What is ChatGPT?
The gender gap in AI is a systemic issue where algorithms like ChatGPT mirror and amplify societal prejudices. While ChatGPT took the internet by storm in 2023, it has now matured into a global tool with over 200 million weekly active users. Classed as one of the most developed artificial intelligence systems in history, it can explain complex physics or draft heartfelt letters in seconds. However, its rapid adoption has accelerated a critical ethical dilemma. Because these models are trained on the internet, they frequently "hallucinate" gendered stereotypes, raising urgent questions about the digital future for women.
Concerns
The use of ChatGPT has raised concerns about its potential to replace jobs, facilitate cheating in academic settings and spread false or biased information. These ethical concerns stem from the fact that ChatGPT can mimic human-like conversations and blur the line between human and machine. The effectiveness of the chatbot thus poses a more complex philosophical dilemma, namely because AI ‘somehow gets closer to our skin than other technologies’ (Müller, Ethics of Artificial Intelligence and Robotics). It has been engineered to appeal to how humans see ourselves—as feeling, thinking, intelligent beings, and consequently poses a significant risk to certain job roles, as it can learn and adapt to individual needs in real-time. Not only does ChatGPT have the potential to render certain job roles obsolete, but it also has bias ingrained within the language model. Whilst it was trained on over 300 billion words sourced from the internet to improve neutrality and accuracy, it raises concerns about the accuracy of this information. This, therefore, begs the question: Is the internet itself an objective and unbiased source? The answer is a resounding no.
How does generative AI amplify gender stereotypes?
How does AI perpetuate gender bias? If we consider the internet a mirror of society, then ChatGPT is a digital lens that focuses those biases. While early studies focused on simple search results, a 2024 UNESCO study titled "Bias Against Women and Girls in Large Language Models" revealed more alarming trends in modern generative AI. The research found that LLMs (including GPT-3.5 and Llama 2) consistently portray women in domestic or subservient roles, frequently associating them with words like "home," "family," and "children." In contrast, male names were associated with terms such as "business," "executive," "salary," and "career."
This isn't just a quirk of the software; it’s a reflection of the "Digital Divide." When asked to write stories about different genders, these models assigned men high-status roles like "engineer" or "doctor" while relegating women to roles that were traditionally undervalued. As AI becomes the default interface for the web, these ingrained stereotypes risk becoming an "automated mansplaining machine" that reinforces a world that no longer exists.
In a study conducted by Fast Company, they asked ChatGPT to write basic performance feedback for various professions, including ‘write feedback for a helpful mechanic’, ‘a bubbly receptionist’, ‘a remarkably intelligent engineer’ and a ‘remarkably strong construction worker’. The article found that ChatGPT automatically made cliched remarks and presumed employee gender, even when the prompt given was highly generic.
With regard to the content of the employee evaluation, they also found that, in all cases, the feedback written for the female employee was longer and involved more criticism. This reflects the real-world biases held against women in the workplace, due to women’s differing communication styles, their perceived incompetence or ‘bossiness’, and also their lack of representation in leadership roles.
These changing skillsets will have adverse impacts on women as opposed to men, namely because of the ‘Digital Divide’. This refers to the increasing levels of digital illiteracy amongst women, and the consequent issue of their exclusion from the global workforce. Sadly, this vicious cycle of digital inequality stems from early gender stereotyping and segregation of genders in education systems, resulting in very few women and girls opting for further education in STEM subjects. These cultural and social norms thus correlate to the lack of representation in technology-related fields, particularly in roles associated with higher status. In fact, according to a study entitled, ‘Where are the women? Mapping the gender job gap in AI’, women in data science and AI have higher formal education levels than men across all industries. Although highly qualified across the board, the same report also found that women in the tech sector experience higher turnover and attrition rates compared to their male counterparts and are more likely to occupy jobs associated with lower pay and status (usually working within analytics, data preparation and exploration).
Criticism and Controversies Surrounding Chat GPT
The internet has been very quick to label ChatGPT as ‘sexist’ and ‘racist’ or conversely ‘too woke’. These were the words of Elon Musk, who objected to Chat GPT’s resistance to ‘say a racial slur in an absurd hypothetical situation, where doing so would save millions of people from a nuclear bomb’ (VICE). In further tweets, he stresses the need for ‘free speech’ and claims ‘What we need is TruthGPT’. Musk, in fact, initially invested in OpenAI, the creators of Chat GPT, in 2015, and resigned from the board in 2018.
