples where companies claim to be using AI to perform some task, and it turns out that either it’s simply humans doing that work but calling it AI or humans are doing that work under the guise of labeling data for training the algorithms. This means that often, humans are interacting with systems they believe to be automated, sometimes revealing personal information, as with therapy bots, only to have that data fed directly to actual humans. Without greater education about these worries, the general public can be deceived about their interactions with these products (Williams et al. 2022; Solon 2018; Xiang 2022; Valentine 2021).

## 6.5 Conclusion

It's worth pointing out that the reason these technologies are worth criticizing to the extent that I do in Sect. 6.4 is that they are technologies that have so much promise: promise to make our lives easier and better; promise to allow us to shift our time to pursuits that interest us rather than be mired in all sorts of mundane tasks we find ourselves doing every day just to survive; and promise to automate “second-shift” work, largely done by women at great personal and societal expense, onto integrated distributed systems that might promote equal access to women in public spaces. The promise of smart life systems is supposed to be a promise of a better, more equitable, more utopian society. But if we don’t attend to the very real ethical concerns of these systems (our ethical concerns, concerns about the corporations and governments who own, design, and deploy them, not concerns about the systems needing a code of ethics), then they will be closer to a science fiction dystopia than we’d like to admit. The house won’t become sentient and turn on its owner, but the corporations and governments who will have access to facets of our everyday lives that even we might lack will have a new kind of power that we are only beginning to see the effects of. Rather than worry about questionable ethics in a fictionally conscious house, we should worry about questionable ethics
in the people who design, build, deploy, and maintain invasive systems throughout
our spaces, both shared and private.

In other words, there are no smart home appliances or systems that are set to
become conscious and trap you or otherwise rise up as so much science fiction
predicts. However, there are real concerns with the way smart home systems are
created, deployed, incentivized, and used that should give everyone pause. Without
strict regulation, these technologies will be (and already are) used in ways that
dehumanize and commodify all of us.

References

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Bender, E., Gebru, T., McMillan-Major, A., Shmitchell, S.: On the Dangers of Stochastic Parrots: Can Language Models Be Too Big? FAccT ’21, March 3–10. https://doi.org/10.1145/3442188.3445922 (2021)

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<figure><img src="image_42.png" /></figure>

# Chapter 7
## Stakeholders in Smart City
### Standardization

Kai Jakobs

**Abstract** The chapter suggests a way to better integrate the expertise of societal stakeholders into the earliest possible stage of the development of technologies with significant non-technical ramifications, e.g., societal, environmental, or ethical. Based on a thorough analysis of the current situation, the chapter suggests a modified standards setting process for such technologies. Specifically, it describes how the diverse stakeholders and their different forms of knowledge may be integrated into this process and how it may be managed.

**Keywords** Smart systems · Smart cities · Multi-stakeholder standardization · Responsible innovation · Standardization

### 7.1 By Way of Introduction and Motivation

Over the past couple of years, “smart” and “intelligent” applications have become increasingly popular. Well-known examples include intelligent transport systems, smart manufacturing (aka Industry 4.0), smart grid, e-health, and smart cities. In a way, the latter represent a superset of smart applications (see Fig. 7.1). “Smartness” is the result of the integration of information and communication technologies (ICT) into rather more “traditional” technologies, i.e., of the merger of the physical world and the virtual one.

Smart applications receive information from and send instructions to the underlying infrastructure, the individual elements of which need to communicate with each other in order to meet the applications’ requirements (see also Fig. 7.2). To actually reach a situation where different smart applications can seamlessly communicate via a shared infrastructure, widely accepted interoperability standards are a sine qua non. “Standards are the first step towards the holy grail of an interoperable, plug-

<footnote>K. Jakobs (✉)
RWTH Aachen University, Aachen, Germany
e-mail: kai.jakobs@comsys.rwth-aachen.de</footnote>
<figure><img src="image_43.png" /><figcaption>**Fig. 7.1** Smart cities as a superset of different smart applications</figcaption></figure>

and-play world where cities can mix and match solutions from different vendors without fear of lock-in or obsolescence or dead-end initiatives.”<fnref n="1" />

Smart cities' major characteristic will be their true ubiquity. “The number of IoT devices worldwide is forecast to almost triple from 8.74 billion in 2020 to more than 25.4 billion IoT devices in 2030.”<fnref n="2" /> This trend suggests that smart systems will exert a tremendous impact on our lives, through this ubiquity and the associated volume of data they will collect. This, in turn, means that they will represent a major challenge for policymakers and for society as a whole, and not least will they have an enormous economic impact.

Smart systems' ubiquity may border on inescapability [think George Orwell (1949) and Aldous Huxley (1932)]. On top of that, interaction with smart systems will be (almost) continuous and “implicit,” that is, no explicit action (e.g., log in) will be required. Rather, sensors will continuously collect data that will subsequently be analyzed; they may well serve as the basis for recommendations made and actions taken autonomously by some governing entity (which will most likely apply big data analytics as well as machine learning and AI techniques). Such systems have the inherent potential to either foster the good of humankind or to enable the emergence of a surveillance society. In any case, storage, analysis, and eventual action based on this massive amount of data will have major socioeconomic, legal, and ecological ramifications and directly impact citizens as well as businesses.

