The Smart City Trap: Privacy Erosion and Inequality at Scale
The pitch is always the same. A gleaming control room, banks of screens flickering with real-time data, algorithms humming away beneath the surface, optimising traffic flow, predicting crime, routing ambulances, trimming energy waste. The smart city, we are told, will be cleaner, safer, faster, and more efficient. It will save money. It will save lives. And increasingly, as municipal budgets tighten and technology vendors sharpen their sales decks, the conversation has narrowed to a single question: what is the return on investment?
That question is not inherently wrong. Cities should spend public money wisely. But when ROI becomes the dominant lens through which urban AI systems are evaluated, something important slips out of focus. The residents whose data powers these systems, whose movements are tracked and whose behaviours are modelled, are quietly reclassified. They stop being citizens with rights and start becoming data points with value. And once that shift takes hold, the consequences for privacy, social equity, and democratic participation are not hypothetical. They are already unfolding in cities around the world.
The Trillion-Dollar Bet on Algorithmic Urbanism
The scale of investment in smart city technology has become staggering. According to MarketsandMarkets, the global smart cities market is projected to grow from USD 699.7 billion in 2025 to USD 1,445.6 billion by 2030, at a compound annual growth rate of 15.6 per cent. Fortune Business Insights places the 2025 valuation even higher, at USD 952.13 billion, with projections reaching USD 6,315 billion by 2034. Whichever estimate you choose, the trajectory is unmistakable: governments and corporations are pouring unprecedented sums into the digital transformation of urban life, driven by projections that over 68 per cent of the global population will live in cities by 2050.
The McKinsey Global Institute, in its landmark 2018 report “Smart Cities: Digital Solutions for a More Livable Future,” found that smart city applications could improve quality-of-life indicators by 10 to 30 per cent. The numbers were compelling: commute times reduced by 15 to 20 per cent, emergency response times accelerated by 20 to 35 per cent, crime incidents (assault, robbery, burglary, and auto theft) lowered by 30 to 40 per cent, greenhouse gas emissions cut by 10 to 15 per cent. McKinsey also noted that roughly 60 per cent of the initial investment could come from private-sector actors, a detail that has shaped procurement models ever since. The report estimated that in a city with an already-low emergency response time of eight minutes, smart systems could shave off almost two minutes; in a city starting with an average response time of fifty minutes, the reduction might exceed seventeen minutes.
But there is a gap between what these technologies can do and the question of who they do it for. The shift towards ROI-driven deployment is now well documented. A report by IoT Tech News observed that smart city deployments are moving beyond pilot phases as operational leaders prioritise ROI and scalable infrastructure. Investment priorities are consolidating around proven technologies rather than experimental solutions, with AI-powered traffic management, smart grid systems, and digital government services receiving approximately 70 per cent of available funding. Outcome-based contracts, where technology providers guarantee specific performance metrics such as energy savings or traffic flow improvements, are expected to represent 60 per cent of new smart city deals by 2028.
The logic is seductive. If a sensor network can demonstrably reduce energy costs or speed up refuse collection, the business case writes itself. But this framing systematically undervalues outcomes that are harder to quantify: civil liberties, community cohesion, democratic agency, the right to be left alone. When the spreadsheet becomes the arbiter of urban policy, the city risks optimising itself into a place that works beautifully for data but poorly for people.
Surveillance by Another Name
The privacy implications of AI-driven urban management are not speculative. They are architectural. Every smart city system that monitors, predicts, or optimises relies on the continuous collection of data about human behaviour. Traffic cameras capture licence plates. Wi-Fi networks log device locations. Smart meters record energy usage patterns that can reveal when residents are home, asleep, or away. Acoustic sensors detect gunshots but also record conversations. Facial recognition systems, despite regulatory pushback in Europe, remain standard infrastructure in many Asian cities. And the risk of function creep is ever-present: technology deployed for one purpose, such as disaster management or traffic optimisation, can be quietly repurposed for more invasive surveillance activities without the knowledge or consent of the people it monitors.
