The Language of Smart Cities: Who Gets Counted? - By Umasankar Mukkamala

Written By: Umasankar Mukkamala

Category: Essay / Argumentative + Personal Reflection

Competition: Global Poetry and Creative Writing Competition 2026

In his thought-provoking essay, "The Language of Smart Cities: Who Gets Counted?", Umasankar Mukkamala delves into the intricate intersection of digital infrastructure and linguistic diversity within urban landscapes. Submitted for the Global Poetry and Creative Writing Competition 2026, this essay provides a reflective and argumentative perspective on how AI and smart city technologies impact equity and inclusion. Mukkamala's work invites readers to consider whose voices are truly heard in the rapidly evolving smart city environments.

About the Work

Umasankar Mukkamala's essay explores the nuanced challenges of linguistic justice in smart cities. Through compelling narrative and insightful analysis, the work highlights the often-overlooked biases in digital infrastructures, questioning the neutrality of technology that prioritizes dominant languages over diverse linguistic expressions. The essay opens a dialogue on the human cost of efficiency, emphasizing the need for systems that honor complexity and diversity rather than erase them.

The Language of Smart Cities: Who Gets Counted?

Original Submission by Umasankar Mukkamala

The Language of Smart Cities: Who Gets Counted?

Category: Essay / Argumentative + Personal Reflection

Theme: Digital infrastructure, linguistic diversity, AI, equity in smart cities

Language: English

-------“A city is only as smart as the voices it chooses to hear.”

The first time the kiosk didn’t understand her, she thought it was a glitch.

She stood in a bright, modern terminal, the kind you see in brochures for “cities of the future.” Touchscreens glowed along the wall. Digital signs flickered with arrival times in crisp, sans-serif fonts. A soft voice announced departures in two languages: one global, one official.

She stepped up to the kiosk, selected “Help,” and spoke in the language she uses with her mother, her neighbors, her childhood friends. The screen paused. A loading icon spun. Then: “Sorry, I didn’t catch that. Please try again.”

She tried again, slower. Same result.

She switched to the dominant language, the one she uses at work, the one that fills most of her screens. The kiosk responded instantly: cheerful, helpful, efficient.

It was a small moment. But it carried a quiet message: This system is not built for all of you.

She is not alone.

In cities around the world, digital infrastructure is being layered onto everyday life: transit apps, emergency alerts, health kiosks, digital IDs, automated service points. These systems promise efficiency, safety, convenience. They are sold as neutral, objective, modern. But they are not language-neutral. They are not culture-neutral. They carry assumptions about who belongs, whose speech counts, whose reality is legible to the machine.

And in that gap, entire communities become invisible.

The Illusion of Neutrality

Smart cities are often described in technical terms: sensors, data, algorithms, optimization. The language is clean, precise, reassuring. But behind every interface is a set of choices: which languages to support, which dialects to recognize, which forms of expression to treat as valid input.

Most systems default to a small set of dominant languages. Official forms, automated voices, chat interfaces—these are designed for the majority, or for the politically powerful. Informal speech, code-switching, regional dialects, and oral traditions are treated as noise.

This is not an accident. It is a design decision.

When a transit app only understands queries in one or two languages, it tells commuters: If you don’t speak like this, you are on your own. When an emergency alert is broadcast in a single language, it tells entire neighborhoods: Your safety is less important. When a health kiosk cannot process a description of symptoms in a local dialect, it tells patients: Your body, your words, your experience—none of these fit our model.

The result is a city that looks inclusive on a dashboard but fails many of its residents in practice.

Data That Doesn’t See You

At the heart of most smart-city systems is data: traffic flows, energy usage, service requests, mobility patterns. This data is used to train models, allocate resources, and make decisions. But data is only as inclusive as the people who generate it and the systems that collect it.

Many communities are underrepresented in these datasets. Informal settlements may not appear on official maps. Unregistered workers may not show up in employment statistics. Multilingual speakers who code-switch in everyday conversation may be misclassified or ignored by language models trained on standardized text.

When a person’s life does not fit the categories in the database, the system behaves as if they do not exist.

Consider a migrant worker who sends money home using an informal network because formal channels are inaccessible. Or a street vendor who relies on oral agreements and cash transactions. Or a grandmother who navigates the city using landmarks and community knowledge rather than digital maps.

To the algorithm, these lives are outliers. To the city, they are essential.

The problem is not only exclusion. It is also distortion. When data is collected without linguistic and cultural context, it can reinforce stereotypes or erase nuance. A neighborhood might be labeled “high risk” based on incomplete data. A dialect might be flagged as “low quality” speech by a model trained on prestige varieties. A community’s informal support networks might be invisible because they don’t generate digital traces.

In this way, smart-city systems can reproduce old inequalities under the guise of neutrality.

The Human Cost of “Efficiency”

Efficiency is a powerful word. It suggests rationality, progress, improvement. But efficiency for whom? At what cost?

When a city optimizes for speed and standardization, it often sacrifices the messy, human elements that make urban life livable: slowness, ambiguity, local knowledge, linguistic diversity.

Imagine a mother trying to report a water leak through a chatbot that only understands formal complaints in a dominant language. She knows how to describe the problem in her own words, in the language of her neighborhood. But the system requires a specific format, a specific vocabulary. She gives up. The leak remains.

Imagine an elderly man who avoids using a digital bus pass because the instructions are only available online, in a language he doesn’t read fluently. He continues to pay cash, even though it costs more. He feels outdated, burdensome, invisible.