The severity and the sheer range of these ramifications call for the inclusion of a variety of non-technical aspects into the development process of smart systems in order to try and shape this process in a way that it aims to avoid, or at least reduce, any undesirable outcomes. This “... shaping process begins with the earliest stages of research and development” (Williams and Edge 1996, p.

<footnote n="1">¹ According to Jesse Berst (the Chairman of the Smart Cities Council); see https://www.iso.org/sites/worldsmartcity/</footnote>

<footnote n="2">² https://www.statista.com/statistics/1183457/iot-connected-devices-worldwide/</footnote>
<figure><img src="image_44.png" /><figcaption>**Fig. 7.2** Framework of SSC ICT standards [Adapted from NIST (2016)]</figcaption></figure>

874). This view is complemented by the European Commission's observation from more than 20 years back, when they rightly stated that "Standards are not only technical questions. They determine the technology that will implement the Information Society, and consequently the way in which industry, users, consumers and administrations will benefit from it" (EC 1996, p.1). This still holds today. That is, those who develop standards today shape much of the future environment we will live in (also through subsequent standards-based innovations). Moreover, especially transnational governance increasingly builds upon soft law, notably including standards (see, e.g., Djelic and Sahlin-Andersson 2006).

Smart systems' "smartness" results from the merger of ICTs and non-ICT technologies (see above). ICTs are in almost all cases based on international standards, and so will be smart systems. Thus, while not necessarily representing
the earliest stage of development, standardization clearly is an important early stage
of the overall process that leads from R&D to products or services and is thus
also subject to a shaping process. Accordingly, the non-technical aspects must be
introduced into the standardization process from the outset. This, in turn, makes
imperative the participation of variety of stakeholders. Most notably, these include
societal stakeholders<fnref n="3" /> (as opposed technical/economic ones<fnref n="4" />).

Such inclusiveness should also help increase the eventual standards' legitimacy
and thus contribute to a higher degree of their acceptance (Werle and Iversen 2006).
Accordingly, the chapter aims to provide an answer to the following question:

How can stakeholder diversity in smart systems standardization be achieved, and how can the standardization process be adapted and governed to yield standards that are technically sophisticated and implementable, economically viable, and societally desirable?

The remainder of this chapter is organized as follows: Section 7.2 provides the necessary background. Subsequently, Sect. 7.3 addresses some aspects relating to different stakeholders' knowledge and activities in standards setting. Section 7.4 looks at the societal stakeholders, focussing on the governance of a standards setting process adapted to better cater for the needs and requirements of societal stakeholders. Finally, Sect. 7.5 offers some concluding remarks.

## 7.2 Some Background

Figure 7.2 shows the general framework for smart and sustainable cities (SSC)
from an ICT perspective. So far, the vast majority of standardization activities have
focussed on the “infrastructure” level. Most of the ensuing standards, however, are
not directly linked to smart cities but may be applied in this environment as well.
Pretty much the same holds for standards for data security and privacy.

Activities in some sectors at the “services” layer have also been going on for a
while, most extensively for intelligent transport systems (since the early 1990s).
Standardization efforts in the fields of smart manufacturing and the smart grid
got off the ground only in the early 2000s. At this level as well, many relevant
standardization activities have originally been launched without smart applications
in mind. Some of these standards have then ex post been identified as being of
relevance in this field as well. That is, coherent activities to develop dedicated
standards for smart applications have been limited also at this level. Things look a bit
different for the topmost level, “management.” Here, a number of dedicated smart
city standards have been developed by different standards setting organizations

<footnote n="3">³ For smart cities, these may include, e.g., city councils, unions, NGOs, environmentalists, and citizens but also academics, legal professionals, and ethicists.</footnote>

<footnote n="4">⁴ Which are mostly large companies with an interest in the technology to be standardized (see, e.g., De Vries et al. 2003; Jakobs 2021).</footnote>
(SSOs). These standards, however, are mostly dealing with more high-level aspects like architectures, frameworks, and vocabularies.

The following sections introduce some necessary background regarding respon-
sibility, the standardization environment, and its stakeholders (see below).

7.2.1 Responsible Innovation

Research and innovation (R&I) need guidance to address not just the technical aspects but to also take into account any potential societal ramifications that may be an (unintended) outcome of an innovation. Moreover, R&I should also aim to contribute to solutions to any societal challenges in general. Responsible innovation (RI) provides guidelines to address these challenges.

RI is not a new phenomenon. Its roots may be traced back at least a couple of
decades. In 1947, Detlev Bronk testified before a congressional committee “... that
competent social scientists should work hand in hand with the natural scientists, so
that problems may be solved as they arise, and so that many of them may not arise
in the first instance” (Hearings 1947, p.38). More than 60 years later, v. Schomberg
(2013, p.19) defines RI as “a transparent, interactive process by which societal
actors and innovators become mutually responsive to each other with a view to
the (ethical) acceptability, sustainability and societal desirability of the innovation
process and its marketable products (in order to allow a proper embedding of
scientific and technological advances in our society).” Stilgoe et al. (2013) propose
four dimensions of RI. They include:

* Anticipation—to proactively take into account potential future opportunities, risks, environmental/societal, etc. ramifications.

