Kafka Became Reality: The Modern Absurdist Predicament Under AI Algorithmic Decision-Making

A Tweet That Struck a Collective Nerve

Recently, a short tweet — "Kafka became reality" — sparked widespread discussion on social media. Three words that precisely captured the bone-deep helplessness people feel when confronting AI-driven decisions and techno-bureaucratic systems.

How the Kafkaesque Predicament Maps onto the AI Era

From Literary Metaphor to Technological Reality

Franz Kafka (1883–1924) was a Prague-born German-language writer whose work was deeply influenced by existentialism and expressionism. The Trial (1925) and The Castle (1926) were both unfinished works published posthumously, yet they became among the most influential literary legacies of the 20th century. The adjective Kafkaesque has been enshrined in the English dictionary, specifically describing that suffocating sense of bureaucratic absurdity — opaque rules, unreachable authority, and the futility of individual effort.

In The Trial, Josef K. is arrested and tried without ever learning his crime; in The Castle, the land surveyor K. can never reach the true center of power. Marx's theory of "alienation" resonates strongly with Kafka's literary imagination: when systems created by humans turn around to dominate them, individuals become estranged from the products of their own labor. This philosophical framework has gained an entirely new technological dimension in the AI era.

Today, these literary scenarios are replaying in everyday life through technology:

• A loan application is rejected by an AI algorithm, but no one can provide a specific reason
• A social media account is banned by an automated system, and the only appeals channel is a bot
• A resume is filtered out by an ATS (Applicant Tracking System), and the job seeker never learns what went wrong

It's worth noting that ATS penetration in modern recruiting far exceeds most people's imagination. Over 98% of Fortune 500 companies rely on such systems to screen resumes, and vast numbers of candidates are eliminated before a human recruiter ever sees their application. Research shows that even highly qualified candidates may be directly discarded if their resume formatting doesn't comply with ATS parsing rules — non-standard characters, unusual fonts, or complex layouts. Job seekers never learn the real reason for rejection, receive no feedback whatsoever, and can only adjust blindly in the dark, never able to touch the actual criteria by which they're judged.

The Kafkaesque absurd is no longer just a literary metaphor.

AI Black Boxes: Incomprehensible Digital Authority

The "black box" nature of modern AI systems bears a striking resemblance to the incomprehensible bureaucratic institutions in Kafka's writing. This opacity is not accidental — it's an intrinsic property of deep learning architectures: a large neural network model may contain billions of parameters, automatically adjusted through massive data training to form extremely complex nonlinear mappings. Unlike traditional rule engines, no one pre-writes "if A then B" logic — decisions emerge from data rather than being designed.

ProPublica's 2016 investigation of the COMPAS algorithm (a judicial assistance system for predicting recidivism rates) revealed that the system exhibited systematic bias against Black defendants, and this bias was buried deep within model weights, making it difficult to directly identify and correct. This is precisely where technological black boxes are most dangerous: discrimination and errors can be institutionalized in the name of "objective algorithms."

Specific manifestations include:

• Inexplicability: Even developers themselves cannot fully articulate how deep learning models make decisions
• Inaccessibility: Users always face automated interfaces, while the actual decision-making layer remains unreachable
• Non-appealability: When algorithms make incorrect judgments, the path to correction is often long and futile

Algorithmic Bureaucracy: The New Face of the Technological Era

From Human Bureaucracy to Algorithmic Bureaucracy

Traditional bureaucratic systems at least had humans running them — you could find a specific clerk and argue your case. But in the algorithm-driven era, the very concept of "the person in charge" is dissolving.

Customer service bots transfer you around in circles, with each layer claiming it lacks the authority to handle your problem. Isn't this precisely a digitized remake of The Castle?

Data as Destiny: How Digital Dossiers Determine Your Life

In Kafka's time, paper files and official documents determined a person's fate. In our time, it's data and algorithms.

Credit scores, social media profiles, consumer behavior data — these digitized "dossiers" are quietly delineating the boundaries of our lives in ways we cannot see. How much you can borrow, what apartment you can rent, even what information you get to see — all of these may be determined by data models you've never laid eyes on.

This mechanism of shaping digital destiny is, in some ways, even more covert than the paper files in Kafka's writing: at least paper files were tangible, while the operations of data models are almost entirely invisible to the individuals affected.

Fighting Algorithmic Alienation: What Can We Do

Explainable AI: Opening the Black Box

Research in Explainable AI (XAI) is attempting to pry open the black box. Currently, the field has two main technical approaches: one is building inherently interpretable "white box" models, such as decision trees and linear regression; the other is providing post-hoc explanation tools for complex black-box models, such as LIME (Local Interpretable Model-agnostic Explanations) and SHAP (SHapley Additive exPlanations). SHAP is based on Shapley values from game theory and can quantify each feature's contribution to the final prediction — it's one of the most widely used interpretability tools in industry today.

However, a fundamental tension exists within XAI: the more powerful the model, the harder it typically is to explain; and forcing interpretability may come at the cost of accuracy. Moreover, "explanations" themselves can be misleading — the feature importance rankings shown to users may not truly reflect the model's internal decision logic, sometimes serving merely as a post-hoc rationalization narrative.

Nevertheless, the EU's AI Act explicitly requires high-risk AI systems to be explainable, granting users the right to know "why this result." This represents humanity's technical pathway for combating "Kafkaization" — making algorithmic decisions traceable rather than incomprehensible.

Institutional Safeguards: Preventing Systems from Spinning Completely Out of Control

Beyond technical measures, institutional design is equally critical. The EU leads globally in this domain: Article 22 of the General Data Protection Regulation (GDPR), which took effect in 2018, explicitly grants individuals "the right not to be subject to purely automated decision-making," requiring human intervention options in automated decisions with significant impact. The AI Act (EU AI Act), formally passed in 2024, establishes the world's first systematic AI regulatory framework, classifying scenarios such as credit scoring, recruitment screening, and judicial assistance as "high-risk" applications.