Antiracist Technology in the US
How can communities of color use technology to advance racial equity and access economic opportunity, health, and safety?
Black, Indigenous, and People of Color (BIPOC) in the US have created resilient, culturally rich, and generous communities despite centuries of institutionalized racism, anti-Blackness, settler colonialism, and oppression. The Covid-19 pandemic has further exacerbated the disparities between BIPOC and white communities in the US, including in wealth, education, incarceration, and health. These disparities are primarily a result of the system working as initially intended, whether through current policies, biased enforcement of rules, or a lasting legacy of past programs such as redlining or the allotment of Native lands. Further, a new wave of technologies has added or perpetuated racial bias, expanded predatory surveillance systems, and driven hidden decision-making under the guise of neutral algorithms.
Solutions that help end enduring injustice will take many forms. In addition to policy changes and dedicated resources driven by intersectional advocacy, technology and innovation also have roles to play. Movements like afrofuturism, community-led efforts on digital literacy, indigenous data sovereignty, and the use of data science for positive change all speak to the potential of technology to support, inspire, and liberate BIPOC communities.
The MIT Solve community is searching for technology-based solutions by and for communities of color that help create antiracist and equitable futures in the US. To that end, Solve seeks solutions that:
Provide tools and opportunities for equitable access to jobs, credit, and generational wealth creation in communities of color.
Catalyze civic engagement and enable communities to plan and control their own housing and industrial land development and ownership patterns.
Create new public safety systems that ensure racial equity and provide alternatives to harmful technologies such as biased facial recognition.
Actively minimize human and algorithmic biases, particularly in healthcare, education, and workplace settings.