The European Union's AI Act, which entered into force on 1 August 2024, represents the most significant legislative attempt to draw boundaries around these capabilities. Article 5 of the Act prohibits real-time remote biometric identification systems in publicly accessible spaces for law enforcement, with limited exceptions for serious crimes and terrorist threats. It bans AI systems that scrape facial images from the internet or CCTV footage and prohibits biometric categorisation systems that deduce race, political opinions, religious beliefs, or sexual orientation. Violations carry fines of up to 35 million euros or 7 per cent of global annual turnover. The Act also prohibits AI systems that evaluate or classify people based on their social behaviour, predict a person's risk of committing a crime, or infer emotions in the workplace or educational institutions.
These prohibitions, which took effect in February 2025, are meaningful. But they apply only within the EU, and even there, the Act classifies many other forms of remote biometric identification (such as retrospective facial recognition using closed-circuit television footage) as “high risk” rather than prohibited. By August 2026, high-risk AI systems must be fully compliant with the Act's requirements for risk assessment, human oversight, and transparency. The question is whether enforcement will match ambition, particularly as the smart city consulting market, valued at USD 5.7 billion in 2025 according to HTF Market Intelligence, creates powerful commercial incentives to push the boundaries of what is permissible.
Outside Europe, the picture is far starker. In China, the convergence of smart city infrastructure with state surveillance has produced what researchers at the Australian Strategic Policy Institute describe as “City Brain” systems, where AI integrates data from cameras, sensors, social media, and government databases into unified platforms for urban control. The Chinese AI company Watrix has developed gait recognition software, incubated by the Chinese Academy of Sciences, capable of identifying individuals from up to 50 metres away, even when their faces are covered. As Watrix CEO Huang Yongzhen told the South China Morning Post: “Cooperation is not needed for them to be recognised by our technology.”
In Xinjiang province, these technologies have been deployed as instruments of ethnic persecution. The Chinese state collects biometric data from Uyghur Muslims, monitors their movements through GPS, and tracks their religious practices using AI-powered surveillance networks. According to the National Endowment for Democracy, PRC-sourced AI surveillance solutions have diffused to over eighty countries worldwide. Hikvision and Dahua, two Chinese surveillance camera manufacturers, jointly accounted for roughly 34 per cent of the global market as of 2024. Through the Belt and Road Initiative, Chinese companies have provided 22 African countries with public security systems including cameras, biometrics, internet controls, and surveillance infrastructure.
The lesson from China is not that all smart cities will become surveillance states. It is that the same technology can serve radically different political purposes, and that the distance between urban optimisation and authoritarian control is shorter than many democratic societies have acknowledged.
The Ghost in the Data
Shoshana Zuboff, the Harvard Business School professor emerita whose 2019 book The Age of Surveillance Capitalism redefined the debate about data extraction, has argued that surveillance capitalism “unilaterally claims human experience as free raw material for translation into behavioural data.” In the smart city context, this dynamic takes on a distinctly spatial dimension. The city itself becomes the extraction zone. Every journey, every transaction, every interaction with public infrastructure generates data that can be captured, analysed, and monetised.
Zuboff's framework illuminates a fundamental tension in smart city governance. Technology vendors frame data collection as a public good: better services, faster responses, more efficient resource allocation. But the commercial models underpinning many smart city deployments depend on the same data having private value. When a municipality partners with a technology company to deploy sensors across its transport network, who owns the data those sensors generate? Who decides how it is used, stored, and shared? And who profits? These are not abstract questions. They sit at the heart of every public-private partnership in the smart city space, and the answers are rarely negotiated in public view.
The collapse of Sidewalk Labs' Quayside project in Toronto offers a cautionary tale. Announced in October 2017 with the backing of Alphabet (Google's parent company), the project envisioned a high-tech waterfront neighbourhood featuring autonomous vehicles, heated sidewalks, and pervasive sensor networks. Sidewalk Labs committed USD 50 million to the planning phase and projected USD 38 billion in private investment over two decades. The company claimed the development would create 44,000 jobs and generate CAD 4.3 billion in annual tax revenues.
But the privacy backlash was swift and sustained. Ann Cavoukian, who served 17 years as Ontario's Information and Privacy Commissioner (from 1997 to 2014) and was hired as a consultant on the project, resigned in October 2018 after Sidewalk Labs refused to commit to de-identifying all sensor data at the point of collection. Instead, the company proposed a “civic data trust” run by an independent group with the power to approve technologies that did not de-identify data at the point of collection, a structure that Cavoukian viewed as fundamentally inadequate. The Canadian Civil Liberties Association filed a lawsuit in April 2019. The grassroots campaign BlockSidewalk mobilised public opposition, drawing explicit parallels to the movement that had forced Amazon to abandon its planned second headquarters in Queens, New York.
