---
title: "Women in AI/ML: Why Diversity is Critical for the Future of Technology"
description: Explore why gender diversity is crucial in AI/ML. Learn how underrepresentation of women impacts technological development and why diverse teams lead to smarter, more inclusive innovations.
image: https://blog.qwasar.io/hubfs/Women%20in%20AIML%20Why%20Diversity%20is%20Critical%20for%20the%20Future%20of%20Technology.png
---

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# [Women in AI/ML: Why Diversity is Critical for the Future of Technology](https://blog.qwasar.io/blog/women-in-ai/ml-why-diversity-is-critical-for-the-future-of-technology)

 Oct 17, 2024 10:25:37 AM / by [Caitlin Carlton](https://blog.qwasar.io/blog/author/caitlin-carlton)

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The gender gap in technology, particularly in fields like Artificial Intelligence (AI) and Machine Learning (ML), remains a persistent issue. Despite the rapid growth of these industries, women, and many other people groups, are still significantly underrepresented. This lack of diversity has far-reaching consequences, influencing the development of the technologies that are shaping our future.

Many professionals, including our co-founder, [Jennifer](https://www.linkedin.com/in/jennifer-robertson-2653a638/), have voiced increasing frustration with the lack of female machine learning engineers in the workforce, and the lack of female presenters and thought leaders at AI/ML conferences. In spaces where innovation is key, the absence of diverse perspectives is both a seriously large missed opportunity and a reflection of the ongoing challenges women face in tech.

Now is the time for change. With AI/ML poised to transform industries, it’s crucial for women to be part of this conversation.

### **1. The Current State of Gender Diversity in AI/ML**

The gender gap in AI/ML is still a big issue. Even though the field is booming, women are vastly underrepresented. A 2022 report from the World Economic Forum found that[just 22% of AI professionals globally are women](https://www.weforum.org/agenda/2018/12/artificial-intelligence-ai-gender-gap-workplace/), which is pretty staggering considering how crucial these technologies are becoming​. Worse, according to [Zippia](https://www.zippia.com/artificial-intelligence-specialist-jobs/demographics/), OVER 90% OF AI SPECIALISTS ARE MEN.

[Research from Deloitte](https://www2.deloitte.com/us/en/pages/consulting/articles/state-of-women-in-ai-today.html) adds to this, showing that only 26% of "data and AI roles" are held by women, and that includes roles such as data analyst or data entry - leaving a lot of questions on how many women are actually in machine learning engineer roles.

This issue is also really obvious at major AI/ML conferences, like NeurIPS or ICML, where female speakers are still rare. It’s frustrating for many in the industry, especially for those who want to see more diverse perspectives shaping the future of these technologies.

### **![Women in AI/ML: Why Diversity is Critical for the Future of Technology](https://blog.qwasar.io/hs-fs/hubfs/MIT-Gender-Shades-01.jpg?width=600&height=400&name=MIT-Gender-Shades-01.jpg)(**[MIT](https://news.mit.edu/2018/study-finds-gender-skin-type-bias-artificial-intelligence-systems-0212)**)**

### **2. Why AI/ML Needs Diversity Overall, Including Women**

AI/ML is shaping the world around us, but without diverse teams, these systems risk being biased or flawed. Bringing in different perspectives, especially from women, is crucial because they can spot issues others might miss. For instance, a lot of AI systems have been found to be biased in areas like facial recognition and hiring algorithms. [Women in AI/ML help challenge those blind spots](https://www.forbes.com/sites/mariaklawe/2020/07/16/why-diversity-in-ai-is-so-important/), leading to more fair and balanced technologies.

Diverse teams naturally come up with more creative solutions. When people from different backgrounds—whether it’s gender, race, or experiences—are in the room, they can catch biases early on and create AI that benefits everyone. This isn’t just good for society; it’s good for business too. Companies with diverse teams tend to be more innovative and perform better. So, in the end, it’s not just about representation—it’s about making AI smarter and more inclusive for everyone.

### **3. The Impact of Monolithic Decision-Making in AI/ML**

When the same group of people—with similar backgrounds, experiences, and perspectives—are the ones making key decisions in AI/ML, we run into some serious problems. Monolithic decision-making means algorithms are being designed without considering the diversity of the world they’re supposed to serve. The result? Biased algorithms that can have damaging real-world consequences.

Take facial recognition software as an example. Multiple studies, like the one from [MIT's Media Lab](https://news.mit.edu/2018/study-finds-gender-skin-type-bias-artificial-intelligence-systems-0212), have shown that these systems are significantly less accurate for women and people of color. In some cases, they’re nearly 35% more likely to misidentify women of darker skin tones IV, V, or VI (on the Fitzpatrick scale) compared to men with lighter skin tones​. The facial-analysis systems had been trained on data that underrepresented women and darker-skinned individuals, leading to skewed and unreliable results for these groups. This kind of bias has serious consequences, particularly when it’s used in law enforcement or public surveillance, leading to wrongful arrests or discriminatory practices. It is unacceptable!

