By – Siddarth Poola

Abstract

Artificial intelligence is routinely framed as a neutral decision-making tool that merely reflects the data it is trained on. This framing collapses under scrutiny when AI systems are examined through the lens of gender expression and power. Drawing on research by UN Women, UNDP, Nature, Stanford, ORF, and investigative journalism. This article argues that AI functions not simply as a mirror of social bias but as a weaponised system that actively disciplines, degrades, and polices gender nonconformity. From language models that reward normative gender performance to facial recognition systems that erase trans bodies and generative tools that automate sexualised abuse, AI systems do not passively reproduce prejudice. They legitimise it, scale it, and present it as technical output rather than ideological choice. This article examines how gender bias is embedded across the AI lifecycle, how AI outputs reinforce harmful ideas about gender expression, and why mitigation strategies that remain trapped in binary frameworks are structurally incapable of addressing the harm inflicted on gender-diverse people.

Introduction: Neutrality Is the Most Effective Disguise

Artificial intelligence is frequently described as impartial, objective, and free from human prejudice. This claim is repeated by developers, policymakers, and institutions precisely because it is useful. If AI is neutral, then any harm it causes must be accidental, unfortunate, or the result of misuse rather than design. The problem is that this claim is demonstrably false.

However, research by the UNDP does show that AI systems do not merely reflect social inequalities but amplify them by encoding historical power relations into automated decision-making processes. UN Women renders a similar point, noting that AI systems frequently reinforce gender stereotypes and exclusions while presenting their outputs as data-driven truths.

Through this piece I would like to take that argument one step further; I  show that AI is increasingly used as a mechanism of social enforcement, particularly against people whose gender expression does not conform to binary norms. AI systems do not simply misunderstand gender diversity; they penalise,  rank, flag, misclassify, sexualise, and erase bodies and identities that fall outside what the model has learned to recognise as legitimate.

In doing so, AI outputs often confirm and normalise harmful ideas about gender, making discrimination appear inevitable, technical, and objective rather than political.

Building the Weapon: Gender Bias Across the AI Development Lifecycle

Gendered harm in AI does not originate at the moment an algorithm produces a biased output. It is built in systematically across the development pipeline. The Observer Research Foundation’s analysis of gender bias in the AI lifecycle demonstrates how bias enters at every stage, from problem formulation and dataset construction to model evaluation and deployment.

The initial framing of a system often assumes gender as a fixed, binary attribute. Data collection practices then reinforce this assumption by excluding or misclassifying trans, non-binary, and gender nonconforming people. When such data is used to train models, the resulting system learns that gender diversity is statistically rare, anomalous, or irrelevant. AI development illustrates how these early design decisions shape downstream outcomes, embedding normative assumptions about gender roles, competence, and appearance into algorithmic systems. Crucially, these assumptions are not neutral. They reflect dominant social hierarchies and treat deviation from them as noise rather than meaningful variation.

The result is that AI systems are structurally predisposed to reward conformity and punish difference. Gender nonconformity becomes a technical problem to be corrected rather than a social reality to be respected.

Language Models and the Policing of Gender Expression

Large language models play a growing role in shaping how people write, learn, search, and understand the world. They are also remarkably efficient at enforcing normative gender narratives. The UNDP’s analysis of AI-generated language highlights how models consistently associate men with leadership, ambition, and authority, while linking women to caregiving and emotional labour.

More insidiously, these models often struggle to represent gender-diverse identities without framing them as deviations that require explanation. Age and gender distortion across online spaces results in large language models systematically skewing representations of gender, reinforcing narrow ideals of legitimacy, attractiveness, and credibility.

When users ask neutral questions about careers, relationships, or authority, the answers frequently reproduce cis-normative assumptions. When users ask about trans or non-binary identities, the tone often shifts to explanation, justification, or moral debate. In both cases, the model reinforces the idea that binary gender is the default, and everything else is commentary.

This is not accidental. Research on AI and gender equality notes that language systems trained on biased corpora internalise the norms of the societies that produced the data. The output then feeds those norms back to users, creating a feedback loop in which harmful ideas appear repeatedly validated by the machine.

Computer Vision, Surveillance, and the Erasure of Gender-Diverse Bodies

If language models police gender discursively, computer vision systems do so materially. Facial recognition technologies have repeatedly been shown to perform poorly on women, trans people, and gender nonconforming individuals. The Swaddle’s reporting documents depict how these systems misgender, misclassify, or entirely fail to recognise trans faces, with consequences ranging from denial of services to heightened surveillance.

These failures might arise due to technical limitations but they also reflect design choices that prioritise recognisability over inclusivity. Systems trained primarily on cisgender faces learn to treat gender diversity as error. When deployed in contexts such as policing, border control, or identity verification, this error becomes institutionalised exclusion. Performance metrics rarely account for whose bodies are misrecognised, only how often recognition succeeds on average.

The consequence is a technological regime in which visibility is conditional. To be legible to the machine, one must conform to its expectations of gender. Nonconformity becomes a risk factor.

Generative AI and the Automation of Gendered Degradation

Perhaps the clearest example of AI as a weapon against gender expression lies in generative abuse. There’s been a lot of outrage about AI tools being used to create non-consensual sexual images, including the digital undressing of women and children, often targeting those already vulnerable to gender-based violence.

These tools go beyond just reflecting misogyny or transphobia; it helps automate and justify it. The ease of access helps make harassment normal, anonymous, and technically mediated and provides people who use AI in such an unethical way with an easy way to justify their actions by saying something along the lines of ‘You shouldn’t have put a picture of yourself online if you didn’t want this to happen to you’ (The excuse has been toned down for obvious reasons).  Gender expression that deviates from dominant norms becomes a site of punishment, humiliation, and control.

UN Women’s 2025 interview on how AI reinforces gender bias explicitly warns that without strong governance, AI will intensify existing patterns of violence and exclusion. The International Women’s Day campaign on gender and AI echoes this concern, emphasising that ethical principles alone are insufficient when systems are structurally aligned with harm.

In these cases, AI outputs do not merely confirm harmful ideas about gender. They actively participate in enforcing them.

Conclusion: When the Machine Agrees With the Mob

Across the specific studies, reports, and investigations discussed here, a consistent pattern emerges. AI systems do not passively reflect social bias. They legitimize it by translating prejudice into technical output. When an algorithm misgenders a person, ranks them as less credible, or facilitates their sexualised abuse, it does so with the authority of computation.

This authority matters. As UNDP and UN Women both stress, AI systems increasingly shape access to work, safety, recognition, and dignity. When these systems are built on binary assumptions and deployed without accountability, they become tools of social discipline.

Efforts to address gender bias in AI must therefore move beyond inclusion rhetoric and technical fixes. They must confront the ways in which AI is used to reward conformity, punish difference, and make discrimination look inevitable.

The algorithm does not learn gender politics. It optimises them, deploys them, and insists that the outcome is neutral.

About the Author:
Siddarth Poola is an undergraduate student doing law in Jindal Global Law School, with a deep interest in Water Sports and Sports Law.

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