SAS: ‘AI will define the future of tech,’ but why is there already a gender gap present?

AI has the power to transform industries, shape economies, and revolutionize the way we work.

However, according to research from Interface, nearly there is a “stark” gender imbalance in 1.6 million AI professionals worldwide, with women contributing to only 22% of global AI talent. 

As we look higher up the chain, the representation of women decreases further, with women occupying less than 14% of senior executive roles in AI. 

The report states that the gender gap represents “a significant loss of potential talent in one of the most crucial industries of our time,” so what do we need to rectify this, before the gap widens even further?

In an exclusive conversation with Prathiba Krishna, Data Scientist and AI & Ethics Lead at SAS UK & Ireland, an analytics, AI, and data management software provider, UNLEASH discovers more.

What are the risks of inequity in AI?

Achieving gender equality in the AI sector isn’t just a matter of achieving equality, but rather ensuring AI systems are being developed to work equitably for all of society.

However, the risk of bias in AI models is a key concern within the industry. 

Krishna expands on this by explaining that when AI systems are trained on biased or incomplete datasets, they can produce “discriminatory outcomes”, particularly in sensitive areas like credit scoring or hiring decisions. This can sometimes negatively impact women. 

To mitigate this risk, she advises that institutions should adopt trustworthy machine learning techniques while conducting regular audits, and ensuring diverse representation in training datasets. 

“These efforts help minimize bias and promote equitable decision-making,” she inputs. “Human intervention is equally important in reducing bias in AI models and supporting adaptation to the AI-driven landscape. 

“Without robust oversight of data quality and the use of trustworthy AI, the risk of biased insights or inaccurate outcomes could undermine public trust and hinder women from being hired for certain roles.”

Undoubtedly, diversity is a critical factor in AI development, and Krishna highlights its ability to understand ethical and solution implications.

For example, products designed with diverse inputs are more likely to meet the needs of a varied customer base, offering a competitive edge in an increasingly globalized market. 

On the societal side, Krishna believes that diverse teams with different viewpoints can lead to more innovative solutions and creative approaches to solving complex problems in AI. 

In essence, the lack of diversity in AI teams can lead to a narrow view of the problems and potential solutions, ultimately resulting in technology that may inadvertently reinforce existing social biases,” she highlights. 

“Conversely, a diverse workforce not only improves the ethical grounding of AI but also drives innovation, making for more robust and socially responsible technology.”

What needs to be done to diversify AI?

As AI literacy is becoming increasingly mainstream, more individuals can equip themselves with the knowledge and tools to navigate the complexities of AI technologies. 

But for it to be put to good use, Krishna insists that a framework must be created that not only safeguards women but “promotes the ethical development of AI technologies globally.” This is both true for the current workforce, and the workforce of the future.

AI will define the future of tech,” she shares. “So, it’s important we have a future where technology serves humanity, not the other way around. 

“I would argue that much of this starts at college or university level with STEM skills that are taught to give students a good grounding and starting point for their future careers.  

“All AI-driven jobs will require a basic level of data or AI literacy so it is important for young people to acquire these skills, whether that is through online courses or training in the workplace at the start of their careers where it’s not embedded into their university courses.”

On the other hand, leaders already in the workplace can leverage the tool to improve practices and promote equity, such as by revising job descriptions and enhancing recruitment processes.  

What’s more, AI can also be used to reduce unconscious bias, by implementing bias-free screening through blind resume reviews and structured interviews, for example.

“Training hiring managers on recognizing and mitigating bias is also essential,” Krishna adds. “Workshops and online courses can create, develop, and offer continuous learning opportunities for these managers to promote certifications and qualifications in AI-related fields.” 

She continues to highlight that tailored upskilling programs can help bridge the gap for women already in the organization, by ensuring they’re supported in their development. 

What’s more, having visible role models and highlighting the success stories of women in AI-focused roles within the organization should also not be underestimated as Krishna believes that showcasing women and their work can inspire others to pursue similar career paths. 

Never the less, she continues to highlight that positive changes are being made, with more women being recruited in senior AI-related roles. She therefore urges women to feel confident when applying for roles in the AI sector – especially as the industry is set to grow significantly over the next few years.  

“As data scientists, it’s important you don’t just have the technical skills, but can also prove that you can work with large, complex data sets,” she says. 

“It’s also important for women who want to apply for these roles to show that they can be creative when it comes to solving business problems and demonstrate their communication skills to coherently explain how the data has been used and what the insights can show.”

Concluding, she encourages women to be “the agent of change,” by realizing their unique perspective can be a valuable asset, leading to innovations that help address biases in AI.

“Invest in your skills and leverage opportunities within inclusive organizations,” she says. “Celebrate small wins that can help you realize that you belong in this space. 

And finally, recognize your worth. Confidence grows with experience and every step you take in learning and contributing builds your expertise.”