Promoting Equality, Diversity, and Inclusion in AI and ML

It is important to ensure that equality, diversity, and inclusion (EDI) are prioritized when creating AI and ML solutions. In this blog post, we will explore how this can be achieved.

EDI is important in the tech sector for two key reasons:

1. Firstly, it is essential for creating fair and inclusive AI and ML solutions. Without a focus on EDI concerns, there is a risk of creating solutions that are biased, which can have negative impacts on individuals and society as a whole, and also on the organisations who are utilising the solution in their decision-making processes. We have all heard about the companies and governmental departments being sued for basing their decisions on discriminatory AI/ML recommendations.
2. Secondly, EDI is essential for creating a diverse and inclusive tech workforce. This reason is less about risk management and more about driving innovation. An organisation with strong EDI attributes is more likely to surface new ideas and perspectives, helping to bring better AI/ML products to market.

1. Diversify Your Team. One of the most effective ways to promote EDI is to diversify your team. This includes ensuring that your team is diverse in terms of backgrounds, gender, race, ethnicity, sexual orientation, and other identities. It’s not just about recruitment. You need to maintain balance within your organisation by monitoring retention demographics, and ensuring that talent is being developed and promoted fairly, so that EDI is reflected all the way through your org-chart.
2. Address Bias in Datasets. It’s a bit of a truism, but AI and ML solutions are only as good as the data they are trained on. Many of the more recent advances in LLMs and Generative AI from e.g. OpenAI have had to resort to lots of human feedback to weed out unsavoury or biassed outputs. But things still slip through, and this is a reputational threat. It’s best to prioritise datasets which are diverse and representative of the population.
3. Use Inclusive Design. Inclusive design involves designing AI and ML solutions that are accessible and usable for everyone, regardless of their abilities or backgrounds. Inclusive design can include features such as alternative text for images, closed captioning for videos, and voice recognition for individuals with disabilities and the ability to change the backgrounds to a coloured to darker page type to make reading text easier for dyslexics.
4. Prioritise Transparency. Transparency is key when it comes to promoting EDI in AI and ML. It is important to be transparent about how AI and ML solutions are designed, how they work, and what data has been used to train them. This can even include making the source code and algorithms open-source, to provide the clearest insight into how decisions are made by the system. At digiLab, we place a lot of focus on uncertainty quantification (UQ). UQ is the process of quantifying and analysing the uncertainty in mathematical models, simulations and data. The primary aim is to assess the reliability of predictions, account for the effects of variability, randomness and misspecification in models, and ultimately assist in decision-making.

In conclusion, promoting EDI in the tech sector is essential for creating fair and inclusive AI and ML solutions. It can be achieved by diversifying your team, addressing bias in data, using inclusive design, testing for bias, prioritising transparency, and fostering a culture of inclusion. As the tech sector continues to grow and innovate, it is important to prioritise EDI to ensure that everyone has equal access to the benefits of AI and ML.

If you want to become a practicioner in AI/ML, perhaps through the digiLab Academy, then we recommend being aware of EDI whenever you're working with data - or people!