Contributors and Project Details
The SoReMo Initiative at Illinois Institute of Technology
This student project was developed under the auspices of the Socially Responsible Modeling, Computation, and Design (SoReMo) Initiative at Illinois Tech in the Spring 2026 semester. SoReMo is a multidisciplinary forum that empowers student fellows to apply technical knowledge—in modeling, data science, design, etc.—toward solving pressing societal challenges. By connecting student research with a network of faculty advisors and community stakeholders, SoReMo ensures that technical innovation is guided by ethics, equity, and transparency.
“The People v. Hasty AI” embodies the SoReMo mission by taking the “invisible” data of the Illinois AI boom and translating it into actionable insights for the communities most impacted by this industrial expansion.
Research Areas/Relevant Topics: Artificial Intelligence (AI) Ethics, Urban Planning, Environmental Justice, Public Policy, Data Visualization
Problem Statement
Residents in affected Illinois corridors often encounter the sudden physical emergence of massive industrial complexes without prior public notice or community consent. This phenomenon represents an “invisible giant”—infrastructure that, while physically imposing, remains operationally opaque due to windowless architecture and strict nondisclosure agreements. The impact is felt not through direct engagement, but through systemic shifts: unpredicted utility rate spikes and diminished reliability of local water resources.
While the rest of the world profits from the speed and convenience of artificial intelligence (AI), residents are left to shoulder the weight of their local giant’s physical footprint. This research addresses the unnotified industrialization of residential zones, leading to socioeconomic externalities such as utility “cross-subsidies” and environmental degradation. Through the defining of the “annoyance threshold,” this study posits that the stability of host communities must be balanced with technological progress through a formalized regulatory model.
Introduction
The “annoyance threshold” is a term that seeks to define the “tipping point” of a person or group’s tolerance towards a particularly bothersome stressor. It can be applied to many different contexts: from a legal requirement to a commonly accepted social norm. To contextualize it through example, think of how a factory is allowed to release ever so much pollutant before they breach a pre-defined legal limit. In the context of AI data centers, unlike most other industrial infrastructure, this tipping point has yet to be legally defined at the federal level. In the meantime, it must be defined on a state-by-state basis. In working towards this definition, this project summarizes the issue of AI infrastructure into three critical points:
- Environment: Environmental Degradation
- Social: Lack of Transparency and Community Devaluation
- Economic: Stress on the Power Grid
What exactly is this project trying to accomplish?
This paper will serve as a comprehensive assessment of the true cost of Illinois’s data center boom. We will begin by laying the groundwork: examining existing research on the stressors inherent to these facilities as well as definitions of the annoyance threshold. This will be followed by a deep-dive of case studies—including the successful community opposition in Naperville and the high-profile conflict of xAI in Memphis—to illustrate the real-world stakes. To ground this research, we will introduce an interactive data visualization tool that maps these facilities against environmental statistics and socioeconomic indicators. This tool is the first step towards mathematically formulating the AI-infrastructure annoyance threshold.
Finally, we will argue that regulation, rather than being an obstacle to progress, can serve as a catalyst for innovation. We will present potential compromises that serve the interests of both the company and the community, ensuring that technological growth no longer requires a sacrifice of local peace and stability.
This project presents a framework for defining the annoyance threshold and offers actionable recommendations for residents, advocates, and policymakers to reclaim agency in the face of hasty AI growth.3
Literature Review
Background: AI Impact on Communities
Recent scholarship increasingly recognizes that AI infrastructure is not immaterial. It is deeply tied to physical systems from electricity grids and water reserves to regional environmental conditions. Researchers studying data center expansion emphasize that AI workloads are rapidly becoming a major driver of global electricity demand; on top of this, it is also introducing significant water consumption through cooling processes that are rarely visible to the public. Both de Vries-Gao (2025) and Patel et al. (2025) suggest that AI-related data center activity could generate tens of millions of tons of carbon emissions annually while consuming hundreds of billions of liters of water worldwide, highlighting the growing environmental footprint associated with large-scale computation. These findings challenge the popular perception of AI as a purely digital technology. We should instead position it as an industrial infrastructure with measurable ecological consequences.
Beyond energy use, researchers have begun examining how these environmental impacts translate into lived community experiences. Lifecycle analyses show that data centers contribute indirectly to air pollution through electricity generation, producing measurable public health burdens that may disproportionately affect nearby populations (Han et al., 2024). Social science research further argues that community concerns often stem not only from environmental exposure but from procedural issues such as limited transparency, restricted access to project information, and insufficient local participation in decision-making processes according to Sovacool et al. (2022). Policy analyses similarly note that rapid data center expansion in the United States frequently occurs faster than governance frameworks can adapt. This creates uncertainty about accountability, disclosure, and long-term community protections (Walker & Goldsmith, 2026).