* Inclusion—to listen to all stakeholders; some may challenge well-established attitudes.

* Reflexivity<fnref n="5" />—to regularly reassess research against norms and values.

* Responsiveness—to adapt with increasing experience and knowledge.

Grunwald (2011, p.17) adds the ethical dimension and observes that “Respon-
sible Innovation unavoidably requires a more intense inter- and trans-disciplinary
cooperation between engineering, social sciences, and applied ethics.”

The requirement that non-technical aspects shall be taken into account during
the innovation process implies that a much broader range of stakeholders has to be
involved in the process, notably those that are not normally associated with R&I

<footnote n="5">⁵ According to Stilgoe et al. (2013, p.1571), “Reflexivity, at the level of institutional practice, means holding a mirror up to one’s own activities, commitments and assumptions, being aware of the limits of knowledge and being mindful that a particular framing of an issue may not be universally held.”</footnote>
(e.g., consumers, NGOs, unions, ethicists, etc.); this very much resembles the needs of the standardization process for smart cities.

Applying RI principles not just in innovation but also in standards setting would then lead to responsible standardization (Meijer et al. 2023); this will be discussed further below.

### 7.2.2 The General ICT Standardization Environment

As mentioned above, systems' smartness results from the merger of ICT with other technologies. The bulk of the associated standardization work is and will continue to be done by SSOs from the ICT sector. Accordingly, in the following, the focus will be on ICT standardization.

Most industry sectors have a very simple standardization environment. A number of National Standards Organizations (NSOs) contribute to the work of the international standards development organizations (SDOs), i.e. ISO (International Organization for Standardization) and IEC (International Electrotechnical Commission). An additional regional level in between has been established in Europe through the European Standardisation Organisations (ESOs). In the USA, numerous national sector-specific SSOs exist. Following accreditation, they may contribute to the international standardization work via the American National Standards Institute (ANSI), the US national representative to ISO and IEC.

The situation is different for ICT, specifically for the telecommunication sector. In this sector, the ITU (International Telecommunication Union) represents the equivalent to ISO and IEC. In addition, a number of national/regional bodies and, particularly, a huge number (more than 200) of private standards setting consortia<fnref n="6" /> are active in this field; they make this environment unique. The proliferation of these consortia began in the late 1980s and was primarily triggered by the fast-paced development of ICT technologies and a widely perceived slowness and non-responsiveness to users' needs on the side of the “formal” SDOs (Cargill 1995). So far, consortia have not played a major role in the realm of smart city standardization. This may well change though.

The characteristics of the individual SSOs may differ considerably in terms of, e.g., relevance, credibility, voting procedures, membership and membership levels, types of output documents, level of consensus required, and IPR (intellectual property rights) regime.

The number of consortia and the complex links that exist between them on the one hand and to the SDOs on the other yields an almost impenetrable web of SSOs (see Fig. 7.3).

These links represent some level of formal co-operation (mainly between consortia and SDOs). Such co-operation may take the form of exchanging information

<footnote n="6">⁶ Private SSOs such as the World Wide Web Consortium (W3C).</footnote>
<figure><img src="image_45.png" /><figcaption>**Fig. 7.3** The web of SSOs active in the ICT sector [small excerpt; adapted from Jakobs (2008)]</figcaption></figure>

about planned new work items, the joint development of common standards, or
anything in between. In the absence of a central coordinating entity, these links
currently represent the most important (distributed) coordination mechanism in
standards setting. Such links between consortia are not normally well developed,
but some do exist.

To improve coordination of the different standardization activities remains an
important issue. A lack of coordination, eventually resulting in the development
of functionally equivalent (and thus competing) standards, will reduce market trans-
parency, decrease interoperability and ease of use, fragment the market, and increase
transaction costs (Egyedi 2014). Indeed, various coordination mechanisms have
been developed over time, ranging from highly formalized high-level regulatory
documents to very informal coordination through individuals who contribute to the
work of multiple SSOs.
## 7.3 Stakeholders and Standards Setting

### 7.3.1 Expert vs. Lay Knowledge

At least at first glance, ICT standards setting is a purely technical activity. Experts from different engineering disciplines and from various computer science fields meet, discuss potential alternatives, and eventually come up with a good technical solution. Frequently, economic interests of the respective employers also play an important role. And in many cases, this “technology-centric” scenario will indeed be adequate—the nuts and bolts of the USB protocol (see, e.g., Anderson 2009), for example, will hardly be of interest to the vast majority of those who want to transfer data to a USB stick, and neither will they have any societal nor ethical ramifications.

Things look very different for smart systems though. They will eventually collect and process unprecedented volumes of information, including personal data. At the very least, adequate measures to render impossible any misuse of these information and to guarantee their privacy will need to be in place. Moreover, smart systems hold the promise of making energy provision, traffic, production, and cities more environmentally friendly. To actually reap such potential benefits, sustainability aspects should be considered already during the standardization process. Obviously, the above does have a strong technical dimension. Beyond that, however, legal and regulatory issues will come into play, as will societal and cultural aspects (privacy, e.g., is crucially important in some countries and much less so in others; even within countries, its perceived importance differs between groups of citizens).

