Introduction:
The hash table is one of the most powerful data structures in computer science, enabling nearly constant-time access to data through key-value pairs. Its design is built around a deceptively simple idea: map each key to a specific location in memory using a hash function, and retrieve the data associated with that key directly. This eliminates the need for linear or binary searches, allowing for rapid data access, insertion, and deletion.
Today, hash tables power everything from language compilers to social media platforms. But like many abstractions in computer science, the hash table didn’t emerge in a vacuum. Its origins are deeply rooted in systems of categorization that long predate digital computing. These systems, designed to classify, sort, and retrieve human data at scale, not only laid the groundwork for modern hash structures, but also raise deeper questions about the politics of categorization and control.
Precursors of the Modern Hash Table:
The origin of the hash table is deeply intertwined with humanity’s long-standing need to sort, categorize and retrieve information efficiently. Long before digital memory or modern programming languages existed, data was encoded physically and processed mechanically—yet the underlying principles that drive hash table design were already emerging.
One of the most significant figures in this pre-digital era was Herman Hollerith, who invented an electromechanical punch card system to assist in processing the 1890 U.S. Census. As discussed in the earlier history section on sorting algorithms, Hollerith’s system effectively implemented a physical form of Radix Sort, using punched holes on cardboard cards to represent demographic information, which could then be sorted and counted using tabulating machines. This innovation marked a paradigm shift: data was no longer static or purely textual—it could now be encoded, moved, and analyzed with mechanical logic (Garfinkel, 2020).
Hollerith’s punch cards directly inspired the IBM Computer Card, introduced in 1928, which standardized the format of punched data for use in business, government, and science. These cards could store up to 80 characters and were often sorted into trays or drawers according to specific encoded values. The physical act of placing a card into a precise location for later retrieval parallels the logic of modern hashing: mapping a key (or identifier) to a fixed address. While Hollerith’s system operated through hardware and human labor, the logic of quick retrieval and deterministic data placement would eventually be abstracted into the digital realm through the invention of hash functions and hash tables (IBM).
In short, the structure and function of the hash table were foreshadowed by these early systems of information encoding and access. They laid not only the conceptual groundwork but also revealed how deeply data structures are shaped by the societal needs—and limitations—of their time.
Categorization as Control: Punch Cards in Wartime Surveillance
Printed across millions of IBM punch cards was a now-iconic warning: “Do not fold, spindle or mutilate.” On the surface, it was a practical instruction—damage the card, and the machine couldn’t read it. But over time, the phrase took on a chilling resonance. It captured the essence of a growing bureaucratic mindset in which human beings were increasingly reduced to data, categorized by machines, and processed with the same cold logic as any other input. It’s a symbol of how data systems—originally designed for efficiency—can become tools of control (Julyk, 2008) .
While early punch card systems revolutionized data processing, they also exposed a darker potential: how structures built to optimize information flow could be co-opted to surveil, classify, and ultimately harm. Nowhere is this more evident than in their use during World War II, when technologies meant to streamline administration were weaponized to track, segregate, and displace entire populations.
IBM Punch Cards in Nazi Germany
During World War II, IBM’s German subsidiary, Dehomag, supplied the Nazi regime with punch card machines and tabulating equipment that played a central role in identifying, classifying, and tracking millions of people. As documented in Edwin Black’s IBM and the Holocaust, these systems were used to conduct detailed population censuses, record racial and religious identities, and manage the logistics of deportations and forced labor of Jewish communities in Nazi Germany.
Punch cards transformed human lives into coded data—sorted, filtered, and retrieved with mechanical precision. Each person became a pattern of holes in a card, slotted into a system that made bureaucratic genocide not only possible, but disturbingly efficient. The logic echoed that of a hash table: an input—such as a name, birth record, or racial category—was mapped to a fixed output. But here, the output was not a memory address or a data value—it was surveillance, displacement, or death. By feeding these cards into Hollerith tabulators, Nazi officials could rapidly identify and classify individuals, enabling the systematic targeting of Jews and other persecuted groups across Germany.
Although IBM would later distance itself from these activities, Black argues that the company’s U.S. headquarters knowingly supported Dehomag throughout the war. IBM retained proprietary control over its leased machines, which required regular maintenance and updates provided through its New York office—sustaining a direct connection to Nazi operations throughout the Holocaust.
If you want to learn more about IBM’s role in the Holocaust, here’s a great podcast by TC Talk discussing Edwin Black’s book and its modern implications.
Japanese-American Internment (WWII)
In the wake of Pearl Harbor, the U.S. government forcibly removed and incarcerated over 120,000 Japanese-Americans—most of them U.S. citizens. Coordinating this mass internment required complex logistical infrastructure, much of which was enabled by IBM punch card systems. Government agencies used these cards to track ancestry, location, occupation, and perceived loyalty. The data was then mechanically sorted to determine who would be relocated, where they would go, and under what classification (Persson, 2017) .