Further exacerbating the issue, other sources connect these developments to broader questions of environmental and data justice. Researchers from both Vera et al (2019) and Lucivero (2019) argue that technological infrastructures distribute benefits and harms unevenly, especially when environmental monitoring data or operational disclosures remain inaccessible to affected communities. At the same time, emerging research on energy burden demonstrates that increases in large-scale electricity demand can intersect with socioeconomic vulnerability, potentially shifting infrastructure costs onto residents through higher energy expenditures (Garland et al., 2025). Together, these studies establish a foundation for examining AI data centers not only as technological innovations, but as socio-environmental systems whose impacts must be evaluated through environmental equality, governance transparency, and economic fairness.
Figure 1: (Patel et al.)
Figure 2: (Patel et al.)
Figures 1 and 2 are graphics taken from Patel et al., 2025; a study titled “The Environmental Impact of AI Servers and Sustainable Solutions”. While Figure 1 quantifies a global energy trajectory that demands haste, Figure 2 maps the localized footprints (water and carbon) that suffer because of it.4 As a pair, they demonstrate the inevitable collision between exponential computational growth and the finite physical resources of the communities that host these facilities. This provides a data-driven justification for the immediate implementation of our proposed annoyance threshold as a regulatory safeguard.
Background: Annoyance Threshold
To contextualize the study of the annoyance threshold, it is necessary to examine how industrial stressors have been historically quantified. Current research primarily frames these thresholds through an acoustic lens, providing a foundational understanding of human tolerance for industrial encroachment.
Theoretical Foundation: The Habituation Effect
Early psychoacoustic studies by Spieth (1956) established that annoyance thresholds are not absolute but are products of a resident’s baseline environment. His findings revealed that individuals in quiet settings exhibit a tolerance roughly 15 decibels lower than those accustomed to industrial noise. This suggests that a community’s “accepted limit” for stress is often artificially inflated through habituation; by the time an “invisible giant” breaks ground in a quiet community, the industry has effectively decided that residents will simply “learn to be bothered less”.
Precedents in Quantitative Annoyance Modeling
To acknowledge established quantitative rigor in the field of annoyance modeling, we look to the widely cited work of Miedema and Oudshoorn (2001). Their research provides a mathematical foundation for predicting community stress by calculating the percentage of a population expected to be “highly annoyed” (%HA) based on their level of industrial exposure from aircrafts, road traffic, and railways.
The primary equation from this study estimates the percentage of people highly annoyed (%HA) at a given Day-Night Level (DNL). Based on Table 5 of the Miedema and Oudshoorn (2001) study, the specific polynomial for road traffic is:
\[\%HA_{\text{road traffic}} = 9.94 \times 10^{-4}\,(DNL - 42)^3 - 1.523 \times 10^{-2}\,(DNL - 42)^2 + 0.538\,(DNL - 42)\]
Understanding the Variables
- DNL: Measures the total noise exposure over a 24-hour period. To account for the increased sensitivity of residents during nighttime hours, a “penalty” of 10 decibels is added to any noise occurring between 10:00 PM and 7:00 AM.
- The 42 dB Threshold: In this model, 42 is the constant representing the baseline annoyance threshold. When the DNL is at or below 42, the result of the equation is zero, mathematically indicating that the stressor has not yet reached a level that causes significant annoyance for the average person.
Interpretation of High vs. Low Values
- High Value (%HA): A high percentage of “Highly Annoyed” residents indicates that the industrial stressor has significantly breached the community’s tolerance. Because the equation is a polynomial, the percentage of annoyed residents grows rapidly as the DNL increases, showing that even small increases in exposure can lead to widespread community unrest.
- Low Value (%HA): A low value suggests that the stressor is being effectively managed or is situated in an environment where it does not significantly contrast with the existing baseline.
In this project’s interpretation of the annoyance threshold, we seek to simplify the complex interplay between various physical and socioeconomic stressors into a single, actionable metric for local governance. Because existing models like those of Miedema and Oudshoorn (2001) are domain-specific—focusing primarily on acoustic or olfactory nuisances—they do not capture the multi-dimensional burden faced by residents in AI corridors, which includes utility rate spikes, water resource depletion, and the “invisible” operational opacity of data centers. By formalizing the threshold mathematically, we move from anecdotal frustration to a transparent, auditable prioritization tool. This allows policymakers to identify “tipping points” where incremental industrial pressure overlaps with high pre-existing county-level stress. Ultimately, this literature review establishes that while AI is often perceived as a digital abstraction, its expansion is a physical, industrial process that must be governed with the same scientific rigor applied to any other large-scale community stressor.
