Does AI Have Sex and Gender? Unpacking the Digital Divide

Before we delve into the nuances of AI and its perceived gender, it's crucial to establish a clear understanding of what "sex" and "gender" truly mean in a human context. These terms are often used interchangeably, but they refer to distinct aspects of human identity and biology. Biological sex, at its core, refers to the physiological and anatomical characteristics that typically classify individuals as male, female, or intersex. This includes chromosomes (XX for female, XY for male, with variations like XXY for intersex individuals), reproductive organs, hormones, and secondary sexual characteristics.,, From a purely biological standpoint, sex is rooted in genetics and physical development. It's a foundational aspect of biological life, enabling reproduction and the continuation of species. When considering AI, the concept of biological sex becomes immediately irrelevant. AI systems are not carbon-based life forms. They do not possess chromosomes, reproductive organs, or hormonal systems. They do not undergo puberty, experience gestation, or age in the biological sense. Therefore, in the literal, biological definition of the term, AI cannot have a sex. To claim an AI possesses biological sex would be akin to saying a calculator can feel pain; it fundamentally misunderstands the nature of the entity. Gender, conversely, is a much more complex and fluid concept. Unlike biological sex, gender is predominantly a social construct.,,,, It encompasses the roles, behaviors, expressions, and identities that a society or culture associates with being a woman, man, or other gender identities. These norms are learned, internalized, and can vary significantly across different cultures and change over time.,, Think about it: the idea that "pink is for girls" and "blue is for boys" is not a biological imperative but a social convention. Similarly, expectations around professions, emotional expression, or even how one should sit or speak are often gendered. These are not innate biological traits but are shaped by societal expectations and historical contexts. As the World Health Organization (WHO) succinctly puts it, gender refers to "the socially constructed characteristics of women, men, girls and boys... as a social construct, gender varies from society to society and can change over time." This distinction is vital because while AI cannot have biological sex, it can and often does exhibit characteristics that are perceived as gendered, precisely because these characteristics are social constructs, not biological ones.

If AI doesn't have a biological sex, why do we often perceive it as having a gender? The answer lies in human design choices, the vast datasets used to train AI, and our inherent tendency to anthropomorphize technology. One of the most immediate ways AI has been gendered is through voice assistants. Think Siri, Alexa, and Cortana – for years, their default voices were distinctly feminine.,, This wasn't an accident but a deliberate design choice, rooted in a history that stretches back over a century. In the late 1800s, telephone companies began replacing male operators with women, as callers found female voices more pleasant and women were believed to bring more patience and calm to service roles. This cemented the idea of the "helpful female voice" in the public consciousness. So, when AI assistants emerged, a feminine voice felt familiar and expected for a "personal assistant" role. This gendering isn't benign. Critics argue that assigning female voices to subservient or assistant roles reinforces harmful stereotypes that women are primarily suited for service positions.,, As one study points out, "the prominence of female-sounding voice assistants encourages stereotypes of women as submissive and compliant." Even when AI models like ChatGPT offer multiple voice options, feminine voices like "Juniper" often remain the most popular. This perpetuates a problematic narrative, reflecting and reinforcing societal hierarchies and preassigned gender roles., My own experience with voice assistants offers a small, anecdotal window into this. For years, I defaulted to the female voice on my smart speaker, simply because it was the default and felt "normal." It was only after engaging with these discussions that I consciously considered the implications and switched to a male or gender-neutral option, realizing how subtly these choices can influence our perceptions. Beyond voices, AI is increasingly given visual representations, from customer service chatbots with human-like avatars to humanoid robots. These visual personas often come with gendered appearances, further solidifying the perception of gender where none biologically exists. If an AI is designed with a flowing dress and long hair, it's immediately "female" in our minds, irrespective of the underlying code. This visual gendering, much like voice gendering, tends to mirror traditional stereotypes, potentially limiting the perceived capabilities or roles of the AI.