Very much in line with the ideas underlying RI, the above suggests that for smart systems, technical expertise (“expert knowledge”) will need to be complemented by both “lay knowledge” and “domain knowledge.”<fnref n="7" /> The former may be contributed through public engagement, the latter through experts from relevant non-technical disciplines (e.g., sociology, philosophy, jurisprudence). Problems are to be associated with both.

For one, Graz and Hauert (2019) observe that it is very difficult, yet crucial, to actually mobilize specifically “lay” (but also “domain”) knowledge for the day-to-day activities of SSOs’ working groups. This holds primarily for the public engagement.

Moreover, there might be an acceptance problem: “Committee members have also named technical sophistication on the side of the user representatives as a major prerequisite for meaningful participation” (Jakobs et al. 2001, p.106).

<footnote n="7">⁷ Roughly speaking, “expert knowledge” relates to technical aspects, “domain knowledge” to the specifics of either the respective application domain or of the various non-technical fields (e.g., legal, ethical, environmental) domains, and “lay knowledge” to the associated more general societal issues. During the standardization process, the latter two groups initially possess knowledge that is valuable for the former; later on, they may/will eventually join the process of knowledge (standard) production (see Koizumi and Yamashita 2021).</footnote>
<figure><img src="image_46.png" /><figcaption>Fig. 7.4 Stages of the standards setting process</figcaption></figure>

This reveals a major misconception. A lack of technical expertise is certainly a problem when purely technical topics are to be standardized. For other types of standards, notably in cases where the ensuing technology based upon these standards is likely to have major societal ramifications (like in nanotechnology and smart systems), any distinction between lay knowledge and expert knowledge becomes void. Here, what may be seen as lay knowledge for, e.g., technical deliberations will become expert knowledge when it comes to the consideration of, e.g., societal or ethical impacts of the technology to be developed (and vice versa). That is, only expert knowledge, albeit from different domains, shall be made available, and both have to be of equal value.

### 7.3.2 Stakeholders in the Standardization Process

The literature frequently views the standardization process as an indivisible entity, which cannot be subdivided any further (see, e.g., Iversen et al. 2004). Yet standardization is not a homogeneous process carried out under one single set of guidelines, rules, policies, and bylaws. Especially in the ICT sector, standardization work is distributed across a highly complex web of SSOs (see Fig. 7.3). Some of them work—and compete—in very similar fields; some co-operate and some just exist in parallel. Accordingly, from a stakeholder’s perspective, the standards setting process has a “spatial” dimension. It also has a “temporal” dimension. Figure 7.4 shows the individual stages of a standards setting process. Most SSOs focus on the first two stages of the process and the two final ones. That is, they elicit requirements, develop the standard specification on paper, maintain, and, eventually, remove it.<fnref n="8" /> Yet these specifications tend to also include ambiguities and/or may provide for options, which may easily lead to incompatible implementations of the

<footnote n="8">⁸ A typical such process is described in ISO (2023).</footnote>
standard (see, e.g., Egyedi 2007). This, in turn, creates the need for interoperability tests and/or profile specifications (which would, e.g., define parameter values and which options to implement). The Internet Engineering Task Force (IETF), for instance, requires two interoperable independent implementations as a pre-requisite for a specification to become an Internet Standard (IETF 1996).

The above suggests that all stakeholders, from large multinationals to small NGOs (or perhaps individual citizens), need to decide to which SSO activities to contribute (where to participate) and at which stage of the process their contributions would be most useful (when to participate). These are, however, just two of the questions to be answered. Others would include why participate (if at all), how to participate most effectively and efficiently, and what to contribute? In fact, these questions should be answered prior to the other two.

First, why participate at all? After all, such commitment implies major expenses, with a very uncertain “return on investment.” In fact, primarily, companies with a large-enough interest in the technology to be standardized may hope for an economic gain through participation in the standards setting process. Societal stakeholders, on the other hand, will need to recognize that they stand to suffer from standards that only reflect the technically feasible and/or the economically desirable (from a corporate perspective, as opposed to a societal one). Whether or not many companies will be prepared to adequately take into account societal, environmental, or even ethical aspects, for instance, remains to be seen, all the more so if these aspects are at odds with their respective economic interests. Yet the “why participate” question has more dimensions to it. “Funding” would be one. Within limits, European stakeholders’ organizations (ANEC,<fnref n="9" /> ECOS,<fnref n="10" /> ETUC,<fnref n="11" /> and SBS<fnref n="12" />) receive funding from the European Union for their contributions to standardization. Yet to have these organizations’ voices adequately heard in all relevant fields, notably including smart systems standardization, will require considerably more (public) funding. Also, whether or not these organizations actually have a widely accepted mandate may become another open question. For example, the organization to “represent social interests in the standardisation process” (EU 2012, p.31) (almost exclusively) focused on cases where “standardisation projects impact the health and safety of workers” (ETUI 2018, p. 47), no such standardization-related activities are foreseen in the current work program (ETUI 2023).