Imagine a teenager who grows up speaking three languages fluidly: one at home, one at school, one on the street. None of the city’s interfaces reflect this reality. Every form asks her to choose one. Every automated voice assumes she is monolingual. She learns, quietly, that her full self does not belong here.

These are not edge cases. They are the daily experience of millions of urban residents.

The human cost is not just inconvenience. It is dignity. It is trust. It is the slow erosion of the belief that the city is for you.

When AI Becomes the Gatekeeper

Artificial intelligence is increasingly embedded in urban systems: predictive policing, automated customer service, resource allocation, content moderation. AI is presented as a tool for fairness: removing human bias, making decisions based on data rather than prejudice.

But AI models are trained on data that reflects historical inequalities. They learn from text, speech, and behavior patterns that are already skewed toward dominant groups. When these models are deployed in cities, they can amplify existing biases under the cover of objectivity.

Language models, for example, often perform poorly on non-standard dialects, code-switched speech, and low-resource languages. Voice recognition systems mishear names that don’t fit expected patterns. Chatbots fail to understand context that is culturally specific.

When AI becomes the gatekeeper to services, information, and rights, these failures are not just technical glitches. They are forms of exclusion.

A resident whose accent is consistently misrecognized by a voice assistant may stop trying to access services digitally. A community whose language is not supported by an emergency alert system may not receive critical information in time. A person whose identity documents don’t match the naming conventions in a database may be denied benefits they are entitled to.

The promise of AI in smart cities is personalization, responsiveness, intelligence. But without intentional design, it can deliver the opposite: standardization, erasure, silence.

Designing for Linguistic Justice

The alternative is not to reject technology. It is to redesign it.

Linguistic justice in smart cities means building systems that recognize and respect the full range of human expression. It means treating language not as a problem to be solved, but as a resource to be honored.

Here are a few principles that can guide this work:

1.Multilingual by Default

Interfaces should support multiple languages from the start, not as an afterthought. This includes not only official languages but also widely spoken regional and community languages. Where full translation is not feasible, systems should allow users to switch languages easily and access human support when needed.

2. Support for Code-Switching and Dialects

Many people do not speak in neat, standardized categories. They mix languages within sentences. They use local expressions, slang, and idioms. Language models and voice systems should be trained on diverse corpora that reflect this reality, not just on formal, written text.

3. Community Data Stewardship

Communities should have a say in how their data is collected, used, and represented. This includes participatory mapping projects, local data councils, and transparency about how algorithms make decisions. Data should not be extracted from communities without their input or benefit.

4. Human-in-the-Loop Systems

Automation should not replace human judgment entirely, especially in high-stakes contexts like health, safety, and rights. There should always be a pathway to speak to a real person, in your own language, when the system fails.

5. Design for Slowness and Ambiguity

Not everything needs to be optimized. Some interactions require time, context, and nuance. Systems should allow for incomplete information, for clarification, for the messiness of human communication.

These are not just technical choices. They are ethical ones.

A City That Hears You

What would a truly inclusive smart city feel like?

It would feel like walking into a public service office and being able to speak in the language you dream in. It would feel like receiving an emergency alert in a form you can understand immediately, without needing to ask someone else to translate. It would feel like using a transit app that recognizes your neighborhood’s name, even if it’s not on the official map.

It would feel like being counted.

This is not a utopian vision. It is a design challenge. It requires investment in local languages, in community partnerships, in models that are trained on diverse data. It requires policymakers to see linguistic diversity not as a barrier to efficiency, but as a core component of equity.

It also requires all of us—users, residents, citizens—to demand better. To ask, when we encounter a system that doesn’t understand us: Who was this built for? Who is missing? What would it take to make this work for everyone?

The Question Behind the Interface

Behind every interface is a question: Who belongs here?

When a kiosk doesn’t understand your language, it’s not just a technical failure. It’s a statement about whose voice matters. When an algorithm misclassifies your neighborhood, it’s not just a data error. It’s a decision about whose life counts.

Smart cities will be built regardless. The question is whether they will be built for everyone.

We can choose to design systems that erase difference in the name of efficiency. Or we can choose to design systems that honor complexity in the name of justice.

We can build cities that optimize for speed and standardization. Or we can build cities that make space for slowness, for ambiguity, for the full range of human expression.

The technology is not the problem. The choices are.

And those choices are ours to make.

Finally here it is what I feel… “Technology should count people, not erase them, and Inclusion is not a feature; it is the foundation.”

About the Competition

The Global Poetry and Creative Writing Competition 2026 by Hashtag Kalakar is a prestigious platform celebrating creativity and expression in various literary forms. The competition encourages participants from around the world to share their unique perspectives and insights through poetry, essays, and creative writing. It serves as a catalyst for meaningful conversations, bridging cultural and linguistic divides through the power of words.

We extend our heartfelt gratitude to Umasankar Mukkamala for his insightful contribution, "The Language of Smart Cities: Who Gets Counted?", to the Global Poetry and Creative Writing Competition 2026. His essay challenges us to rethink the role of technology in urban environments and advocates for a more inclusive approach to smart city design. Thank you, Umasankar, for prompting essential discussions on equity and linguistic diversity.

About Hashtag Kalakar

Hashtag Kalakar is a creative platform dedicated to providing artists, writers, singers and other creative talents with opportunities to showcase their work and reach wider audiences through competitions, features and creative initiatives.