The project was cancelled in May 2020, with Sidewalk Labs CEO Daniel Doctoroff citing the economic impact of COVID-19. But observers widely agreed that the pandemic was merely the final blow. The project had been fatally undermined by its failure to address legitimate concerns about data sovereignty, corporate control, and the absence of meaningful consent mechanisms for residents. A controversial June 2019 scope expansion, in which Sidewalk Labs proposed a project spanning 77 hectares (sixteen times the original five-hectare plan), had further eroded public trust.
As Cavoukian warned at the time, other cities would take notice of how Sidewalk Labs “flagrantly” underestimated public privacy concerns. The data privacy strategy that the company used in Toronto, she argued, was unlikely to work anywhere else.
Sensor Deserts and Algorithmic Redlining
If privacy concerns affect everyone in a smart city, the equity implications are distributed unevenly. There is mounting evidence that AI-driven urban management systems do not merely reflect existing social inequalities; they amplify and entrench them.
The mechanism is straightforward. Smart city systems rely on sensor data. Sensors cost money. And the decisions about where to place them are shaped by the same political and economic forces that have always determined which neighbourhoods receive investment and which do not. The result is what researchers Rachel S. Franklin of Newcastle University and Jack Roberts of the Alan Turing Institute have called “sensor deserts”: areas where the absence of monitoring infrastructure renders communities invisible to algorithmic decision-making.
In their 2022 study published in the Annals of the American Association of Geographers, Franklin and Roberts examined Newcastle's Urban Observatory sensor network and found significant coverage gaps. Relatively deprived, post-industrialised areas along the north bank of the River Tyne were underrepresented, despite 23 per cent of Newcastle's population living in the 10 per cent most deprived areas nationally. Environmental justice research, they noted, confirms that these populations are likely to be more exposed to pollution and other urban hazards, making them a priority for monitoring. Yet the sensor infrastructure systematically overlooked them. The researchers developed a decision support tool demonstrating the significant trade-offs involved in sensor placement: increasing coverage of workplaces, for example, necessarily reduced coverage of older persons due to their different locations in the city.
This pattern is not unique to Newcastle. A January 2026 analysis by the University of the People found that placing more sensors in affluent neighbourhoods leads to interventions that disproportionately benefit those communities while leaving others in “data shadows,” reinforcing cycles of neglect where the lack of data becomes a justification for the lack of investment. The phenomenon resembles a digital-era version of redlining: not a line drawn on a map by a bank officer, but an absence of data that produces the same discriminatory effect.
The bias extends beyond sensor placement to the participatory systems that smart cities rely upon for citizen feedback. Research by Constantine Kontokosta and Boyeong Hong at NYU's Urban Intelligence Lab, published in Sustainable Cities and Society in 2021, examined Kansas City's 311 reporting system and found that despite greater objective and subjective need, low-income and minority neighbourhoods were less likely to report street condition or nuisance issues. The study analysed 21,046 resident satisfaction survey responses, more than 500,000 service reports, and 29,884 objective street pavement condition assessments. The findings were stark: predictive algorithms trained on this complaint data would systematically under-allocate resources to the neighbourhoods that needed them most, further reinforcing existing disparities. The likelihood of a resident calling 311, the researchers found, depended heavily on awareness, trust in city services, and socioeconomic factors, meaning that the communities with the greatest need were precisely those least likely to be heard.
The implications for smart city governance are profound. When algorithmic systems are trained on biased data, they do not merely reproduce historical patterns of neglect. They encode them into infrastructure decisions that can shape urban life for decades. When a machine learning model recommends where to build a hospital, extend a metro line, or increase police patrols, it is making choices that will persist long after the algorithm itself has been updated or replaced. If the underlying data reflects discrimination, the AI automates inequality at scale, transforming historical bias into what one analysis described as “architectural permanence.”