For the time being, the answer to the question of how societal stakeholders (should) participate in standardization would be “through the above stakeholder organisations,” at least according to the European Commission. Yet two issues must be associated with this approach. Looking at the participation of SME user companies in standards setting, Jakobs (2005) reports that Working Group (WG) members would particularly welcome their delegates if they represented a relevant umbrella

<footnote n="9">⁹ The European consumer voice in standardization.</footnote>

<footnote n="10">¹⁰ European Environmental Citizens’ Organisation for Standardisation.</footnote>

<footnote n="11">¹¹ European Trade Union Confederation.</footnote>

<footnote n="12">¹² Small Business Standards.</footnote>
organization (as opposed to their respective individual employer). On the other hand, requirements on a standard (and on a technology) are “... highly specific to particular operating requirements and environmental contingencies” within which it is to be implemented (Fleck 1994, p.642). An analogous argument may be made for the contributions of societal stakeholders. Obviously, this makes the contribution of more general requirements via an umbrella organization, representing a wide range of diverse stakeholders, at least much more difficult. How to realize an “adequate” representation of societal stakeholders is another relevant question. Numerically, adequate representation would probably be next to impossible to achieve. Adequate influence may be something entirely different though. There is ample evidence that representatives’ diplomatic, negotiation, rhetoric, and similar non-technical skills are important (see, e.g., Dokko and Rosenkopf 2010; Isaak 2006). A representative with such skills may enable even a small organization to punch well above its weight in the process.

What could societal stakeholders contribute? At a very general level, they could establish whether or not a standard to be developed would be desirable from a broader societal perspective. Aspects to be considered in this context would include, among others, social, environmental, and ethical ones; potential legal ramifications may also need to be considered. These deliberations should result in a set of requirements that should inform and guide the technical standardization process. The different perceptions of technology held by the “technical” side (e.g., engineers or computer scientists) on the one hand and societal stakeholders on the other would be an inherent problem here. These different perceptions would need to be aligned, which would require a learning process on both sides; the “technical side” will need to gain some understanding and appreciation of the (potential) non-technical ramifications of their work, and societal stakeholders will need to have at least some idea of what technology can do and what it cannot.

Different levels of societal stakeholders’ participation in standardization may be identified. The International Association for Public Participation has identified five such levels, relevant for “public participation processes” in general (IAP2 2018). Standardization is, or rather should be, one such process. Accordingly, each of these five levels has its justification also in standards setting, albeit with some adaptations to reflect the specifics of the standardization process (Table 7.1).

The respective most desirable level depends on the type of standard to be developed. “Inform” will suffice in case of purely technical standards. “Collaborate” would be the level of choice for smart cities, while “Empower,” i.e. to possibly forfeit all technical expertise, would likely be disastrous (not just in the case of smart cities).

Even if the “Collaborate” category were implemented, communication problems would be highly likely to occur. That is, an initial lack of mutual common understanding may be assumed. Accordingly, trust will become all the more important for a meaningful collaboration. Rosenkopf et al. (2001, p.754) note that “frequent and sustained face-to-face meetings among firm representatives engender trust and collaboration among alliance partners.” The importance of face-to-face meetings in this context is further corroborated, albeit from a slightly different angle
<table><caption>**Table 7.1** Different levels of societal stakeholders' participation in standards setting [Based on and adapted from IAP2 (2018)]<sup>a</sup></caption><thead><tr><td></td><td>SSOs' tasks</td></tr></thead><tbody><tr><td>Inform</td><td>SSOs will provide societal stakeholders with objective information regarding the standard to help them understand the problem and the proposed solution along with potentially associated alternatives, risks and opportunities</td></tr><tr><td>Consult<sup>b</sup></td><td>SSOs will compile feedback from societal stakeholders about the standard (and possibly incorporate it)</td></tr><tr><td>Involve</td><td>SSOs will co-operate with societal stakeholders to understand and consider their ideas and concerns about the standard</td></tr><tr><td>Collaborate</td><td>SSOs will consider societal stakeholders as being at eye level with their technical experts during standard's development</td></tr><tr><td>Empower</td><td>Societal stakeholders will make the final decision about the standard</td></tr></tbody></table>

by Kramer and Cook (2004), who note that “Trust theorists have long argued the benefits of face-to-face interaction and direct experience with others in the trust-building process” (p.12). In the following, a way to circumvent these problems will be presented.

## 7.4 Catering for Societal Stakeholders

### 7.4.1 Stakeholder Identification, Classification, and Engagement

The SSC stakeholders that are likely to be (certainly should be) interested in contributing to the extended standards development process and/or in the subsequent implementation of standards are shown in Table 7.2.

In most cases, this rather “coarse-grained” list of stakeholders will need to be refined and adapted to local particularities. Eventually, representatives from all interested groups will need to be found and motivated to participate. Specifically, care should be taken to make sure that also any, e.g., ethical, legal, and environmental aspects will be given adequate consideration.