Case Studies
From Global Expansion to Local Reality
AI infrastructure is often discussed at a global scale, framed through innovation, computational growth, and economic competitiveness. Yet data centers are ultimately local projects. They occupy land, draw electricity and water, alter traffic and construction patterns, and introduce new industrial rhythms into surrounding communities. Across the United States, recent development trends show that while AI systems appear digital, their impacts are material and geographically uneven. Communities experience these projects not as abstract technological progress but through everyday environmental conditions, planning decisions, and economic tradeoffs.
To maintain consistency, each of the following cases will be examined through the aforementioned three interconnected dimensions: environmental, social, and economic. The environmental dimension focuses on physical and operational impacts such as power infrastructure expansion, generator emissions, land conversion, lighting, and noise. The social dimension considers transparency, participation, and community consent during planning and approval processes. The economic dimension evaluates how projects are justified through investment and redevelopment narratives while also recognizing potential long-term costs or perceived inequities. Together, these dimensions help define the report’s concept of the annoyance threshold, the point at which cumulative impacts begin to reshape daily community experience.
A Glimpse at an Established Precedent: The Case of South Memphis and xAI
The city of Memphis provides a glimpse at the impact of unregulated AI infrastructure. Dubbed xAI’s “Colossus,” this project encapsulates the idea of hasty AI expansion, bypassing traditional multi-year planning cycles. This speed was achieved at a significant cost to the local community of Boxtown in South Memphis. Community members would report tangible impacts on their daily lives.
Before examining what’s at stake in Illinois, we will first assess what has already happened in Memphis, following the same environmental, social, and economic cascade of impacts.
Figure 3: (Lindwall, 2025)
Figure 4: (Lindwall, 2025)
Figure 3 is of the xAI Colossus data center in Memphis, Tennessee, as of May 2025. Figure 4 overlays a different data center site, Meta’s “Hyperion”, onto a map of Manhattan. Showing that a single data center footprint can swallow nearly a third of the island, this image illustrates a scale that is difficult to comprehend in isolation.
This facility represents a demand for space and energy that dwarfs the residential neighborhoods surrounding it. Exemplifying this, when the Memphis electrical grid could not immediately meet the massive demands of the Colossus facility, xAI turned to a “quick and dirty” power source: 18 mobile natural gas turbines. As noted by Cyrus Farivar for Forbes, these turbines are roughly 50% less efficient than permanent plants and emit significantly higher levels of nitrous oxide and formaldehyde.
Using data from public NASA satellites as well as the European Space Agency, researchers at the University of Tennessee found that average concentrations of nitrogen oxide have increased by 9% in the Boxtown communities—compared to periods prior to June 2024 (when xAI began constructing Colossus). Further, average concentrations were found to be up by nearly 79% in the area immediately surrounding the facility—when compared to pre-Colossus levels (Chow, 2025).5 Because these turbines were labeled as “temporary,” they bypassed the Environmental Protection Agency’s (EPA) oversight required for permanent stationary plants.
In environmental law, a “temporary” source—often labeled as “temporary-mobile”—is one that remains on a site for less than one consecutive year (Environmental Protection Agency, 2016). It’s typically installed by mounting turbines onto flatbed trailers or shuffling units until the permitted time elapses. Companies have historically evaded the stringent pre-construction permits required for stationary power plants via this “temporary” loophole (Ireland, 2026).
From June 2024 onwards, this strategy in South Memphis has allowed the facility to force residents to breathe in pollutants linked to long-term respiratory issues. What follows is a list of anecdotal quotes from residents, taken from Chow (2025):
- One resident suffered her first severe asthma attack in 15 years, describing a smell of gas wafting into her neighborhood that made her feel like her “chest was caving in.”
- “Our lives and our lungs are being sacrificed on the altar of their capitalistic exploitation,” noted another.
- And finally: “There is no amount of money that can persuade me to accept pollution killing me and my family.”
Figure 5: (Chow, 2025)
Figure 6: (Chow, 2025)
Figures 5 and 6 picture public unrest. The first captures Tennessee state representative Justin Pearson speaking at a rally against xAI on April 25th, 2025. The second is another march, again, against xAI, taken June 17th, 2025.
In January 2026, the EPA issued a final ruling clarifying that all gas turbines supplying steady power to a fixed facility—regardless of whether they are on trailers or intended for short-term use—must be permitted as stationary sources under the Clean Air Act (Kerr, 2026).
