The artificial intelligence market is growing at an astonishing rate. And headlines dedicated to ChatGPT and its rivals demonstrate the palpable hype around this technology. 

For all the crystal ball-gazing and pontificating about what AI will mean for society and business, there are more immediate concerns that are too readily overlooked. Namely, that there is a striking lack of diversity in the world of data and AI – this leads to the very real risk of “the single story”, as novelist Chimamanda Adichie describes it.

What do we mean by this? Essentially that if we hear only a single story about another person, group, culture or country, we risk critical misunderstandings. Applied to data and AI, this means that if these technologies are advanced by a community lacking in diversity, the tech itself can fall foul of in-built misunderstandings, bias and, at worse, racism.

Indeed, a lack of representation leads to a lack of diverse perspectives in the development of technology, which can perpetuate biases and inequalities in the systems and products created. Yet, according to techUK, less than a tenth (8.5%) of senior leaders in UK tech are from ethnic minority groups, and just a sixth (16%) of IT professionals are female.

The situation is even more concerning for women of colour based on available data.

A recent report entitled The Experiences of Black Women in the IT Industry revealed that just 0.7% of black women in the UK work in the IT industry, compared to 1.8% across the UK’s entire workforce. The study also revealed that women of all backgrounds and ethnicities make up around 22% of IT professionals (approximately 424,000), compared to 48% when examining the entire UK workforce. In other words: women, particularly black women, are far less likely to work in tech than other sectors.

Bias in data and AI without representation

The adoption of AI and data-intensive decision-making is becoming more and more common. But set against the backdrop of so few women of colour working in UK tech, the emphasis on data and AI ought to ring alarm bells. 

Racial bias in data and AI can arise when algorithms are trained on biased datasets that do not accurately represent the diversity of society. If the data used to train algorithms is biased, the outputs of those algorithms can perpetuate and even amplify the biases.

When black women are not involved in the development and deployment of technology, their perspectives and experiences are often not considered. This can lead to the development of products and services that do not work as well for black women or can even actively discriminate against them, such as failures of AI to properly identify the gender of darker-skinned women compared to lighter-skinned women. 

Further, the Information Commissioner’s Office said AI-driven discrimination could have “damaging consequences for people’s lives” and lead to someone being rejected for a job or being wrongfully denied a bank loan or a welfare benefit.

To address this issue, it is important to promote the use of diverse and representative datasets in the development of AI and machine learning models to mitigate the risk of perpetuating biases. Likewise, it is crucial to increase representation of black women and other underrepresented groups in the tech industry, particularly in leadership and technical roles.

Getting more black women working in tech, data and AI

Although we face significant challenges, it is noteworthy that numerous exceptional black women are making significant contributions to the data industry and paving the way for future generations. In addition, there has been a surge in black-owned businesses that are tirelessly advocating for women’s advancement and preparing them for equitable corporate opportunities.

For instance, the Niyo Group, which upskills black women for tech and data roles, is dedicated to this mission, providing training opportunities for black women seeking tech and data roles. This inclusion of including black women in the advancement of data and AI technologies works to prevent bias from affecting data sets and algorithms. Indeed, black women in data means having black female representation in data.

British author and campaigner Caroline Criado Perez said: “It’s not always easy to convince someone a need exists, if they don’t have that need themselves.” When black women work in data and build AI-driven products, they are ensuring their existence is reflected in the data and that the products are designed with a diverse society in mind.

The question, then: how do begin putting this into effect? 

Amplifying female voices & why tech needs gender equality

Digital skills bootcamps

Digital skills bootcamps are an excellent example of programs available across the UK that equip learners with essential digital skills for roles in fields such as coding, cybersecurity, and digital marketing. These bootcamps are offered free of charge to participants.

Niyo runs skills bootcamps in collaboration with the West Midlands Combined Authority. They support the unemployed, those seeking a career change, as well as employed people looking to gain the digital skills required to secure more responsibility or a promotion with their current employer.

Niyo’s bootcamps specifically cater to black women seeking a pathway into a tech career. The program offers online and flexible learning opportunities within a growing community of black women in tech.

Bias in AI and data is real danger to the advantages these technologies can bring to the world. As such, diversifying the data used to build algorithms by promoting diversity in building technologies is urgently needed. 

Therefore, employers and those seeking new skills should be encouraged to look for digital skills bootcamps in their area. The training provided facilitates essential digital skills while creating career pathways regardless of background.

Shy bairns get nowt (and other leadership lessons from my mum)