What the Father of Black History Can Teach Us About Technology

2026-02-27 · Source: Tech Policy Press · Field: Technology & Digital — Artificial Intelligence & Machine Learning, AI Ethics & Policy · Depth: Novice, short

Summary

Danielle A. Davis Canty, director of technology policy at the Joint Center for Political and Economic Studies, argues that the "miseducation" framework developed by Carter G. Woodson in 1926 for racial inequality in education applies directly to contemporary technology. Woodson, who founded Negro History Week, detailed how educational structures excluded Black contributions and normalized racial hierarchy. Canty posits that modern discourse similarly presents digital systems as neutral and algorithms as objective, framing harms as technical bugs rather than outcomes of design choices, governance, and accountability. This approach, she contends, habituates people to accept technological inequalities as accidental or inevitable, rather than as predictable results of systems designed without affected communities, leading to concrete consequences like disproportionate surveillance and biased automated decision-making in Black communities.

Key takeaway

For technology leaders and policymakers evaluating AI ethics and equitable deployment, recognize that claims of system neutrality often mask inherent biases. Your teams should actively interrogate design choices, governance structures, and accountability mechanisms, rather than accepting unequal outcomes as mere technical glitches. Prioritize inclusive design and community input to prevent predictable technological inequalities.

Key insights

Technological "miseducation" normalizes inequality by presenting digital systems as neutral and harms as accidental.

Principles

• Exclusion in design leads to predictable inequality.
• Systemic bias is often framed as technical error.

In practice

• Question claims of technological neutrality.
• Examine whose experiences are centered in system design.

Topics

• Technology Policy
• Algorithmic Bias
• Digital Inequality
• Facial Recognition
• Automated Decision-Making

Best for: CTO, VP of Engineering/Data, Director of AI/ML, Policy Maker, AI Ethicist, Tech Journalist

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Editorial summary, takeaway, and curation by AIssential. Original article published by Tech Policy Press.