While xAI eventually secured stationary permits for some units in Memphis, it simply “copy-pasted” the unpermitted model just across the state line. As of March 2026, the company has been operating up to 27 unpermitted “temporary” turbines in Southaven, Mississippi, to power the same Colossus supercomputer network (Ireland, 2026).6
To suggest that those unhappy with the smog should “just leave” is not a solution; it is a normalization of corporate trespassing that treats local well-being as a secondary concern. No company should have the unilateral right to degrade a living situation without warrant; no person should be forced to make the choice between their homes and health. Some residents simply do not have the financial mobility to uproot their lives, making them captive to whatever pollutants the “invisible giant” breathes out. Within this framing, the choice of location for Colossus tells a more intentional story. Boxtown is a predominantly Black community with a median income of approximately $37,000—less resources to “fight back”. Critics argue it is no coincidence that the “biggest supercomputer in the world” was placed in a neighborhood with low political resistance and high economic vulnerability (Chow, 2025). Accepting this precedent in Memphis normalizes a concept that should be unthinkable, putting every community—including those in Illinois—at risk of becoming the next site.
The social dimension of the Memphis case is defined by a complete lack of transparency. The project was negotiated in secret, leaving the community completely in the dark until the facility was operational. This “ask-for-permission-later” approach is unfit for a community that has historically suffered from environmental hazards. The NAACP and Southern Environmental Law Center have since sent separate intent-to-sue notices for violations of the Clean Air Act, but the damage to the social fabric is already done (Chow, 2025; More Perfect Union, 2025; Hilt, 2026). Residents feel that their agency was traded for corporate speed, setting a dangerous precedent where tech companies can bypass local democratic processes as long as they promise “innovation”.
While the facility brings investment to the region, it also introduces massive ratepayer risk. If the grid requires billions in upgrades to support xAI’s expansion, those costs could be shifted onto the very families who are already struggling with the environmental fallout. Despite the outcry, the cycle of development continues: plans are already underway for an even larger data center in the nearby neighborhood of Whitehaven.
It is important to recognize the wider issue of industrial plants operating near residential areas. Abuse of residential zoning in Memphis predates xAI’s Colossus, and thus, the entire problem of industrial encroachment cannot be attributed to a single company. However, through the global excitement surrounding AI, this trend toward hasty infrastructure is being revived. The hasty model observed here is a repeatable strategy that can be applied to other vulnerable areas.
If Memphis represents this model at its most extreme, Illinois represents opportunity. Unlike South Memphis, where the industrial footprint is already deeply entrenched, the expansion in Illinois is not yet in an advanced stage. By examining local responses—from successful community opposition in Naperville to the rapid industrialization of DeKalb—this project will provide further context as to how the situation is unfolding in Illinois and brainstorm what can be done about the problem before it gets out of hand
In the Context of Illinois
Illinois provides a particularly useful setting for examining these dynamics because it contains examples across the full lifecycle of data center development. Within a single state, operational facilities coexist with newly proposed campuses and at least one high-profile rejected project. This range allows the analysis to move beyond hypothetical impacts and instead observe how expectations, negotiations, and outcomes differ depending on location and community context.
The following case studies therefore shift the discussion from general debates about AI infrastructure toward grounded observations drawn from Illinois communities themselves.
Existing Data Centers in Illinois: Environmental Baselines and Established Infrastructure
Existing facilities provide a baseline for understanding how data centers function once they become normalized parts of regional infrastructure.
Figure 7: (350 East Cermak Road in Chicago | Digital Realty (19.5 MW), 2026)
Figure 8: (Google Maps, 2021a)
In downtown Chicago, sites such as Digital Realty’s ORD10 interconnection hub—figures 7 and 8—operate within dense commercial corridors where large industrial buildings and energy infrastructure have long histories. Public materials emphasize connectivity and modernization rather than environmental transformation, reflecting how impacts in urban environments are often absorbed into existing infrastructure systems. Environmental concerns in these settings tend to center on operational factors such as generator testing, rooftop mechanical systems, and construction disruptions rather than land-use change.
Figure 9: (Google Maps, 2021b)
A different pattern appears in suburban and regional locations. The Meta data center campus in DeKalb—pictured in figure 9—illustrates how environmental impacts can develop incrementally through expansion. City communications describing the campus growth frame the project primarily through investment and development language while implicitly signaling increased utility capacity and infrastructure coordination (Meta Expanding DeKalb Data Center, 2016).