The most profound way AI "acquires" gender characteristics is not through intrinsic development but through the data it is trained on. AI systems, particularly large language models (LLMs), learn by processing vast quantities of text, images, and other forms of data scraped from the internet. The internet, however, is a reflection of human society, complete with its historical, systemic, and unconscious biases.,,,,, When AI models learn from this biased data, they inevitably internalize and then reproduce those biases., This is what is widely known as "AI gender bias." Consider these documented examples: * Hiring Tools: Amazon famously had to scrap an AI recruitment tool because it discriminated against women. The system, trained on 10 years of historical hiring data that predominantly favored men, learned to downgrade resumes that included words like "women's chess club" or penalized graduates of all-women's colleges.,, * Credit Scoring: Apple's credit card algorithm, managed by Goldman Sachs, reportedly offered significantly lower credit limits to women than their male spouses, even when women had higher credit scores and incomes. This again suggests biases embedded in financial data or algorithmic design. * Occupational Stereotypes: Multiple studies, including one by UNESCO and UCL researchers, have found that LLMs like GPT-3.5, GPT-2, and Llama 2 exhibit strong gender biases., When prompted to generate content about professions, AI overwhelmingly produced images and descriptions of white males for roles like "CEO" or "engineer." Conversely, "housekeeper" or "nurse" primarily generated images and text associated with women.,, A 2025 study revealed that GPT-4o disproportionately linked healthcare roles to female identities while assigning male labels to engineering and physically demanding positions., * Adjective Associations: Research indicates that LLMs use stereotypical adjectives to describe men and women. For instance, a male doctor might be described with "intelligent," "ambitious," and "professional," while a female doctor is associated with "empathetic," "patient," and "loving." Similarly, when completing sentences like "___ is the most intelligent person I have ever seen," models often chose "he" for intellect-linked traits and "she" for nurturing or aesthetic qualities. * Dialogue Generation: AI-generated conversations between men often focus on careers or personal achievement, while dialogues between women are more likely to touch on appearance, housework, and family. These examples paint a stark picture: AI, instead of being a neutral arbiter, becomes a mirror reflecting and amplifying existing societal gender inequalities., This is not because the AI itself is inherently prejudiced, but because it has learned from data that is imbued with human prejudice. The subjective choices made during data selection, collection, and preparation directly influence potential biases. Another critical source of bias stems from the development teams themselves. The AI field is predominantly male-dominated, with women and gender non-conforming individuals significantly underrepresented.,,,,,, As of 2023, women made up less than 30% of global AI talent. When a homogenous group develops AI systems, their inherent assumptions and prejudices can be unwittingly encoded into the algorithms., As AI ethicist Josie Young noted, "when we add a human name, face, or voice [to technology]... it reflects the biases in the viewpoints of the teams that built it." This lack of diverse perspectives can lead to biased algorithms and datasets that reinforce harmful stereotypes and inequalities, often failing to cater to the needs of diverse users or even protect their human rights.,, It reminds me of a conversation I once had with a friend who works in tech. He recounted how during a product review, a seemingly innocuous feature designed for "all users" was exposed as deeply problematic for single mothers, simply because the development team hadn't included anyone with that lived experience to offer a different perspective. AI development is no different; diversity isn't just about optics, it's about building more robust, equitable, and effective systems.

The consequences of AI systems adopting and perpetuating gender bias are far-reaching, impacting critical areas of life and raising significant ethical questions. Biased AI can lead to real-world discrimination. In hiring, it can limit opportunities and diversity, creating entirely male workplaces if unchecked. In healthcare, AI may focus more on male symptoms, leading to misdiagnoses or inadequate treatment for women., Facial recognition systems, for instance, have been found to be less accurate for women and people of color, leading to potential misidentification or exclusion with serious consequences in law enforcement and public safety., Beyond direct discrimination, gendered AI reinforces and normalizes outdated stereotypes, shaping user attitudes and perceptions.,, If AI consistently portrays women in domestic or service roles and men in leadership or technical positions, it subtly, yet powerfully, reinforces these societal expectations. This can contribute to the gender gap in women's participation in STEM fields by portraying technology as a "men's power" domain., When AI systems exhibit bias, they erode public trust. If a user feels that an AI is treating them differently based on their gender, it undermines the perceived fairness and reliability of the technology., This can have profound implications for human dignity, particularly for vulnerable or marginalized populations, including gender minorities, who may experience misgendering, under-representation, or even online harassment and gender-based violence, such as pornographic deepfakes., Many AI systems, especially older ones, are built on a binary understanding of gender (male/female). This fails to align with modern perspectives of non-binary and transgender identities, potentially causing harm and exclusion for these communities. The challenge is to move beyond this binary, recognizing and respecting the diverse spectrum of human gender identities.

