How the feminization of AI enforces gender stereotypes


Written by Adrian Johansen, Digital Transformation, Writer

If there’s one element that seems to follow artificial intelligence (AI) wherever it goes, it’s femininity. The feminization of AI can be seen in pop culture, science fiction tropes, voice assistants, chatbots, and robots — more often than we may realize. 

Take a step back and ask yourself, “How often do I see masculine or genderless representation in AI versus feminine expression?”

As beneficial as feminization can be, it still raises some serious concerns for programmers, AI professionals, and people. This article will offer tips on how we can address the feminization of AI. But first, let’s look at where this issue stems from. 


Where the feminization of AI stems from 

First, AI is a product of the people that program it. These systems are dependent on the data that’s fed to them. 

Biases in programming can be purposeful or accidental due to unconscious, preconceived notions. For example, say a programmer has a gender bias centered around women being in more service-oriented roles rather than in physical, traditionally male positions, like a farmer, landscaper, or plumber. These “masculine” jobs are less likely to be replaced by AI

More traditionally feminine roles, like secretaries, customer reps, or cleaners, are more likely to be filled by AI.  In that case, the programmer will be more inclined to attribute feminine qualities to a service-oriented role for an AI tool, like a chatbot or voice assistant. Consciously or unconsciously, they don’t picture men in these positions.

Trying to get people not to be afraid of AI is hard enough. Femininity also helps in this respect because women are unfortunately considered more “human” than men. They’re more likely to be viewed as comforting, caring, and warm. Those characteristics are crucial in curbing fear in people. Although this is helpful for AI adoption, it’s furthering the feminization of AI.

The impact of gendering AI technologies can be dangerous because much of what’s “feminine” in these tools is based on harmful gender stereotypes. Therefore, we must address the feminization of AI to uproot its destructive aspects and reinforce the ones that enhance AI.


How to address the feminization of AI 

Now that we’re a bit more familiar with where the feminization of AI stems from, let’s look at how we can address it so that it can be beneficial rather than harmful. 

Think more deeply about how we view gender 

Addressing the feminization of AI starts with a deeper understanding of how we view gender and the roles associated with gender. Do some research and ask yourself: 


  • What are the different gender identities? 
  • What are the conversations surrounding them?  
  • How are we supporting those that don’t assign a gender to themselves?
  • What conscious or unconscious biases may we have toward women? 
  • How does this show up in our daily lives?


As you examine gender and gender roles, learning more about gender bias is crucial. Go beyond the basics. Research just how gender and other biases affect various industries that use AI, like healthcare, marketing, and recruiting. 

We must dive deeply into these core facets of gender to figure out how we can create AI tools that reinforce what’s authentic about gender rather than the stereotypes and untrue narratives perpetuated by society. 


Eliminate harmful stereotypes about men

We touched on how people view women as more human, emotional, nurturing, and capable of serving than men. However, isn’t always the truth. There are millions of men out there who love hard, serve, care, and are vulnerable and emotional. They can be just as, if not more, nurturing than women. Most importantly, they’re human too. 

We must work to eliminate the harmful stereotypes about men so that masculinity won’t be associated with negativity as often as it is. As a result, there can be a better balance of masculinity and femininity in AI. Companies developing AI won’t feel as pressured to feminize AI with a female voice or name in order to make it palatable and unthreatening to the masses.


Create more male-oriented AI

At some point, designers, programmers, and leaders in AI will have to go against the grain and create more masculine-presenting AI tools. The goal isn’t necessarily to eliminate femininity in AI — it’s to make these tools more balanced. 

Giving male-presenting individuals more representation in AI can help do this. As we eliminate the harmful stereotypes about men, it’s essential that the qualities and behaviors that humanize men and make them more approachable can also be programmed into AI tools. 


Hire more women in AI-based roles 

As mentioned above, there aren’t a lot of women in information technology (IT), AI, and machine learning positions. So, much of what’s programmed into AI tools as “feminine” comes from a man’s perspective of what that entails. 

Hiring more women in AI-based roles can help remove harmful gender stereotypes in AI and create an accurate depiction of the many sides of femininity. Women can also assist in eliminating harmful stereotypes about men. They can do this by sharing their experiences with men and non-binary individuals and coding those traits and behaviors into the AI tools they’re working with. 


Continuously work to eliminate personal biases

At the end of the day, programmers and AI professionals are individuals. They have their own minds and behaviors and are influenced by their experiences and perspectives. They also have their own biases. 

Nobody can make someone work on their personal biases. However, if we all committed to doing so, fewer people in AI or programming roles would have personal and gender-related biases to transfer to the machines and software they’re working with. These efforts would help diminish the feminization of AI over time. 



Analyzing and, in time, minimizing the feminization of AI is critical. Harmful gender stereotypes that define feminine qualities will continue to be reinforced if we don’t. 

It will take a deeper examination of gender, gender roles, and bias to get us started on the right path. After that, we must work to eliminate harmful stereotypes about men and implement more masculinity in AI 

And finally, we each need to address our personal biases, and women must fill more AI-based roles if we want to truly address the feminization of AI.

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