Figure 10: (Google Maps, 2024)
Similarly, Elk Grove Village has evolved into a concentrated data center cluster where multiple facilities operated by companies, such as Stream Data Centers, reinforce the area’s identity as a connectivity hub (Stream Data Centers, 2025). See figures 10 and 11.
In these environments, the environmental question shifts from individual projects to cumulative infrastructure demand, including repeated construction cycles and sustained energy requirements.7
Figure 11: (Google Maps, 2024)
Socially, existing sites often have limited visible public debate, suggesting that once infrastructure becomes established, development is perceived as routine. Economically, municipalities frequently present these facilities as indicators of technological relevance and regional competitiveness, reinforcing acceptance over time.
Proposed and Under-Development Projects: Negotiation and Uncertainty
Proposed projects reveal impacts more clearly because communities encounter them before normalization occurs. The Edged “Project Vector” proposal near the existing Meta campus in DeKalb demonstrates how environmental concerns emerge during planning stages. Public hearings and local reporting highlight discussions surrounding lighting mitigation, stormwater management, and infrastructure integration, indicating attempts to anticipate nuisance impacts before construction begins (Edged Development Approved • DeKalb, IL, 2022).
In Minooka, planning materials and community outreach events surrounding a proposed Equinix data center show how environmental and economic narratives intersect. Village documents emphasize investment potential while residents raise questions about farmland conversion and long-term water use (Village of Minooka, 2025). Rural proposals such as the CyrusOne campus in Sangamon County further illustrate how large projected electrical loads and utility planning become central issues when projects enter agricultural landscapes, where industrial infrastructure represents a visible shift in land identity (Sangamon County, 2025).
Socially, these projects generate structured participation through hearings, open houses, and public comment periods, creating clearer records of negotiation between developers and residents. Economically, redevelopment proposals in Hoffman Estates and Mount Prospect are often framed as adaptive reuse of former corporate campuses, presenting data centers as successors to earlier economic eras rather than entirely new industrial activity (Compass Kicks off Development of Its First Campus in Illinois, 2024; ComEd and CloudHQ Break Ground on New Hyperscale Data Center in Mount Prospect, 2022). These cases highlight uncertainty, where benefits remain projected while impacts remain speculative.
Denied Development in Illinois: Community Limits and Upholding the Annoyance Threshold
The rejected Karis Critical proposal in Naperville provides an important counterpoint to both operational and proposed projects. Located on the former Lucent campus, the proposal underwent months of hearings and revisions before the city council ultimately denied the conditional use permit in January 2026. News coverage consistently identifies resident concerns related to diesel backup generators, air quality, noise, and proximity to homes as central factors influencing the decision (Pirc, 2026; Piekos, 2026).
Environmentally, the debate focused on routine operational impacts rather than catastrophic risk, demonstrating how cumulative everyday stressors can shape perception. Socially, the extended review process and high public turnout reflected sustained community engagement and skepticism toward mitigation assurances. Economically, supporters emphasized redevelopment and investment potential, while opponents questioned whether benefits justified long-term neighborhood impacts.
As a case study, Naperville illustrates that acceptance of AI infrastructure is not inevitable. When environmental concerns align with strong social mobilization and uncertain economic tradeoffs, communities may determine that a project exceeds their acceptable threshold. The denial therefore functions not as an anomaly but as evidence that local governance remains a decisive factor in shaping the trajectory of AI development.8
From Map to Decision: An Explainable County-Level Impact Platform
Platform Design and Analytical Methodology
Live platform: The People v. Hasty AI
GitHub repository: https://github.com/Laasya-73/SoReMo-S26-people-v-hasty-ai.git
Research Objective and Questions
The objective of this study is to develop and evaluate an explainable, county-level decision-support platform for AI data center expansion in Illinois. Rather than treating siting proposals as isolated events, the platform integrates infrastructure footprint, environmental stress, community vulnerability, and scenario-based burden projections into one analytical workflow.
The tool was built to address three research questions:
- Where are existing and proposed data centers concentrated in Illinois?
- How do these concentrations overlap with pre-existing county-level stress and vulnerability conditions?
- Under explicit growth assumptions, which counties show the largest projected increase in cumulative burden?
Workspace Implementation
The deployed platform is organized as five workspaces:
Home: quick orientation and how to use the platform
Figure 12.
Illinois Map: interactive layers and county/site inspection. Scroll down on the left-hand side of the screen to see all options for interacting with the map. Hover over counties and points for more details.
Figure 13.
County Intelligence Briefing: county-specific summaries for communication. Adjust via the “Brief Settings” area; click “Generate County Brief” once done. See the “Download Brief (.pdf)” button at the bottom of the generated report.