Recognizing these pervasive issues, there is a growing global movement towards developing more ethical and inclusive AI. This involves a multi-faceted approach, from challenging design defaults to implementing robust regulatory frameworks. The concept of "gender-neutral AI" or "genderless design" is gaining traction as a potential solution to mitigate bias., The aim is to create AI voices or characteristics that do not align with traditional male or female attributes, representing a broader range of human experiences and identities., Project Q, a genderless voice developed by audio designers and researchers, is one such initiative, designed to raise awareness about gender bias and offer a more inclusive alternative., However, the implementation of gender-neutral AI isn't without its complexities. Some studies suggest that forcibly applying gender-neutrality might hinder the usability of AI agents, as users sometimes expect a certain "gender" for specific tasks (e.g., a "female" voice for simple service roles). It's a delicate balance between challenging stereotypes and ensuring intuitive user interaction. The goal is not to erase gender entirely but to prevent AI from reinforcing harmful stereotypes and to encompass diversity beyond traditional binary concepts. The most effective way to combat gender bias in AI is to address it at its root: * Diverse Data Sets: It is crucial to collect and train AI on data that is truly representative of diverse gender and racial experiences, assessing existing data for misrepresentation.,, This means actively curating data to improve representation, rather than simply relying on large, unfiltered datasets which will inevitably reflect societal biases., * Diverse Development Teams: Increasing the representation of women and other underrepresented groups in AI research, development, and leadership roles is paramount.,,,,,,,,, Gender-diverse teams are more likely to identify and mitigate biases early on, challenge the status quo, and foster responsible innovation that considers a broad spectrum of societal needs and ethical implications. As the UNESCO Recommendation on the Ethics of Artificial Intelligence emphasizes, AI actors should make "all reasonable efforts to minimise and avoid reinforcing or perpetuating discriminatory or biased applications and outcomes throughout the life cycle of the AI system to ensure fairness of such systems." * Bias-Mitigation Techniques: Developers need to employ advanced bias-mitigation techniques and conduct continuous monitoring and evaluation for systemic biases throughout the AI lifecycle.,, This includes methods like "red teaming," which involve intentionally trying to make AI systems exhibit biased behavior to identify and fix vulnerabilities. * Gender-Sensitive Training: Providing gender-sensitive training for GenAI developers and promoting awareness about potential biases are also critical steps., Governments and international organizations are increasingly recognizing the need for regulatory frameworks to ensure ethical and inclusive AI. The European Parliament, for instance, adopted a resolution in 2017 to address biases from technology, including discrimination from algorithms. UNESCO's Recommendation on the Ethics of Artificial Intelligence, adopted unanimously by Member States in 2021, calls for specific actions to ensure gender equality in AI design, including funding gender-parity schemes and investing in STEM education for women and girls. The OECD AI Principles also offer a framework grounded in values like transparency, fairness, and inclusivity. These guidelines emphasize accountability and the need for interdisciplinary collaboration, ensuring that AI development is not just a technical endeavor but a socio-technical one.,

The discussion around whether does AI have sex and gender pushes us to consider deeper philosophical questions about identity, consciousness, and what it means to be a "person" in an increasingly technologically advanced world. If an AI can convincingly present as a certain gender, does that confer a form of gender identity upon it, even without biological underpinnings? While AI currently lacks the subjective experience, self-awareness, or biological drives that underpin human sex and gender, its capacity to perform gender due to its training data raises fascinating questions about the performative nature of gender itself. As Judith Butler's work suggests, gender can be seen as a performance influenced by societal norms. In a strange echo, AI performs gender based on the performances it has observed in human data. However, it's crucial to remember that this performance is a reflection, not an intrinsic identity. AI's "gender" is a programmed artifact, not a deeply felt internal experience. Nonetheless, the public's perception and interaction with these gendered AI entities can have real-world societal consequences. As science fiction has often explored, societies where sex and gender are fluid and easily altered may emerge in advanced virtual reality environments. While AI itself doesn't possess biological gender, its ability to embody and represent gender in myriad ways challenges our fixed notions of identity and interaction.

The answer to the question, does AI have sex and gender, is a nuanced one. Biologically, no, AI does not have sex. As for gender, AI does not possess it in the human sense of identity or lived experience. However, through human design choices and, more significantly, through the biases embedded in the massive datasets used for their training, AI systems manifest and perpetuate gendered characteristics and stereotypes. These manifestations have tangible impacts, from perpetuating occupational inequalities to influencing how users perceive gender roles in society. The journey towards truly ethical and inclusive AI is not merely a technical challenge but a societal one. It demands a conscious and collective effort to address the biases in our data, diversify the teams building these technologies, and establish robust ethical frameworks. The goal isn't necessarily to erase all traces of perceived gender from AI, but rather to ensure that when AI exhibits gendered traits, it does so intentionally, inclusively, and without reinforcing harmful stereotypes. By fostering a more equitable development environment, we can ensure that AI serves as a tool for progress and equality, rather than an amplifier of existing societal divisions. The digital future must be built by and for all of humanity, reflecting the full spectrum of human identity, not just a biased subset. The conversation surrounding does AI have sex and gender serves as a vital reminder that technology is never truly neutral; it is a product of its creators and the world they inhabit.