Predictive Policing and the Feedback Loop of Injustice
Nowhere is the equity problem more acute than in predictive policing, one of the earliest and most controversial applications of AI in urban management. The premise is simple: use historical crime data to predict where future crimes are likely to occur, then deploy officers accordingly. The problem is equally simple: historical crime data does not measure where crime happens. It measures where police have been. A 2019 study by Rashida Richardson, Jason Schultz, and Kate Crawford at the AI Now Institute, published in the New York University Law Review, described how some police departments rely on “dirty data,” defined as data “derived from or influenced by corrupt, biased, and unlawful practices,” to inform their predictive systems.
The Los Angeles Police Department adopted the predictive policing tool PredPol in 2011, claiming reductions in burglary in pilot districts. By 2020, the programme was discontinued after independent audits revealed that the system had created a feedback loop: police patrols generated more recorded incidents in already-targeted areas, which reinforced the algorithm's prediction that those areas were high-crime, which generated more patrols. An audit by the LAPD inspector general found “significant inconsistencies” in how officers calculated and entered data, further fuelling biased predictions. The department's separate LASER programme directed heightened surveillance against minority neighbourhoods based on historical arrest records that were themselves products of discriminatory policing.
Chicago's experience followed a parallel trajectory. In 2012, the Chicago Police Department implemented the “Strategic Subject List” (colloquially known as the “Heat List”), an algorithm designed to identify individuals at higher risk of involvement in gun violence. The system disproportionately targeted young Black and Latino men, subjecting them to intensified surveillance and police interactions. An analysis found that 85 per cent of those flagged had no subsequent involvement in gun violence. Chicago abandoned the system in 2020.
In January 2025, seven members of the US House and Senate jointly wrote to the Department of Justice calling for an end to federal funding of predictive policing projects “until the DOJ can ensure that grant recipients will not use such systems in ways that have a discriminatory impact.” The EU AI Act classifies predictive policing systems as “high-risk,” requiring conformity assessments, documentation, and human oversight. The UK Home Office AI Procurement Guidelines, issued in 2025, require explainability, bias testing, and ethical board review before operational deployment of such systems.
These regulatory responses are welcome but belated. For the communities that lived under the algorithmic gaze for years, the damage has been done. And the underlying structural problem remains: any predictive system trained on data generated by a biased institution will reproduce and amplify that bias, regardless of how sophisticated the algorithm.
Democratic Deficits in the Automated City
Beyond privacy and equity, there is a deeper question that smart city advocates rarely confront: what happens to democratic participation when urban management is increasingly delegated to algorithmic systems?
The Organisation for Economic Co-operation and Development has noted that AI is becoming an integral part of digital government worldwide, facilitating automated internal processes, improving decision-making and forecasting, and enhancing fraud detection. The OECD also recognises that AI can facilitate innovation in civic participation, generating simulations and visualisations that allow citizens to engage with complex urban planning decisions. In Hamburg, Germany, the CityScope platform developed at MIT Media Lab used 3D and AI technologies to engage stakeholders in deciding on 161 viable locations to house refugees. In Greece, the opencouncil.gr platform uses AI to automatically transcribe local council meetings and generate summaries, making local governance more accessible.
But these are exceptions rather than the rule. In most smart city deployments, the algorithmic layer sits between citizens and the decisions that affect them, operating with minimal transparency and limited mechanisms for democratic input. When a traffic management system reroutes vehicles through a residential neighbourhood based on real-time congestion data, the residents of that neighbourhood have not voted on the decision, been consulted about it, or in most cases even been informed. When a resource allocation algorithm determines which parks receive maintenance funding and which do not, the affected communities have no insight into the criteria or the weighting. The decisions are made, in effect, by code.
Digital twins add another dimension to this problem. These virtual replicas of physical urban systems, combining real-time sensor data with simulation models, are increasingly used to test infrastructure scenarios before implementing them in the real world. A 2025 review in the Journal of the American Planning Association warned that AI algorithms used in digital twin urban planning “will likely rely on historical data for training models” and that “these historical data may carry biases inherited from past discriminatory practices and systemic inequalities.” When an AI model recommends building new infrastructure, extending a metro line, or reallocating resources, it shapes urban life for decades. If the training data favours investment in affluent areas over marginalised ones, the digital twin will recommend resource allocation that continues to neglect underserved communities, all while presenting its recommendations with the veneer of computational objectivity.