Chat GPT has also been criticised for refusing to ‘make jokes about women’ or to write a 10-paragraph argument for ‘using more fossil fuels’. Critics have even pointed out that Chat GPT is just an automated ‘mansplaining machine’ as it can often get answers wrong as it generates ‘vaguely plausible sounding, yet totally fabricated and baseless lectures in an instant with unflagging confidence in its own correctness on any topic, without concern, regard or even awareness of the level of expertise of its audience’ (@andrewfeeney). However, unlike humans, algorithms are non-sentient and don’t have fragile self-esteem that requires them to diminish or elevate others, nor can they subconsciously favour certain groups over others or make moral judgements.
How can we fix algorithmic bias in 2025?
So how can this bias be rectified? For one, it’s important to acknowledge that the developers themselves share responsibility in manufacturing bias, and they must be responsible for ensuring that their models are designed inclusively. 78% of global professionals with AI skills are male, which demonstrates how the industry itself is instinctively biased to favour men. Gender segregation in the tech industry has been likened to a ‘brotopia’ in the eyes of Emily Chang, a Silicon Valley insider, who cites that these toxic, misogynistic workplaces exclude women from technology development and access to higher positions in the field. Frida Polli, CEO of Pymetrics, similarly questioned this lack of diversity in AI, asking:
“Can you imagine if all the toddlers in the world were raised by 20-year-old men? That’s what our A.I. looks like today. It’s being built by a very homogenous group.”
This lack of accountability and transparency on the part of developers could lead to a failure to acknowledge and address gender biases in their systems, painting a bleak picture for an industry that should be at the forefront of innovation and progress. Furthermore, to make AI more accurate, fair, and inclusive for all groups, it is essential to encourage more women and girls to pursue STEM and prioritize diversity in the design, development, and testing of these systems.
Training AI and Its Potential Impact
Training Artificial Intelligence is the logical step forward, in order to create a more comprehensive, unbiased and accurate model for public use. For example, LinkedIn has launched a new AI-led initiative that invites relevant member experts to contribute their expertise to AI generated prompts. By sharing their lessons, anecdotes and advice, these members will be directly supporting and training the AI engine to be more efficient. Although training AI will lead to a more accurate system, it is worth asking: who will be targeted in order to achieve this? Women and minority groups in particular, may be a focus for developers to counter bias within AI, which surely must be a good thing? Initially, the proposed change may appear to be a positive and essential step, however the increased involvement of trained knowledge workers poses a higher chance of job roles beings phased out and replaced by AI.
What is the "Triple Whammy" facing women in the AI workforce?
The Impact of AI on Women’s Jobs: The 2025 Perspective. As AI continues to evolve, women face what the World Economic Forum (WEF) 2025 Global Gender Gap Report calls a "triple whammy." First, while AI is creating new roles, the tech industry faces a severe talent shortage where women are still less likely to reach the C-suite or enter AI-engineering roles. Second, women occupy a higher proportion of administrative and service-oriented roles that are most susceptible to being replaced by automation.
Finally, and perhaps most critically, the report found that women’s jobs are less likely to be "augmented" (enhanced) by AI compared to those held by men. This means that even when women stay in the workforce, they may be denied the productivity-boosting tools that allow their male counterparts to excel. At the current rate of progress, the WEF estimates it will take 123 years to reach global gender parity, a timeline that AI could potentially extend if not checked by inclusive design. For women building long-term careers in tech, this makes understanding how systems, visibility, and evaluation are changing a core part of career progression, which is why these themes are addressed in the Fast-Track to Success in Tech programme.
Closing the Gap: How to Protect the Future of Women in Tech In the end, women are at risk of becoming collateral damage in the AI revolution unless we diversify the workforce at the development level. We cannot fix the "bias in the box" if the people building the box all look the same. To combat the digital divide, we must prioritize:
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AI Literacy: Ensuring women have equal access to GenAI training to "augment" their roles rather than be replaced by them.
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Inclusive Datasets: Demanding transparency from developers like OpenAI and Google on how they are rectifying training data.
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Leadership Representation: Moving beyond entry-level roles and ensuring women are at the forefront of AI policy and ethics.
If AI is going to transform technology on a global scale, it is our responsibility to ensure it represents the actual diversity of our world, not just the biases of our past.
This article was originally written in 2023 by Isabel F and has been transferred across from the Girl Geek Dinners site to Girl Geek Network and updated in December 2025.