Q&A with Joie Acosta, Douglas Yeung, and Sana Zakaria
The RAND Center to Advance Racial Equity has launched the RAND Methods Volume on Racial Equity Policy. The goal of this series to identify the policy analysis considerations in creating equity-oriented systems and policies. RAND is advancing critical thinking to inform the field of systems analysis, policy analysis, and related fields. The authors of the first volume on technology equity, Joie Acosta, Douglas Yeung, and Sana Zakaria, share more.
What are your main concerns about digital and technology equity today?
Sana: Developments in the digital domain are accelerating faster than they can penetrate the public consciousness. While the digital technologies and their applications are accelerating, there are concerns for the segments of the population who have not been brought along on the educational journey on the benefits as well as the pitfalls of these developments, and what it could practically mean for them. For example, commercial interests that develop AI tools may not adequately account for equity harms, nor might they decide to serve marginalized populations if they are not seen as financially beneficial targets. To truly leverage these developments and to ensure equitable use and benefit, there needs to be a concerted effort to educate, inform, and conduct deliberative public engagement to inform the development and applications of these technologies across various demographics.
What do you think are the greatest promises of AI and other technology for health promotion specifically?
Joie: Overall, the greatest promise of AI and other technology is its ability to engage marginalized communities in the key health promotion policy issues that influence them—often significantly and unfairly. Moving past symbolic engagement to authentic engagement through digital participatory innovations will help prevent racial bias and underrepresentation and begin to address inequities at the intersection of digital and racial health equity.
Sana: One example of this promise is related to the bottlenecks in the health systems where people find it hard to access evidence-backed sources of prevention and tailored health promotion strategies based on diversity and demographics of a given population. When equitably applied to genomic medicine, AI could make it easier for people to ask about and receive relevant, accurate, personalized health information and access “health assistants” for tailored support related to mental health or physical health.
What should researchers consider in our work on technology equity, in terms of what we measure or how we design studies?
Joie: Researchers play a vital role in promoting racial and digital health equity. More research is needed to validate the importance and impact of carefully monitoring and improving equitable implementation processes. Conducting research that more authentically engages the affected populations could also help expand the diversity of authors, populations, and settings involved in technology equity research. Finally, researchers can help encourage more transparency of and trust in the AI and tech regulatory processes by developing and tracking equity measures. Transparency of these types of equity metrics can help provide checks and balances needed and incentivize the tech companies to approach their innovations in ways that advance public interest and build public trust. Greater trust is needed to promote uptake of digital health technologies in populations that have not benefited from these innovations and are critically important if we are to address the growing health inequities in the U.S.
What should policymakers account for in the growth of AI and other technology application to ensure equity considerations are balanced with benefits?
Doug: Policy makers should incorporate funding requirements that include equity measures to be monitored and incentivize AI developers to incorporate equity considerations into tool development and deployment. Additional investments are also needed in technology implementation infrastructure that would maximize or help the industry ‘level up’ their equity focus; policy makers could support these investments both during funding considerations and through incorporating accountability mechanisms as standards and regulations are updated and enforced. Policy makers need to work with AI developers to ensure that accountability and liability associated with use of technologies are clear and that mechanisms exists to minimize harm.