A January 2026 study published in Telematics and Informatics examined what the authors called the “smart governance paradox”: the finding that smart city development may intensify socioeconomic inequalities by excluding digitally disadvantaged groups from participatory governance. Wealthier communities with greater digital literacy and connectivity receive prompt responses from algorithmic systems, while lower-income neighbourhoods, lacking broadband access or the capacity to navigate digital platforms, are effectively shut out of the feedback loop that determines how city resources are distributed.
The paradox is sharp. The technologies that promise to make government more responsive also risk making it less accountable. When decisions are automated, the lines of responsibility blur. A human official can be voted out of office. An algorithm cannot. A council meeting can be attended by the public. A machine learning model's training data cannot be interrogated by a concerned resident. The democratic infrastructure that allows citizens to challenge, contest, and shape the decisions that govern their lives is being quietly bypassed, not by design necessarily, but by the relentless logic of efficiency.
Barcelona, Amsterdam, and the Counter-Models
Not every city has followed the ROI-first playbook. Some have attempted to build smart city infrastructure around democratic principles rather than despite them, and their experiences offer instructive contrasts.
Barcelona's trajectory under Mayor Ada Colau, who took office in 2015, represents perhaps the most ambitious attempt to reimagine the smart city as a democratic project. Francesca Bria, an Italian innovation economist appointed as the city's Chief Digital Technology and Innovation Officer in 2016, argued that the traditional smart city approach was “technology-heavy, pushed by a Big Tech agenda with a lack of clarity around data ownership, algorithm transparency, and public needs.” Under her leadership, Barcelona pursued a model of “technological sovereignty” built on several pillars.
First, the city mandated that digital infrastructure and the data it generates should be treated as a public good, owned and controlled by citizens rather than corporations. Barcelona adopted a technological sovereignty guide and digital ethical standards stipulating that digital information and infrastructure used in the city should be publicly owned and controlled. Second, Barcelona rewrote its procurement policies to prioritise open-source software, committing 70 per cent of its budget for new digital services to free and open-source development, a move designed to eliminate vendor lock-in and retain public control over data. Third, the city launched the DECODE project, a five-million-euro EU-funded initiative to develop blockchain-based tools that would give citizens granular control over how their personal data was shared and with whom. Fourth, and most significantly, Barcelona deployed Decidim, an open-source participatory democracy platform that enabled direct citizen engagement in urban planning and budgeting. Nearly 40,000 people and 1,500 organisations contributed over 10,000 proposals through the platform, with 71 per cent of citizen proposals ultimately accepted and incorporated into the city's Municipal Action Plan. By 2023, Decidim had grown to over 120,000 registered participants and more than 31,000 proposals across 126 participatory processes.
Amsterdam pursued a complementary approach through its Tada manifesto, developed between 2017 and 2019 by a coalition of 60 experts, organisations, politicians, and businesses convened by the Amsterdam Economic Board. Tada established six core values for the responsible use of data and technology in the city: inclusivity, citizen control, human-centricity, legitimacy, openness and transparency, and universality. Deputy Mayor Touria Meliani translated these principles into concrete policy, including proposals for data minimisation, privacy by design, open data by default, and a ban on Wi-Fi tracking. The city also launched a digital map showing where the municipality had placed cameras and sensors and what data they collected, and created “My Amsterdam,” a personal digital environment where residents could view all information the municipality held about them. Amsterdam's Datalab conducts neutral audits of the algorithms used to route the 250,000 issues in public space reported annually, testing whether those algorithms are biased towards particular privileged areas or problems.
These models are imperfect. Academic research on Barcelona's implementation has noted a gap between inspiring rhetoric and practical delivery, and critics have argued that participatory platforms can create an illusion of engagement while real decision-making power remains with elected officials and their advisers. But the fundamental principle they embody (that citizens should have sovereignty over the data generated in their city, and that democratic participation should be designed into smart city systems rather than bolted on as an afterthought) stands in stark contrast to the vendor-driven, ROI-first approach that dominates most deployments globally.
Governing What We Have Built
The challenge facing cities in 2026 is not whether to adopt AI-driven urban management. That train has left the station. The challenge is whether the governance frameworks surrounding these systems will evolve fast enough to protect the rights they threaten.
