AI is increasingly being used to shape the decisions that we make, both for businesses and governments.
As AI agents begin to automate even more of our everyday lives, attention is now firmly on the models underpinning these platforms. Are they representative? What is being done to mitigate bias? How do we ensure they are making decisions with objective benefits, rather than skewed towards the views of the engineers who built them?
When it comes to gender diversity in AI, there is already cause for concern when it comes to model bias. Research shows that women only comprise of 22% of AI talent, representing a clear imbalance: the effect of this underrepresentation trickles into algorithms and systems, which often inherit biases due to the limited perspectives shaping them.
So how exactly can better representation of women in the AI sector lead, ultimately, to better AI models?
Mitigating Bias
AI models are only as good as the data they are trained on. If models are trained on data that is only representative of limited demographics, that can cause in-built biases in the output. Put another way: fewer women contributing to AI models and model data will lead to gender bias in the output of those models.
Mitigating that bias is not just a good thing for society (although, given how AI is now being used to make decisions across the board, this is an important consideration!). Removing bias from models also ensures the quality of the output is higher: that’s something every tech professional should be striving for.
Promoting creativity and innovation
So much of the success of AI stems from creative thinking. As we know in the tech world, allowing a broad range of views and thinking outside of the box is the best way to get ahead and drive innovation.
Creating gender diverse teams is critical, then, to push AI forward. The female perspective within AI teams can bring new innovations traditionally overlooked in the tech world, such as applications in women’s health. Fresh perspectives and lived experiences are crucial not just for AI datasets, but for broadening thoughts around how we use AI creatively and innovatively.
Trust and legitimacy
Diversity and representation in development teams is also important to ensure trust and legitimacy regarding the building of AI models. It’s one thing to build something and it’s another thing for people to accept it into their practice. Having a diverse and representative team who can prove that they have interacted, contributed and also tested AI models through a variety of lenses help to build trust across broader populations. For AI models to be used successfully, the users need to be able to trust it first.
It’s an old adage by now, but AI really is only as good as the data and team that build it. Ensuring that women are well represented in the AI sector isn’t just a morally right thing to do, it will actively improve the quality and uptake of AI models. For businesses looking to gain a competitive advantage, increasing female representation in this field should be a priority.
