Inequalities

Towards an intersectional understanding of inequalities

Our cross-cutting intersectional inequalities theme acknowledges that structural, contextual and individual factors such as ethnicity, gender, differing abilities and socioeconomics, are interactive and multiplicative in determining health outcomes.

AI, in its development and application, also has the potential to unintentionally perpetuate existing inequalities in society and health.

Whether due to non-representative datasets, biased algorithms, or unequal access to technology, it can mean that those most in need are excluded from essential services. This was most recently evident in the response to the Covid-19 pandemic. There is a clear imperative to mitigate against
this.

To fully understand and positively influence the trajectories of MLTC-M-PP we will apply an intersectional lens of analysis and scrutiny to all work packages.

We will interrogate and stratify the diverse datasets to characterise the patterning of MTLC-M-PP across these factors.

These insights will inform the development of AI algorithms with the broadest inclusivity and reach which aim to
narrow health inequalities in access and outcomes.