Ruha Benjamin on deep learning: Computational depth without sociological depth is ‘superficial learning’ | AI - VentureBeat

Princeton University associate professor of African American Studies and Just Data Lab director Dr. Ruha Benjamin said engineers creating AI models should consider more than data sets when deploying systems, observes Khari Johnson, Senior AI Staff Writer at VentureBeat.   

Ruja Benjamin urged engineers to consider historic and sociological factors when building deep learning systems in her keynote address at ICLR.

She further asserted that “computational depth without historic or sociological depth is superficial learning.”

“An ahistoric and asocial approach to deep learning can capture and contain, can harm people. A historically and sociologically grounded approach can open up possibilities. It can create new settings. It can encode new values and build on critical intellectual traditions that have continually developed insights and strategies grounded in justice. My hope is we all find ways to build on that tradition,” she said.

In a talk that examined the tools needed to build just and humane AI systems, she warns that without such guiding principles, people in the machine learning community can become like IBM workers who participated in the Holocaust during World War II — technologists involved in automated human destruction hidden within bureaucratic technical operations...

Benjamin explored themes from her book Race After Technology, which urges people to consider imagining a tool for counteracting power imbalances and examines issues like algorithmic colonialism and anti-blackness embedded in AI systems, as well as the overall role of power in AI. Benjamin also returned to her assertion that imagination is a powerful resource for people who feel disempowered by the status quo and for AI makers whose systems will either empower or oppress.
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Source: VentureBeat