AI and Racial and Ethnic Inequalities in Health and Care – Competition Brief

The Artificial Intelligence and Racial and Ethnic Inequalities in Health and Care call will support research to advance artificial intelligence (AI) and data-driven technologies in health in ways that better meet the needs of minority ethnic populations.

The Sub-Categories

There are two categories to this call outlined in detail below. Applicants are expected to respond to one of the two sub-categories.

1) Understanding and enabling opportunities to use AI to address racial health inequalities

This first category focuses on leveraging opportunities to use AI to improve the health outcomes of minority ethnic communities in the UK.

2) Optimising datasets, and improving AI development, testing, and deployment

This second category focuses on creating the conditions to facilitate the adoption of AI that serves the health needs of minority ethnic communities, including through mitigating the risks of perpetuating and entrenching racial health inequalities.

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Sub-Category 1: Understanding and enabling the opportunities to use AI to address inequalities

Innovation in AI may not benefit minority ethnic communities to the extent that it benefits the White majority population because of a lack of diversity at a strategic level and within research teams, as well as weak incentives to develop products for smaller markets. Despite significant investment in the field, there has been limited exploration of if and how AI might be utilized to address disparities and improve the health of minority ethnic communities. A core challenge here is how to better inform the research and development of AI solutions in order to enable innovation in AI that benefits the health needs of minority ethnic communities and/or can reduce disparities in health outcomes.

The focus of this category will be on encouraging approaches to innovation that are informed by the health needs of underserved communities and/or are bottom-up in nature, as well as better understanding the opportunities for AI to respond to the health needs of different minority ethnic groups. Potential projects might entail, but are not limited to, qualitative and quantitative evidence synthesis or exemplar development to examine if/how AI could be employed to address disparities, or how existing solutions, could be adapted to improve accuracy for minority ethnic patients. Projects could also entail trialing initiatives that incentivise bottom-up innovation in AI in health and care from the perspective of minority ethnic communities, including patient and public involvement (PPI) or ethnographic studies to further an understanding of the specific challenges these communities face and/or their interactions with data-driven technology, in order to shape health outcomes.

The following are some examples of possible outputs of value, but this is far from an exhaustive list. We will consider other outputs that meet the stated objectives of focus for this category.

  • Identification of health needs that are amenable to AI solutions, and innovations that could be adapted to work for minority ethnic communities.
  • Transferable learnings from exemplar tech development.
  • Engagement and involvement of minority communities and stakeholders in technology development.

Sub-Category 2: Optimising datasets, and improving AI development, testing and deployment

Issues of bias can influence the datasets integral to model development, resulting in AI solutions that do not work effectively or accurately for minority ethnic groups. Potential problems may relate to the under-representation of minority ethnic groups in training datasets, but could also pertain to issues of structural bias, such as the unjustified inclusion of factors that correlate with race. There is a clear need for interventions that prevent or address complex issues of bias in datasets.

In addition to addressing bias within datasets, it is necessary to target potential bias during the development, testing, and deployment stages of the AI pipeline. The performance of algorithms for minority ethnic groups must also be effectively evaluated and monitored to ensure that these algorithms continue to work as intended for these populations and/or that they do not perpetuate inequalities.

This element of the research call will seek to support approaches that can aid in improving the quality and availability of datasets and their appropriate application for minority ethnic groups, as well as focusing on research that can support improvements in the development, performance, testing, and monitoring of AI models across patient populations. Projects could entail, but are not limited to, work that helps to improve the quality and representativeness of datasets for ‘training’ AI; approaches to facilitate the discoverability of diverse datasets and their appropriate application; research to improve or inform the use of different types of data for building AI models, including interdisciplinary analyses on how to model the complex intersection between the different determinants of health; computational approaches and technical analyses to prevent racial bias in modeling; and research that can help to inform guidelines, evaluation, and practice to address bias and promote equity in the stages of AI development, testing and deployment.

The following are some examples of possible outputs and their value, but this is far from an exhaustive list. We will consider other outputs that meet the stated objectives of focus for this category.

  • Resources and improved understanding to enhance the quality and utilization of datasets for training AI models.
  • Novel understanding from research or techniques, e.g. computational approaches to avoid perpetuating inequalities by design and/or approaches to promote equity by design.
  • New findings to inform best practice, guidance, and evaluation frameworks for AI development, testing, and/or deployment.

Finally, while the focus on the research call is on optimizing AI for minority ethnic groups, the call does not preclude projects whose activities and findings have applicability across different population groups in addition to minority ethnic populations. We are interested in learning how the findings of the proposed research activities could be applicable to other underserved groups.

Ruben Harutyunyan

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