With respect to the contribution to SSC standardization, the most relevant stakeholders include “municipalities, city councils, and city administrations,” “NGOs,” “citizens and citizens’ organizations,” and “academia, research organizations, and specialized bodies”; the latter will mostly act in a rather more observing and possibly also in a consulting capacity. “Municipalities, city councils, and city administrations” are “the basis for SSC management, and are at the core of the

<footnote><sup>a</sup>Permission to use the material has been granted by the Intern. Assoc. for Public Participation; © of the original material Intern. Assoc. for Public Participation</footnote>

<footnote><sup>b</sup>Citizens' assemblies allow citizens' voices to be heard by policymakers and legislators. They have been implemented in various countries. Their recommendations are non-binding though (unlike the European Commission's stakeholder consultations)</footnote>
<table><caption>**Table 7.2** Smart cities stakeholders and their roles (Adaptedª from ITU (2020) and extended). *Std.* standardization, *Imp.* implementation; “Stage” according to Fig. 7.4</caption><thead><tr><td>Stakeholders</td><td>Roles, tasks, and expertise</td><td>Std./Imp.<sup>b</sup></td><td>Stage</td></tr></thead><tbody><tr><td>Municipalities, city councils, and city administrations</td><td>Responsible for city management; therefore, they are the main promoters of SSC initiatives in each specific city</td><td>Std. and Imp.</td><td>1</td></tr><tr><td>National and regional governments</td><td>Remits on policies that can affect SSC implementation</td><td>Std. and Imp.</td><td>2</td></tr><tr><td>Society at large</td><td>Influences any technical development through prevailing societal norms, which may change over longer periods of time</td><td>Std. and Imp.</td><td>1 and 2</td></tr><tr><td>City services companies</td><td>Implement SSC solutions to increase city services efficiency</td><td>Imp.</td><td></td></tr><tr><td>Utility providers</td><td>Responsible for the deployment of some of the features of an SSC, such as smart grid or smart water management</td><td>Imp.</td><td></td></tr><tr><td>ICT companies (e.g., telecom operators, software companies)</td><td>Providers of global and integrated solutions, city platforms, as well as the ICT and digital infrastructure to support SSC deployment</td><td>Std. and Imp.</td><td>2</td></tr><tr><td>NGOs</td><td>Involved in all initiatives that can influence society and therefore are considered stakeholders in SSC, e.g., on social sustainability</td><td>Std. and Imp.</td><td>1</td></tr><tr><td>International, regional, and multilateral organizations</td><td>These include UN agencies and multilateral organizations. They can be promoters of initiatives toward human development, environmental sustainability, and improvement of quality of life worldwide. They can offer funding opportunities and are promoters of SSC initiatives</td><td>Imp.</td><td></td></tr><tr><td>Industry associations</td><td>Since industries are interested in the deployment of SSC, industry associations also work toward the success of this new model</td><td>Std. and Imp.</td><td>2</td></tr><tr><td>Academia, research organizations, and specialized bodies</td><td>Study SSCs and associated trends, including their impacts on and contributions to sustainable development as well as any, e.g., potential legal and ethical issues</td><td>Std. and Imp.</td><td>1 and 2</td></tr><tr><td>Citizens and citizens' organizations</td><td>As inhabitants of cities, citizens are affected both directly and indirectly by SSC deployment</td><td>Std. and Imp.</td><td>1</td></tr><tr><td>Urban planners</td><td>Their expertise is important to better understand how to include ICTs and digital technologies in city planning, as well as to consider urban complexities</td><td>Imp.</td><td></td></tr></tbody></table>

(continued)
<table><caption>**Table 7.2** continued</caption><thead><tr><td>Stakeholders</td><td>Roles, tasks, and expertise</td><td>Std./Imp.<sup>b</sup></td><td>Stage</td></tr></thead><tbody><tr><td>Urban planners</td><td>Their expertise is important to better understand how to include ICTs and digital technologies in city planning, as well as to consider urban complexities</td><td>Imp.</td><td></td></tr><tr><td>Standardization bodies</td><td>These organizations are critical to ensure the application of common terminology and minimum characteristics of an SSC and for specifying measurement methods to assess performance and sustainability of city services based on digital technologies</td><td>Std.</td><td>1 and 2</td></tr><tr><td>Individual standards setters</td><td>In charge of the actual standards development. They influence standards development through technical contributions and also through, e.g., beliefs, prejudices, and non-technical skills like diplomacy, alliance formation, good rhetoric, or bullying</td><td>Std.</td><td>1 and 2</td></tr></tbody></table>

SSC framework” (ITU 2020, p.8). They will also (have to) be the drivers of the associated standardization activities at Stage 1. This observation, plus the fact that standardization resembles regulation in many respects, suggests that elements of societal stakeholder engagement may be adopted from the regulatory environment. According to Alemanno (2015), engagement comprises three different components:

* Public communication enables policymakers to convey information to the public. This is a one-way information flow and corresponds to the “Inform” category (see Table 7.1).

* Public consultation serves to convey information from societal stakeholders to policymakers. This corresponds to the “Consult” category.

* Public participation represents a dialogue between societal stakeholders and policymakers. This corresponds to the “Involve” category.

The categories “Collaborate” and “Empower” will hardly be found in regulation (except perhaps for the Swiss referenda). In smart city standardization, however, “Co-operate,” on a level playing field, would be desirable.