![img_1 6](https://blog.qwasar.io/hs-fs/hubfs/img_1%206.jpeg?width=500&height=500&name=img_1%206.jpeg)

The same goes for hiring algorithms. [Amazon famously had to scrap its AI recruiting tool](https://www.reuters.com/article/world/insight-amazon-scraps-secret-ai-recruiting-tool-that-showed-bias-against-women-idUSKCN1MK0AG/#:~:text=In%20effect%2C%20Amazon's%20system%20taught,the%20names%20of%20the%20schools.) after discovering that it favored male applicants over female ones. The AI had been trained on resumes submitted over a 10-year period, which largely came from men, and as a result, it penalized resumes with words like "women's" or "female". These examples highlight how a lack of diversity in the teams designing these systems can reinforce existing biases and perpetuate inequality.

How did it discover so late in the process that the product had an enormous flaw? How many female machine learning engineers worked on that product? Clearly not enough.

These are but two examples of many that exist, and countless that we don’t know about, and many more that are currently being built.

This is why having gender-diverse teams matters. Women and other underrepresented groups are more likely to bring different experiences and perspectives, making it easier to spot and correct biases early in the development process. Studies show that diverse teams are better at identifying blind spots and building more inclusive, fairer technology. When AI is built by people from all walks of life, the technology becomes more reflective of the society it’s meant to serve.

![Women in AI/ML: Why Diversity is Critical for the Future of Technology](https://blog.qwasar.io/hs-fs/hubfs/women-in-ai-khlaaf.webp?width=600&height=338&name=women-in-ai-khlaaf.webp)

([Heidy Khaleef, TechCrunch](https://techcrunch.com/2024/03/10/women-in-ai-heidy-khlaaf-safety-engineering-director-at-trail-of-bits/))

### **4. We Need to Get Over the Idea That AI/ML Is Hard or Intimidating**

With the AI boom and “AI” coming into mainstream, a lot of people don’t understand it or the technology behind it, find it intimidating, and think it’s hard and requires a very heavy mathematics background. As a result, many people, many women included, count themselves out of becoming an AI/ML Engineer before they even start or try.

This needs to change. You do not need a degree in mathematics or advanced calculus to become a machine learning engineer! To this end, universities that demand such are wrong. If you want to pursue a PhD, then sure, you will need and do a lot of mathematics, but most machine learning engineer roles don’t require a PhD.

### **5. You Should Consider an AI/ML Engineer Position if You Like Data**

Do you work with data every day? Whether you're a database analyst, Salesforce admin, HubSpot marketer, or managing a school district's information systems, your skills are more relevant to AI/ML than you might think. If you love working with data, don’t count yourself out from exploring machine learning (ML) and artificial intelligence (AI).

At its core, machine learning is about finding patterns in data, making predictions, and helping systems “learn” from that data. If analyzing data, organizing information, or drawing insights from numbers gets you excited, then transitioning into AI/ML could be a great next step. Plus, the field is in dire need of diverse professionals—especially women. The more variety in backgrounds and experiences AI/ML professionals have, the more inclusive and effective the technology becomes.

If data is your thing, consider making the leap to AI/ML. It’s a fast-growing field, and your skills might just be what the future of tech needs!

![Women in AI/ML: Why Diversity is Critical for the Future of Technology](https://blog.qwasar.io/hs-fs/hubfs/pexels-fauxels-3184434.jpg?width=600&height=400&name=pexels-fauxels-3184434.jpg)

### **6. Pushing for Change—Representation Matters**

The lack of female speakers at AI/ML conferences is a glaring issue that deserves more attention. When you look at the panels and keynotes at these events, it’s often the same faces representing the industry. This not only limits the range of ideas shared but also sends a message that women’s voices and expertise are less valued. Our co-founder, [Jennifer](https://www.linkedin.com/in/jennifer-robertson-2653a638/), has expressed frustration over this trend, noting how essential it is for young women to see leaders who look like them in tech. [Representation matters](https://techcrunch.com/2024/05/29/the-women-in-ai-making-a-difference/); it inspires the next generation and helps create an environment where diverse ideas can thrive.

Having women in leadership roles within AI/ML not only encourages aspiring professionals but also leads to better decision-making. Organizations and conference organizers need to take an active stance to feature more diverse voices, from women to other underrepresented groups. This isn’t just a feel-good initiative; it’s essential for the growth and integrity of the AI/ML field.

Let’s push for change in these spaces and ensure that every voice has the opportunity to be heard. [Our AI/ML program](https://qwasar.io/tech-talent-programs/ai-machine-learning-engineer-program/) aims to support and empower women in tech, fostering a more inclusive environment​.

![Caitlin Carlton](https://blog.qwasar.io/hs-fs/hubfs/caitlin_image.jpeg?width=100&height=100&name=caitlin_image.jpeg)

#### Written by [Caitlin Carlton](https://blog.qwasar.io/blog/author/caitlin-carlton)

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