February 6th, 2024
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The Rebel Tech Newsletter is our safe place to critique data and tech algorithms, processes, and systems. We highlight a recent data article in the news and share resources to help you dig deeper in understand how our digital world operates. DataedX Group helps data educators, scholars and practitioners learn how to make responsible data connections. We help you source remedies and interventions based on the needs of your team or organization.

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IN DATA NEWS

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“Wisconsin’s Dropout Early Warning System (DEWS) is an algorithm to predict a student's risk of either dropping out or graduating from high school. The algorithm relied on factors like race and income to determine if students had a “high risk” of dropping out. Nonetheless, it grossly labeled Black Students as “high risk.”

Reading Mia’s and Maurice’s experiences with DEWS is yet another example of when algorithms hurt people. Specifically, these algorithms are hurting Black people. Black people who could be my relatives. But in the mainstream media, (white) children being influenced or hurt by social media sparks US congressional legislation, US Senate and US House hearings. Black kids have been regularly exposed to hurt through algorithms way before they engaged with social media. Anti-Blackness is global with white colonialism as the dissemination mechanism.

For Black kids, Latin kids, Indigenous kids, Asian kids, South Asian kids, and LGBTQ+ kids, the silence regarding their high school graduation rates is deafening. The lack of care is noticeable. The lack of outrage is palpable. Dropout early warning systems aren’t a new innovation. They’ve been around since at least the 2010s. According to a 2016 US Department of Education brief, ethnicity and race aren’t mentioned but known proxies for race such as homelessness, involvement with social services or foster care, and Involvement with the criminal justice system are flagged as dropout indicators, as shown in the figure below. Society’s insistence that Black people are presumed problematic rather than trusted as meritorious drives these “high risk” labels.

This hurt through algorithms may seem really familiar as if you’ve heard about a similar system. Yes, you have. It’s called COMPAS — Correctional Offender Management Profiling for Alternative Sanctions. The COMPAS recidivism tool used an algorithm to assess the risk of a person becoming a repeat offender. ProPublica’s deep investigation revealed that Black people were at least twice as likely than white people to have a higher recidivism risk score when their crimes were similar. Navigating disparities throughout institutional systems and within our digital infrastructure starts before birth. The ‘bias’ has breadth, is intentional, remains a constant affront, and is systemic.

As written in Data Conscience, “[b]ias isn’t what we think it is. Well, it’s the right word if we want to mask clear messaging of what’s happening within digital systems. The tech community has been pushed to recognize, consider, and address uneven outcomes of its products to certain protected groups.”

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"AI tools that eat data often hit bumps because they fail to take people into account.” pg 231 Data Conscience

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​HAPPENINGS & APPEARANCES

• For those who missed Kanika Tolver’s Twitter Spaces on January 31st, check out the recording of our discussion about non-corporate data careers.
• Avela Gronemeyer and I had a thought-provoking discussion on debiasing tech with an international group of data leaders and responsible AI advocates. We talked about the state of data/AI governance, accountability approaches and practical recommendations. This webinar is part of Women in Tech - South Africa Chapter’s series. Looking forward to having more conversations with Avela!
• This week, I’m officially joining UPenn’s Centering Racial Equity Throughout Data Integration workgroup to revise their toolkit first released in 2019.

LAUGHING IS GOOD FOR THE SOUL

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Stay Rebel Techie,

Dr. Brandeis

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