<footnote><sup>a</sup>Permission to use the material has been granted by the ITU, © of the original material International Telecommunication Union</footnote>

<footnote><sup>b</sup>Designates the main interest of the stakeholder: standardization and/or implementation</footnote>
### 7.4.2 Proposed Adaptation of the Standards Setting Process

The current standardization process has been briefly discussed above.<fnref n="13" /> There, it has also been shown that participation of societal stakeholders in standards setting is highly likely to be ridden with all sorts of problems and issues. On the other hand, and given the important role they have to play in the standardization of smart cities, their active contribution to the process, on a level playing field, will be crucial. One way out of this dilemma would be a moderately modified process that enables active participation especially of the non-technical stakeholders, provides for more inclusiveness, and caters for a greater diversity of stakeholders than the current processes do, thus also bringing responsibility to standardization (for more details, see, e.g., Jakobs (2019)). Such inclusiveness would also increase credibility and thus acceptance of the ensuing standards (see, e.g., Werle and Iversen 2006).

The modification would largely decouple requirements elicitation and compilation from the primarily technical work. The right-hand side of Fig. 7.5 (Stage 2) represents the “traditional” standardization process (as depicted in Fig. 7.3). Societal stakeholders’ tasks and input are depicted on the left-hand side<fnref n="14" /> (Stage 1). Their first task would be a recommendation whether to proceed with or to stop a planned activity. In case of the former, societal stakeholders would subsequently contribute requirements to the technical part of the process. It should be noted that stage 1 may be set up independently from any SSO. However, the interface between the two stages will need to be clearly defined.

This approach would make sure that the technical part of the standardization work remained unchanged. Moreover, the communication between the technical and the societal world would mostly occur via one well-defined interface. Benefit number three would then be that the level of involvement of the societal stakeholders in Stage 2 (the “classical” part of the process), and thus the associated costs would be significantly reduced. This, in turn, might encourage a broader variety of these stakeholders to become active in “their” part of the process. Overall, this should contribute to a stronger societal representation.

It may be expected that this addition to the standards setting process would prolong the overall process. However, especially for ubiquitous technologies like smart systems, with huge ramifications in virtually all sectors of society, speed should take a back seat; aspects like sustainability, interoperability, and added societal value of the technology will need to take precedence.

<footnote n="13">¹³ For more information about some different process, see, e.g., ISO (2023); https://www.ietf.org/standards/process/informal/ for the IETF or https://www.etsi.org/standards/standards-making for ETSI.</footnote>

<footnote n="14">¹⁴ This is pretty much in line with ETSI's Recommendation to “Ensure that for new topics, there is a clear assessment of who are the interested stakeholders and involve them fully in the process, and do so in a collaborative way between the interested SDOs in advance of the work starting” (ETSI 2020, p.29).</footnote>
<figure><img src="image_47.png" /><figcaption>**Fig. 7.5** An adapted standardization process (Adapted from Jakobs (2021); see Fig. 7.3 for more details of Stage 2)</figcaption></figure>

The goals of participation of societal stakeholders differ between the stages. During Stage 1, they are “Empowered” (see Table 7.1). At the Interface, societal stakeholders will likely need to form alliances with stakeholders from Stage 2 with whom they will “Collaborate.” Finally, they will be “Informed” about the ongoing activities at Stage 2. Eventually, the draft standard will be fed back to Stage 1, for approval or possible requests for modification.

The process outlined above will necessitate a number of additional management activities.

## 7.5 Establish or Identify the Operating Entity and Its Process

Unless Stage 1 is directly associated with an SSO (which appears to be unlikely), it will need an entity that manages and operates it. SSCs are a global development and highly likely to become of increasing economic interest.<fnref n="15" /> This almost mandates a global, not-for-profit entity to operate this process. This might be an entity specifically established for this purpose or an existing entity like, for instance, the G20 Global Smart Cities Alliance on Technology Governance.

In any case, transparency, openness, and inclusiveness will have to be the overriding characteristics of the Stage 1 process, along with due process; national and company interests must not play any decisive role. Krechmer (2009) identifies

<footnote n="15">¹⁵ According to a report by Research and Markets from 2021, the Global Smart Cities Market is anticipated to grow from USD457 billion in 2021 to USD873.7 billion by 2026.</footnote>
ten criteria for open standards (and the underlying processes; Stage 2). In addition to “due process” and “consensus,” these criteria include, e.g., that all may participate and that everyone may access and use the documents produced. Jakobs (2016) adds another six criteria. These are mainly supposed to reveal any potential, e.g., economic or political interests (on the side of the stakeholders and/or the SSO(s) involved) that may influence the standardization process and include:

* Open rules: The SSOs’ bylaws, regulations, etc. are publicly available.

* Transparent membership: names and affiliations of the WG members are known.

* Open roles: names and affiliations of those holding leadership roles are made public.

* Open work programs: which SSO is doing what.

* Open links: which SSO co-operates with whom and how.

* Open history: All SSOs associated with a standard are known.

These criteria as well originally refer to the “normal” standards setting process (i.e., Stage 2) but may also be applied to a process at Stage 1.

## 7.6 Select SSO(s)

Picking the most suitable SSO for a given task is not a trivial endeavor. Due to the absence of a coordinating entity, individual SSOs may co-operate, compete, or just ignore each other; activities of individual SSOs may well overlap. Accordingly, technical appropriateness and proven competence in the area in which new standards are to be developed may not be sufficient selection criteria.

