Racial Justice x Technology Policy

Algorithmic Oppression: An Intersectional Anti-Racist Approach to Addressing Birth Inequities in Black Women

Black women are three times more likely to die from complications of pregnancy and two times more likely to experience severe maternal morbidity. These outcomes are driven by systemic, structural, interpersonal, and internalized racism, and sexism, exacerbated by the US socio-political context, inequitable social and health policies, and algorithmic oppression (AO). We ask: What is the impact of algorithmic bias/oppression/racism on birth inequities?  How can public and institutional policies mitigate algorithmic bias/oppression/racism?  An intersectional antiracism framework was utilized to examine and evaluate actions and provide recommendations related to ameliorating AO.

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The Birth Inequities Team

Laurie Nsiah-Jefferson

Laurie Nsiah-Jefferson '80, PhD'06, MPH, MA

UMass Boston - Director, Center for Women in Politics and Public Policy

Ezra Tefera, MD, MSc

The Heller School – RJxTP Program Director

Jallicia Jolly, Ph.D., MA

Amherst College - American and Afro-American studies, Assistant Professor

Violet Acumo, MPP

UMass Boston - Dept. of Public Policy and Public Affairs, Ph.D. Candidate

Makda TekleMichael, MA

UMass Boston - Dept. of Public Policy and Public Affairs, GLPP Masters Certificate Student

Jared Nwanagu

Palamar College, CA - Undergraduate Student