Several principles are emerging from the academic literature and from the practical experiences of cities that have grappled with these questions. Igor Calzada of the University of the Basque Country and colleagues, writing in Discover Cities in 2025, call for an “Urban AI Social Contract” that would embed digital inclusion, equity, and democratic legitimacy into the design of AI-enabled cities. Their work argues that participatory governance architectures, cross-sectoral policy coordination, and mechanisms such as data cooperatives are essential to ensuring that AI deployments serve the public interest rather than private profit.
The OECD recommends structuring smart city governance across seven functional layers, embedding cross-cutting principles of human agency, participation, fairness, transparency, accountability, and sustainability. Research published in Frontiers in Sustainable Cities in 2024 proposes a comprehensive governance framework integrating privacy-centric AI, fairness-aware algorithms, and public engagement strategies.
These frameworks share common elements. They call for algorithmic transparency: citizens should be able to understand how automated decisions are made and on what basis. They call for mandatory bias audits: AI systems that allocate public resources or determine policing priorities should be regularly tested for discriminatory outcomes. They call for meaningful consent: residents should have genuine choices about what data is collected about them and how it is used, not merely the option to accept or reject an opaque terms-of-service agreement. And they call for democratic oversight: elected officials and the publics they represent should retain authority over the goals that algorithmic systems are designed to optimise.
The question is whether these principles will be adopted as binding requirements or remain aspirational. The EU AI Act represents a significant step, but its geographic scope is limited and its implementation timeline extends to 2027 for many provisions. In the United States, the White House Office of Management and Budget issued a landmark policy in March 2024 expanding reporting requirements for AI systems “presumed to be rights-impacting,” but this policy does not cover state and local law enforcement, where the vast majority of smart city policing applications operate.
Meanwhile, the commercial pressures driving ROI-first deployments show no sign of abating. Outcome-based contracts tie vendor compensation to measurable performance metrics, creating strong incentives to maximise the volume and granularity of data collection. Public-private partnerships, which have become the dominant funding model for smart city projects, align public policy objectives with private-sector profit motives in ways that can obscure accountability. And the sheer pace of technological change means that regulatory frameworks are perpetually playing catch-up, governing yesterday's capabilities while tomorrow's are already being deployed.
What Citizens Deserve From Their Cities
The smart city is not a neutral proposition. It is a political project dressed in technical language. The sensors, algorithms, and data pipelines that constitute its infrastructure are not merely tools for improving urban efficiency. They are instruments of power: power to observe, to predict, to classify, to include, and to exclude.
The evidence assembled here points to a clear set of risks. Privacy erosion is not a bug in the smart city model; it is a feature of systems designed to generate continuous behavioural data at population scale. Equity failures are not aberrations; they are predictable consequences of sensor networks and algorithmic systems that reflect and amplify the socioeconomic hierarchies already embedded in urban geography. Democratic deficits are not temporary growing pains; they are structural outcomes of governance models that prioritise computational efficiency over citizen agency.
None of this means that AI-driven urban management is inherently harmful. The McKinsey data on reduced emergency response times and lower crime rates describe real benefits with real human value. The question is not whether these technologies should exist, but who they should serve, who should govern them, and what rights citizens retain in a city that increasingly thinks for itself.
Barcelona and Amsterdam have demonstrated that alternative models are possible: cities where data is treated as a public good, where algorithmic decisions are subject to democratic scrutiny, and where participation is not an afterthought but a design principle. Toronto's Quayside failure has demonstrated that ignoring citizen concerns about surveillance and data sovereignty carries tangible costs, measured not just in lost investment but in eroded public trust.
The trillion-dollar smart city industry will continue to grow. The algorithms will become more sophisticated. The sensors will become cheaper and more pervasive. The question that remains, and that no amount of ROI analysis can answer, is whether the cities of the future will be governed by their residents or merely optimised around them.
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Tim Green UK-based Systems Theorist & Independent Technology Writer
Tim explores the intersections of artificial intelligence, decentralised cognition, and posthuman ethics. His work, published at smarterarticles.co.uk, challenges dominant narratives of technological progress while proposing interdisciplinary frameworks for collective intelligence and digital stewardship.
His writing has been featured on Ground News and shared by independent researchers across both academic and technological communities.
ORCID: 0009-0002-0156-9795 Email: tim@smarterarticles.co.uk