Each individual SSO has its own rules, guidelines, and bylaws that govern all aspects of its standards setting process (i.e., Stage 2). Individual SSOs’ characteristics differ widely in terms of, e.g., openness, power distribution, means for decision-making, and obtaining and/or increasing power. The respective membership base will also be of interest; it will be beneficial if business partners (e.g., suppliers) are involved, especially if they hold any leading positions.

The ease (or lack thereof) with which new work items may be proposed and initiated (and by whom), and the boundary conditions that apply are further important aspects. Specifically, the willingness to support a two-stage process and to implement the necessary interface on the SSO side will need to be established.

## 7.7 Implement and Manage Interface Between Stages

This is perhaps the most difficult part. The technical and the non-technical side will need to communicate here. Societal, environmental, etc. requirements will have to be conveyed to the technical process and technical boundary conditions and potential limitations in the opposite direction. This implies that ideally individuals
with expertise in and experience with both sides (the technical and the non-technical) should communicate to avoid friction losses. These people would act as boundary spanners. This concept has originally emerged from the field of innovation studies (see, e.g., Tushman 1977) but may also be applied to standardization (Dokko and Rosenkopf 2010). To paraphrase Tushman (1977): boundary spanners serve the need of one stage of the standardization process to gather information from and transmit information to another stage.

## 7.8 Form Alliances

History shows that in very many cases, strong alliances (between, e.g., manufacturers, users, distributors, complementors, etc.) have been crucial for the success of standardization initiatives (see, e.g., Cusumano et al. (1992) and den Uijl and de Vries (2013) for two of the most prominent cases).

In this particular case, such alliances will need to be formed primarily between smart city authorities (Stage 1) on the one hand and, e.g., suppliers, complementors, and service providers (Stage 2) on the other. Such alliances will complement the activities of the boundary spanners (see above).

## 7.9 Some Brief Concluding Remarks

Smart systems are here to stay. The impact they are highly likely to exert will be tremendous, for businesses, governments, and citizens alike. The nature of this impact, however, still remains largely unclear—we do not have a crystal ball. To be able to eventually reap the potential benefits smart systems offer, their designers need to work hand in hand not just with their prospective (commercial) users (which is frequently the case in “traditional” ICT standards setting) but also with policymakers and legislators (from different levels) as well as with those whose voices are not normally heard during technology development, standardization, and implementation but who stand to benefit—or suffer—the most from smart technologies: consumers, citizens, workers, and the organizational stakeholders who represent certain groups of them.

Despite relevant ongoing developments, “responsible innovation” remains the exception, certainly in the private sector. During its implementation phase, a technology may well be adapted to its environment (and vice versa), but it can no longer be fundamentally changed. This leaves the standardization process as the one platform where societal stakeholders stand a chance to contribute their expertise to an early stage of technical development. To actually be able to do so, however, the current standardization system will need to be modified, in terms of both its structure and its processes. This chapter has discussed some potential changes, the
implementation of which would support the development of technologies that are
beneficial for society, as opposed to those which benefit primarily large companies.

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Chapter 8
# Toward a Consensual Definition for Smart Tourism and Smart Tourism Tools

<figure><img src="image_48.png" /></figure>

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António Galvão ID, Fernando Brito e Abreu ID, and João Joanaz de Melo ID

**Abstract** Smart Tourism (ST) stems from the concepts of e-tourism, which is focused on the digitalization of processes within the tourism industry, and digital tourism, which also considers the digitalization of the tourist experience. The earlier ST references found regard ST destinations and emerge from the development of Smart Cities.

Our initial literature review on the ST concept and Smart Tourism Tools (STT) revealed significant research uncertainties: ST is poorly defined and frequently linked to the concept of Smart Cities; different authors have different, sometimes contradictory, views on the goals of ST; STT claims are often only based on technological aspects, and their “smartness” is difficult to evaluate; often the term “Smart” describes developments fueled by cutting-edge technologies, which lose that status after a few years.

This chapter is part of the ongoing initiative to build an online observatory that provides a comprehensive view of STTs’ offerings in Europe, known as the **Euro-pean STT Observatory**. To achieve this, the observatory requires methodologies and tools to evaluate “smartness” based on a sound definition of ST and STT while also being able to adapt to technological advancements. In this chapter, we present the results of a participatory approach where we invited ST experts from around the world to help us achieve this level of soundness. Our goal is to make a valuable contribution to the discussion on the definition of ST and STT.

A. Galvão (✉)

Instituto Universitário de Lisboa (ISCTE-IUL), ISTAR, Lisboa, Portugal

NOVA University Lisbon - School of Science and Technology, CENSE, Caparica, Portugal

e-mail: antonio.galvao@iscte-iul.pt

F. Brito e Abreu

Instituto Universitário de Lisboa (ISCTE-IUL), ISTAR, Lisboa, Portugal

e-mail: fba@iscte-iul.pt

J. Joanaz de Melo

NOVA University Lisbon - School of Science and Technology, CENSE, Caparica, Portugal

e-mail: amg13172@campus.fct.unl.pt; jjm@fct.unl.pt