As historian Margo J. Anderson notes, “the provision of technical expertise and small-area tabulations to the army enabled the roundup, evacuation, and incarceration of the Japanese-ancestry population—more than 110,000 men, women, and children—from the West Coast of the United States.” The same systems once used for social planning and census work were now repurposed to facilitate mass displacement.
While not a campaign of extermination, the Japanese internment program showed how administrative technologies quietly enforced racial segregation and collective punishment. Individuals were reduced to machine-readable records, and entire families were slotted into a system that prioritized logistical efficiency over civil rights. The punch card became the interface between bureaucratic logic and racialized state power.
This history complicates the story we often tell about technological progress. The same innovations that made data systems efficient and scalable were also repurposed to enable mass incarceration and genocide—often in the service of state control or corporate profit. It is a stark reminder that data structures—even ones as seemingly neutral as the hash table—are never just technical tools. They reflect the values of the systems in which they are designed and deployed—and they help shape the outcomes of those systems in return.
The Legacy of the Hash Table in Modern Systems
The histories of Nazi Germany and Japanese-American internment show how systems of classification—initially viewed as neutral or efficient—can become tools of harm. These systems didn’t disappear; they were abstracted. The punch card became the hash function. The drawer became the data structure.
The data structure we now call the hash table was first proposed by Hans Peter Luhn in 1953, as a method for organizing information through key-to-address transformations. Later popularized by Donald Knuth, the structure became foundational to computer science—powering dictionaries, databases, caches, and countless modern systems. Its appeal lies in speed: transform an input into a fixed location for instant retrieval.
But with that efficiency comes risk. In contemporary systems—search engines, predictive policing, credit scoring—hash-based classification helps determine what we see, how we’re profiled, and what decisions are made about us. The capacity to sort at scale, once used to streamline census work, now operates invisibly in algorithmic infrastructures.
This isn’t to say hash tables are inherently harmful. But their simplicity can flatten nuance, turning categories into consequences. The legacy of the hash table reflects the priorities of the systems they serve and how that in itself can be inherently harmful. To use them responsibly is to remember that data structures are never just technical. They carry forward histories and shape futures.
Technology and Political Realities
In the 20th century, punch card systems helped governments surveil, classify, and displace entire populations. Today, that logic persists—transformed by algorithms, scaled by cloud infrastructure, and legitimized through the rhetoric of “innovation.” The same structural logic behind the hash table—reducing identity to a key and mapping it to a fixed outcome—now operates inside powerful digital systems that influence who is watched, who is targeted, and who is excluded.
Across borders, governments are increasingly turning to algorithmic tools to manage populations. Immigration and policing agencies in the U.S. and elsewhere now use massive identity databases, often maintained by or contracted to private tech firms. Hash-based indexing allows these systems to flag individuals based on biometric signatures, location histories, or perceived affiliations. These flags, though automated, carry real consequences: deportation, denial of entry, targeted surveillance.
At the same time, a deeper political shift is underway: the merging of big tech and state power. Companies like Palantir, Amazon, and Google now build and maintain critical infrastructure for law enforcement, immigration, military intelligence, and predictive analytics. These platforms don’t just serve government functions—they reshape them, embedding the values of the private sector into the workings of public policy. Speed is prioritized over due process. Optimization over accountability. Scale over scrutiny.
This integration raises a dangerous possibility: that the technical systems being deployed in the name of “efficiency” or “national security” will mirror the incentives of the corporations that design them—rather than the democratic institutions they are meant to serve. And just like IBM during World War II, today’s firms may claim neutrality while quietly enabling forms of violence through infrastructure.
This moment doesn’t require history to repeat itself in the same form—it only requires that we ignore how infrastructure shapes power. As public institutions increasingly rely on private platforms, and as categorization is outsourced to automated systems, we risk rebuilding the same machinery of exclusion with new tools. The hash table, once a technical innovation, now reminds us that every system of classification carries political weight—and that the values encoded into our technologies will always reflect the priorities of those who control them.
Discussion Questions
-
When does abstraction become dangerous? How might simplifying human identity into keys and categories—whether via punch cards or hash functions—erase complexity in ethically troubling ways?
-
How does the history of the hash table—emerging from systems like Hollerith’s punch cards—challenge the idea that data structures are apolitical or purely technical? What ethical responsibilities come with designing or deploying such systems today?
-
In what ways do modern technologies, like hash-based recommendation systems or surveillance tools, reflect the values of the corporations that build them? How should we understand the role of tech companies—like IBM then, or Amazon, Meta, and Palantir now—in shaping the moral direction of our digital infrastructure?